Novel CMI pipelines go beyond correlative microscopy and PHI setups and usually include more than two complementary modalities. They aim at (1) bridging (preclinical) in-vivo imaging with ex-vivo biological microscopy to zoom in from a living sample to individual cellular structures and/or (2) adding localized spectroscopic [biophysical (e.g., mechanical properties or vibrational modes) or chemical (e.g., molecular or elemental)] information to the acquired structural and functional parameters. With all the electromagnetic spectrum explored for imaging, only incremental improvements in contrast, resolution, or sensitivity are expected for the available spectrum of imaging technologies (see e.g. **Tables 1**–**3**). To explore the multiple spatial and temporal scales necessary for a holistic understanding of organisms and their biology, novel CMI pipelines will be the method of choice. However, novel CMI pipelines with more than two modalities are in their infancy due to lack of access to a single researcher, the broad expertise required to oversee several modalities and due to lacking workflows and software solutions to track ROIs across modalities from living 3D tissue down to high lateral molecular resolution. While there are several EUfunded initiatives that aim at improving accessibility of advanced imaging technologies and interdisciplinary imaging expertise (such as Euro-BioImaging or COMULIS), such novel CMI pipelines nevertheless require substantial method development. Due to the diverse plethora of potential combinations of imaging technologies, setting up universal correlation protocols for CMI pipelines is not feasible. Sample preparation procedures for exvivo microscopy differ substantially across the technologies and even within a modality; AFM images alone, for example, can be acquired under various conditions (vacuum, atmosphere, and liquid). Correlative imaging usually requires modality-specific preparation and setup trade-offs, such as between preservation of fluorescence and subcellular architecture for CLEM, between the AFM laser and excitation spectra of the used fluorophores to avoid bleaching for correlative AFM [78], or between preservation of fluorescence and X-ray contrast for correlative

CT. Dependent on the used technology, the sample preparation needs to be adapted.

In respect to correlation strategies, universal protocols to assess correlation accuracy are not implemented and restrict finding the same ROI after relocation between imaging platforms or co-alignment of data sets. Strategies to improve correlation of different technologies include (1) resolution matching of the technologies and (2) correlative markers that can be visualized in different imaging technologies.

A common approach to improving correlation accuracy in correlative microscopy is to match the FM resolution to that of the microscopy technique with the highest resolution (EM, SXT, AFM) by integrating super-resolution FM. For CMI pipelines that bridge in-vivo with ex-vivo imaging, usually intermediate (mesoscopic) resolution steps need to be implemented—as for example mesoscopic ex-vivo MRI for the integration of macroscopic in-vivo MRI data and microscopic CT data [79]. A common example includes the emerging use of CT in a different context: As an intermediate imaging technology to create a 3D template of the sample after in-vivo imaging and before sectioning of the sample to probe the ROI. CT can visualize thick tissues in 3D at micrometer resolution, tracks distortions and morphological changes of the ROI after embedding and fixation, and allows ROI identification even without (preserving) fluorescence. CT is specifically suited as an intermediate technology between in-vivo optical microscopy and EM since it can also visualize the sample in resin blocks due to the heavy-metal stains used for EM sample preparation. It qualifies for other correlative microscopy approaches as well since it can reveal endogenous landmarks, such as the vasculature, after barium sulfate perfusion.

While there are a variety of fiducial markers that can be used and tracked in correlative microscopy (such as QDs or dyelabeled nanoparticles), there are currently no correlative markers that can be visualized with high accuracy both by microscopy and preclinical imaging technologies. Besides, robust fiducial markers that might withstand electron bombardment or high X-ray doses are also lacking. A common approach to facilitate correlation when using CT as an intermediate modality is near-infrared branding (NIRB). Prior to CT, a pulsed, near-infrared laser is used to create defined 3D marks in the fixed tissue that can be traced by both FM and EM, and hence facilitates dissecting the sample to assess the ROI in a biopsy. In Karreman et al. [80], the position of the ROI was predicted with an accuracy of below 5 µm.

A typical correlation workflow for a CMI pipeline including for example, in-vivo optical microscopy, CT and EM typically might include the following steps, and will need to be adapted for the specific biomedical research question: (1) in-vivo functional imaging of molecular dynamics using FM, such as spinning disk, light sheet or multi-photon microscopy, or in-vivo imaging of metabolic processes using advanced preclinical imaging technologies, such MRI or OCT; (2) (a) near-infrared branding, sample fixation, dissection, and further EM processing or (b) dissection, high pressure freezing, and freeze substitution; (3) resin embedding (lowicryl if fluorescence is to be preserved); (4) CT for identification of ROI; (5) volume EM. This workflow must be adapted according to the desired biomedical outcome. To preserve the native ultrastructure, cryo-fixation (high-pressure freezing) might be desired. This might be followed either by freeze substitution or by a cryo-workflow with the aim to perform cryo-EM. Surely, preserving the fluorescence (either with LRwhite or HM20 acrylic resins and adapted EM protocols or by keeping the sample under cryo-conditions) can be of advantage to re-locate ROIs. If considering serial section EM (or on-section CLEM), fiducial markers can be added, and a commercial CLEM system can be used to re-identify the ROI in the fluorescence channel and retrieve it in the EM using e.g., SerialEM.

Several workflows have so far been established that solved the above-mentioned challenges on sample preparation, relocalization of ROIs, and data correlation. Recent examples for multiscale combinations of in-vivo and ex-vivo imaging include the correlation of intravital microscopy, CT and EM to study single tumor cells in the cerebral vasculature [81]; correlation of X-ray holographic nano-tomography, EM and FM to disentangle dense neuronal circuitry in Drosophila melanogaster and mammalian central and peripheral nervous tissue [82]; correlation of local neuronal and capillary responses by two-photon microscopy with mesoscopic responses detected by ultrasound (US) and BOLD-fMRI [83]; or extended CMI pipelines that include the correlation of a variety of imaging technologies, such as non-invasive US, CT and highresolution episcopic microscopy (HREM) for phenotyping left/right asymmetries of all visceral organs in a mouse model of heterotaxy or combined OCT, PAI and HREM of chick embryos at multiple development stages [8, 84, 85]. Further examples of novel CMI pipelines that uncover biophysical or chemical information include the correlation of FM, molecular (MALDI MSI) and elemental imaging [X-ray fluorescence (XRF)] to analyze lipids and elements relevant to bone structures in the very same sample section of a chicken phalanx without tissue decalcification at the µm scales [86].

## Correlation Software

In addition to the experimental elements helping to bridge the different modalities mentioned in the previous sections, analyzing automated software solutions to correlate complex, multiscale, multimodal and volumetric image data including reconstruction, segmentation, and visualization are an essential pillar of CMI. Image processing and image analysis in biomedical imaging is a wide field of research, having their own conferences (such as ISBI, MICCAI, or NEUBIAS) and specialized journals (IEEE TMI or Medical Image Analysis for example), with thousands of new methods published every year. One common aspect defining the field is the cross expertise needed to develop new algorithms and software: The physics of the imaging modality, and the knowledge of the biological model or of the disease and organs beyond studies are usually important elements to be considered when developing an image processing or analysis method, making this field highly pluridisciplinary. Methods tackle different problems such as restoration (denoising and enhancing the quality and resolution of the acquired images), segmentation (identifying and spatially localizing objects in images), registration (aligning different images of the same or similar objects), and visualization (generating a comprehensive, potentially interactive, representation of the acquired imaging data). In this review, we focus on the two latter, in the context of CMI. Other main elements are mostly specific to one modality and we refer the reader to existing reviews for general approaches, for example, for the use of deep learning for all of these main components of image analysis [87] or for specific components for a specific modality (such as for EM image data restoration [88]). Note that one exception can be made regarding segmentation, where aligned volume can be sometimes used for what is called multimodal segmentation where information gathered from the different modalities refine the segmentation of the ROI [89]. This last category is actually one example of the interest of CMI from the image analysis point of view, where CMI helps image analysis and quantification.

Image (2D or 3D) registration is the process of computing the transformation linking two images or volumes to overlay matching structures (**Figure 5**). It is a prerequisite for joint quantitative evaluation of the data across modalities and scales for any kind of multimodal visualization of imaging data.

The model of transformation, i.e., the number of degrees of freedom allowed between the two images, is an important choice, relying on the knowledge of the physical relationship between the sample or the organ from one modality to the other modality. This transformation can be seen as a change of coordinate system if the organ or sample was not undergoing important deformation or deterioration between the two modalities. In that case, a rigid (rotations and translations), similarity (rigid plus uniform scaling), or affine (similarity plus shearing or reflection) may be sufficient. If there are deformations due to the sample evolution over time or due to the sample preparation step for the second modality, a more complex model allowing global and/or local deformation will have to be used (non-rigid or elastic models), according to the required accuracy. Currently, registration is often done in a semi-automated way in two steps: first, manual or automatic identification of landmarks or whole structures (segmentation) in the images to be correlated; second, manual definition of corresponding pairs of these features by the user. These landmarks serve then as an input to the registration process, that is computing an optimal transformation by maximizing the spatial matching of all defined features pairs.

In CLEM, mainly three software solutions are used to perform landmark- or segmentation-based registration: a plugin for FIJI [90] (distribution of ImageJ including many useful plugins) called BigWarp (initially developed to provide training and validation sets), a plugin for ICY called ec-CLEM [91], and the

then combined to link all acquisitions and move between scales and modalities. CoS means Coordinate System. T (CoSUS to CoSCT) means the transformation spatially linking the two CoS, allowing for example to locate a US image in the CT volume. To compute the cell in the CT CoS, one can then combine transformations and apply it to the fluorescent cell images (CoSFluo) for example T (CoSFluo to CoSCT) = inverse [T (CoSHisto to CoSFluo)] × T (CoSHisto to CoSµCT) × T (CosµCT to CoSCT). Note that these changes of the coordinate system (or transformations) have to be computed in 3D to take into account possible changes of obliquity, and that they do not take into account deformations induced by sample preparation from one imaging modality to another one.

commercial software AMIRA (Thermofisher, Bordeaux France). Several structures have been used to correlate, including vessel, mitochondria, nuclei, added fiducials such as quantum dots (QDs) in the correlative microscopy field, or specific anatomical landmarks in the medical fields. Several other solutions exist in particular in the medical field, but are very dependent of the medical or biological question and of the workflow of imaging. One method will usually be composed based on a set of existing basic bricks performing one task to achieve the expected results [92].

The challenges in fully automated multimodal registration come from the discrepancy in the appearance of structures by different contrast mechanisms and resolution. While the specimen or sample undergoing imaging is usually kept the same size, imaging can focus on a very different field of view with a very different resolution. Algorithms then have to deal with what is called occlusion effect. The problem is usually tackled in a two-step process: first finding the coarse relationship between images, then doing a more accurate registration, that may take into account local deformation if any [93]. These local deformations are usually due to the sample preparation step (for instance, dehydration in histology, which makes the workflow of **Figure 5** very challenging without the use of fiducials). Two main approaches can be considered [94]:

machine learning approaches [99]. Note that a plethora of variants exists for point-cloud registration, some of them sounding particularly promising for feature-based multimodal registration [100]. Interesting approaches mixed both feature-based and full registration by restraining the learning data set to registered features [101].

For both approaches one of the commonly used libraries for software implementation is ITK (https://itk.org) usually coupled with its visualization counterpart VTK (https://vtk.org).

Very powerful (command line) tools for landmark based or fully automatic image registration (rigid and deformable) are Elastix (http://elastix.isi.uu.nl), its derivative Simple Elastix (http://simpleelastix.github.io) and ANTs (http:// picsl.upenn.edu/software/ants/). They allow the definition of fully parameterizable complex registration pipelines. Both libraries support the creation of so-called templatesstandard reference spaces that enable the co-registration, comparison and joint analysis of images related to the same structure as represented on the template. These images can come either from the same or different subjects and, as long as there is enough joint information content to ensure registration, they can come from another modality. Multichannel imaging, where one channel enables easy registration to the template, can support the integration of imaging data with complementary information to the template, like the integration of anatomical and functional images or spatial gene expression data. One prominent example for such standard spaces are standard brain templates that are used to spatially integrate collections of multi-modal brain data e.g., of humans or rodents like the Allen Brain (https:// portal.brain-map.org/) or Human Brain Project (https:// ebrains.eu) atlases, or the brain of adult [102] and larval [103] drosophila melanogaster.

Visualizing multimodal data, also referred as image fusion, require the knowledge of the spatial transformation linking the images, obtained by registration as explained above. Once this spatial relationship is known, there are several ways to fuse the image information for its interpretation by the user (**Figure 6**). Visualization per se is mainly categorized in two areas: image and volume rendering (for example using raycasting algorithms), and region of interest rendering, using for example surface representation with meshes of polygons to match the ROI outside, after segmentation. One simple surface representation without proper identification is isosurface rendering, where a surface shape is defined by an intensity threshold and a surface mesh generated from it. A third way is to use slicing from a 3D volume and come back to a 2D visualization problem. One of the difficulties in multimodal visualization is the difference of spatial resolution between images, calling for interpolation, e.g., upsampling the images of the modalities with lower resolution to the same resolution as the images with the highest resolution. Most registration algorithms automatically resample the moving image to the resolution of the images to which it is registered (the fixed or target image), meaning pixels not existing in the original image have been created by interpolation.

FIGURE 6 | Example of image sources (volume rendering or segmentation) from different modalities, where each type of modalities will be processed differently to provide the final visualization. Figure extracted from Schlachter et al. [104] under the Creative Commons Attribution License.

Efficient visualization of multimodal images will usually propose a combination of different visualization methods [104] and can add an additional channel of information related to the registration itself, such as the error in registration [105, 106]. One of the particular challenges is to deal with data that do not have the same dimensionality, such as time lapse vs. 3D or hyperspectral images, and heterogenous data [107]. To keep the full resolution of the biggest image, with data that can reach several terabytes in size for just one specimen, efforts are ongoing regarding efficient approaches of displaying and manipulating very big data, such as the big data viewer [108] as also used in the BigWarp Fiji Plugin.

For a more exhaustive list of software used in the field, the reader is invited to refer to a constantly updated list of software established in collaboration between the COST actions NEUBIAS (CA15124) and COMULIS (CA17121): www.comulis.eu & www. biii.eu.

## CHALLENGES OF THE FIELD AND CURRENT SOLUTIONS

## Standardization

### CLEM and Correlative Microscopy

As highlighted earlier, every biological question demands its own technological approach. This makes standardization difficult. There will never be one workflow to tackle every single question. It is important however, to try to avoid re-inventing the wheel all over again. Dissemination of established protocols and training the next generation of scientists in these protocols is therefore of the utmost importance. COMULIS is actively promoting such training and standardization where possible.

In CLEM, one of the areas where standardization would be possible is on the correlation precision. Where correlation down to around 50 nm is currently possible (e.g., [16]), for certain approaches an even more precise correlation would open up completely new possibilities. Can we map single fluorophores onto a single protein structure inside a crowded cellular environment? This is currently a dream scenario but will likely be possible in the future (see section CLEM and Correlative Microscopy).

For the moment we will have to do with internal or external added markers that can be used as fiducials for the alignment of the two datasets. If these dual-modality markers are used also to label specific proteins of interest, the first choice are quantum dots as they are fluorescent and their core, generally being made of Cadmium and Selenium, and is made of heavy metals for visualization in EM. Care must be taken however that the proteins coupled to such probes still fulfill its original function. We have shown that Transferrin coupled to QDs does not recycle anymore but, likely due to multiple receptors binding to one QD, is directed down the degradative pathway [109]. An alternative approach, using fluorescent moieties coupled next to a gold particle, has its own issues. It is well-known that fluorescent dyes can be quenched when in close proximity of gold particles [110]. We have recently shown that Alexafluor488 coupled to a 10 nm gold particle is quenched by 95% [111] rendering that particular probe useless as a true CLEM probe. One is probably better off using two individual probes, one tagged with fluorescence and one tagged with gold particles. In our experience a 1:10 ratio works well. Due to technical restraints we have not been able to measure the quenching effect of smaller gold particles yet. One can also add a fiducial marker from the outside. Kukulski et al. [16, 18] reported the use of 50 and 100 nm sized fluorescent beads that are added just before fixation and which are also readily identifiable in the electron microscope. The current methodology allows to correlate the LM and EM images from such experiments down to approximately 50 nm and would at the moment be considered as the standard. With the increasing integration of super-resolution FM technologies into CLEM workflows, this precision is most likely to improve. This does however warrant a remark. Again, it is all down to the underlying question what kind of precision is required. When one is for instance searching for a rare transfected cell amongst a field of untransfected cells all that is required is a correlation precision in the range of micrometers rather than nanometers.

In correlative AFM, first combinations studied the sample first using FM and then transferred it to the AFM [112], which restricted correlation. Early efforts imaged the fluorescence of labeled molecules and topographically imaged the same area. This was misleadingly termed as synchronized operation. The two high-resolution techniques can hardly operate simultaneously due to their reciprocal disturbances. Apart from the mechanical instability of the construction, FM excitation laser(s) can induce disturbances by influencing the detection system of the AFM and/or by short-time heating of the cantilever. Additionally, the AFM laser can lead to photobleaching of fluorescently labeled molecules. In general, these problems can be solved by carefully planning the performed experiments and adequately assembling the correlative setup. The biological material itself and the molecule of interest has to be immobile or immobilized as otherwise we would not be able to benefit from the merge. Most immobile samples are studied in combination of AFM with superresolution as both techniques demand immobile samples. Optical microscopy allows studying molecules within transparent samples in contrast to AFM, which is exclusively applicable on surfaces. TIRF excites fluorophores only close to an interface between different optical densities. To limit the influence of the optical excitation to the AFM system, it is convenient to combine these two methods.

### Preclinical Hybrid Imaging

Over the last decade, there has been an ongoing discussion about the ability to successfully translate preclinical findings into clinical practice [113–116]. Multiple studies have demonstrated that a bench-to-bedside translation from preclinical results into clinical practice is not as easy as anticipated [113, 115, 117]. **Figure 7** illustrates multiple biological, methodologic and technical factors inherently linked to the reproducibility, reliability, and comparability of preclinical imaging data. Each of these factors has a significant impact on the validity of the acquired data and hence can influence reproducibility and reliability of results. Furthermore, it has been shown that replication of already published results is not as straightforward as the scientific community would hope [113, 119, 120]. Hence, standardization of preclinical imaging protocols and techniques to overcome the "replication crisis" has been stated to be of utmost interest [121, 122].

In sharp comparison to the preclinical research field, clinical standardization is much further advanced and accreditation programs of scanners have been implemented together with unified quality control protocols to ensure reliable, comparable and reproducible results. Furthermore, standardized protocols are in place, which allow multi-center comparison and pooling of the data [123–125]. However, multi-center comparison is still not as easy as anticipated, but up to this point the efforts undertaken have clearly shown its benefit in clinical practice [126, 127].

It is important to emphasize that "over"- standardization in the preclinical environment is not the goal. The fast, dynamic pace of preclinical development is still a strength of one of its kind and should not be outmaneuvered. However, we have to ensure that preclinical findings can be translated more straightforward into clinical research and practice. Therefore, certain techniques, such as anesthesia protocols and animal handling, need to be unified. Furthermore, as has been stated multiple times, precise reporting of methods and techniques is of utmost importance to ensure feasible replication, as well as to facilitate findings from literature and build up based on the existing knowledge [128, 129]. Guidelines, such as the "Animals in Research: Reporting in vivo Experiments (ARRIVE)" guidelines help to improve the quality of reporting, and will consequently maximize the output and validity of published data [129]. In addition, multiple journals have updated their requirements for manuscript submission so that the respective authors either need to upload imaging data as well as all metadata, or to include a data availability statement during submission [130, 131]. An open access of the imaging data and respective metadata is certainly a huge step toward transparency and increased reproducibility and reliability of the data [132], as it has been demonstrated that image analysis is highly user- and software-dependent [133]. Randomized preclinical multi-center studies have been proposed to overcome the lack of reproducibility due to inadequate sample size, low significance, and low confidence of data [134–137]. However, the potential of multi-center studies cannot be fully accessed without proper standardization techniques in place in each participating institute. A recent study focusing on utilizing a basic [18F]fluorodeoxyglucose ([18F]FDG) imaging protocol in 4 different institutes demonstrated that the comparability among multiple institutes might be hampered due to, e.g., animal handling (each institute had different fasting protocols of animals in place; temperature regulation of animals during acquisition differed significantly), and animal facility environment or image analysis [133].

In regards to image analysis, the use of hybrid imaging technologies, with which anatomical co-registration data can be acquired using CT or MRI, significantly enhances the reliability of image analysis since this allows a precise definition of ROIs on the anatomical images that can be overlaid with functional imaging data (e.g., PET or OI data) to ensure the correct placement of ROIs, which is often difficult on functional data only [133]. Multimodal hybrid imaging can enhance reproducibility of results, but nevertheless precise standardized protocols to do so need to be implemented.

There is a huge demand for standardization in preclinical imaging and efforts to implement standardized protocols undertaken by initiatives, such as COMULIS, on a multi-center basis are certainly major steps toward more reproducibility and reliability of preclinical imaging data, as well as increased translation into clinical research and practice.

### Correlation Software

As seen from section Correlation Software, different methods have already been proposed in the literature for image registration or multimodal visualization. However, a few of them are actually used by researchers in life sciences, and one of the main reasons for this is the lack of user-friendly implementation and availability as software. In addition, as underlined in the other sections, every biological or medical questions comes with its own image analysis workflow [92]. In order to help with these workflows, but also to help data sharing and open science, a standardization of the representation of the multimodal spatial relationship and content types will be important. In the medical imaging field, such standards are in place, using Digital imaging and communications in medicine (DICOM), which includes guidelines for the representation of spatial transformation between multimodal images [linear (C20.2 DICOM) or deformable (C 20.3 DICOM)]. However, the list of metadata proposed by DICOM is really exhaustive and may prevent users and constructors to actually fill in this information, which represents 15% of major errors as reported in Gueld et al. [138]. In particular, this effort will be important for imaging modalities for which this standard is not in use now and information is not automatically filled in, or for thirdparty software or home-made methods to compute the spatial transformation that link two images or volumes. For this reason, there is ongoing effort associated with the deployment of public image archive [139–141] to define some minimal metadata requirement, and the one associated with CMI have still to be defined by the community.

Computing the accuracy and assessing the quality of the registration is one of the central problems of correlative microscopies or more generally CMI, in particular because the structure of interest is usually not marked in both modalities, and so it is essential to confirm the correlation is correct and not biased by user assumptions. This is of particular importance when dealing with largely multiscale approaches, since one pixel can be matched with a structure of hundreds of pixels in another modality, and then an error of one pixel could lead to erroneous conclusions. In previous publications [16, 142], an iterative leave-one-out method was used to assess the accuracy of the registration, where the registration error was computed as the average error of localization of beads not used for the registration and was therefore empirical. Recent work tried to find a theoretical estimation of the error, using the Cramer

Rao limits [143] to estimate the transform error by taking into account the high resolution limit of accuracy. In ec-clem, the error is estimated using a formalism from the medical field for 2D and 3D rigid registration [144], originally developed for image guided surgery. It can be applied to correlative microscopy but is limited to a rigid-transformation point-based framework, and has the advantage of not requiring any ground truth matching in the images. In addition, this method may lack a proper mathematical formalism to demonstrate their usability in the fields. Note that metrics to compare images based on intensity may not be adapted in most of the case due to the discrepancy of image content (structures not present in both images). In the case of the absence of fiducials or anatomical landmarks to validate the registration, another currently used method to validate the accuracy of a registration is to use segmentation quality metrics, such as DICE metrics, to assess the overlap between known segmented structure.

Challenges are the competition of algorithms on a given dataset, where the ground truth is known in order to rank the algorithms according to a set of metrics designed for the challenge. Some challenges have been organized for the multimodal field, but usually focused on one particular problem (Anhir focused on multiplexed histological data or Curious for US to MRI brain images registration [145]). These challenges are of great interest since they provide a way to identify the actual level of accuracy state of the art and give direction for future research. They will also help define common and standard ways to assess the accuracy of the alignment of multimodal data, by defining community accepted stand error metrics.

## Requirements for Optimization and Challenges to the Field

### CLEM and Correlative Microscopy

In the recently published book "Correlative Imaging: Focusing on the Future," there was one over-arching theme that was highlighted in almost all chapters: Data. If handling data from a single modality can already create headaches for the data analysis and IT people, how about trying to combine datasets from different imaging modalities. Developments and possible solutions will be discussed in other parts of this review.

On the hardware side there are also clear trends visible and they almost seem to diverge from each other. On the one hand, there are the cryo-CLEM approaches trying to map protein structures in ultrastructural data, on the other, volume CLEM (a collection of techniques including FIB-SEM, SBF-SEM, array tomography, electron tomography) is more focused on large scale structures and mainly looks at connectivity between cells.

As with the integration of GFP in CLEM from 2000, the resolution revolution in cryoEM has now also been integrated into CLEM workflows, especially aided by the development of LM stages that can work under Liquid Nitrogen conditions [146– 148]. These devices allow for the observation of fluorescent structures of plunge frozen samples and record their location for further studying in a cryo-TEM. Whereas, these stages are now fairly commonly in use, the development of cryo-CLEM workflows is still improving; e.g., super-resolution cryofluorescence has been recently shown [149]. One of the issues that needs to be dealt with in cellular cryoEM is the thickness of the sample. In most cases, only the outer edges of a cell can be directly imaged. Anything more inside will be too thick to image directly. Cryo-FIB-milling is currently the only way to acquire thin slices of frozen material for cryo-electron tomography (ET) [150]. Targeting the correct area in Z-height, the depth of the sample, is still one of the bottlenecks but acquiring fluorescence data in 3D using confocal cryo-fluorescence will further aid with this targeting problem.

Life is 3D, so also techniques falling under the quiet revolution banner (FIB-SEM, SBF-SEM, Array tomography, electron tomography) are being integrated more and more into CLEM approaches. Especially in these cases, finding the structure of interest adds another dimension of complexity. Resolution in z is generally lower than in x, y so targeting is even more difficult. Also acquisition of z-stacks acquired before processing for EM can be useful and essential but the coordinates may change during the processing. The addition of fiducials or endogenous tissue-existing landmarks, such as blood vessels, can be useful. In addition, a bridging step with intermediate resolution such as CT are relatively new additions to the CLEM workflows [80].

Fully integrated light and electron microscopes should be able to provide the best correlation between the two modalities and both integrated LM-TEM [151] and LM-SEM [111, 152] have been developed and are still improving. Of note here is that all these systems can only work with fixed samples and one of the hallmarks of light microscopy, live imaging, is lost. As before, it is the biological question that will determine what workflow and technology fits best.

In correlative AFM, correlative challenges and opportunities are faced by recent technological advancements, improving temporal and spatial resolution of already established techniques. The combination of these highly sophisticated setups is far from trivial and highly challenging. High-Speed Atomic Force Microscope, for example, allows studying dynamic processes at sub-molecular and sub-second scale and can be combined with STED to track molecular movement at nanometer resolution and in the millisecond range.

To enable a precise and simultaneous superposition, disturbances in correlative AFM must be sufficiently shielded. An acoustically shielded chamber around the FM including the AFM measuring head is suitable for this purpose. All components that can cause electronic or acoustic interference must be removed from the isolation chamber. Typically, water-cooled EM-CCD cameras are used here to minimize interference from the outset. During simultaneous applications, the problem arises that the measuring tip of the AFM is heated by the excitation laser. This problem cannot be prevented; in this case, combinations with TIRF or confocal microscopy are usually used. It is important to keep the excitation energy at the position of the cantilever low. If both techniques are used at the same time, a real time superposition of the images is currently not possible. In most cases, the images have to be adapted to each other by mathematically forced imaging errors. For this purpose, a fluorescent grid is suitable which is taken before the actual measurement with both techniques at the same position and subsequently adapted to each other. Currently, the AFM manufacturers already offer their own software for superimposing the images. The measuring tip is moved to several different positions, which are recorded simultaneously on the microscope and then superimposed directly. However, the measuring tip in the fluorescence image cannot be superimposed more precisely than the resolution of the microscope. Of course, the resolution can be increased by fitting techniques, known from super-resolution microscopy, but the resolution of the AFM itself can never be achieved.

Further challenges will be the combination and further development of additional microscopy technologies, such as correlative SXT. Since imaging under cryo-conditions preserves a close-to-native environment (as for cryoEM) and in certain cases (as for SXT due to its operation in the water window) is the only possible implementation, the development of cryo-FM will play a crucial role in correlative microscopy. For correlative SXT, superresolution FM is specifically appealing since it matches the achievable spatial resolution, and further efforts will focus on its cryo-implementation [23].

## Preclinical Hybrid Imaging

One of the current bottlenecks for PHI lays in radiation doses for X-ray imaging together with radioisotopic imaging methods. CT, PET, and SPECT imaging delivers substantial radiation dose into the animal, and, of course, to the patient as well in clinics. The prolonged CT scanning times along with high doses of radioisotopes applied to the animal may interfere with the immune system, tumor growth and rapidly proliferating tissues (bone marrow, intestine) renewal. There are estimates that up to one percent of patients repeatedly scanned for possible metastases by whole body CT, PET/CT or SPECT/CT during a 5-years follow-up period may die because of new tumors induced by the imaging process [153, 154]. The significantly lower radiation load to the patient brings PET/MRI imaging; nevertheless, the high dose coming from high energy PET isotope injection cannot be avoided. Moreover, CT is excellent for hard tissue (bone) and contrast (e.g., angiography) imaging but soft tissue discrimination is rather poor. Several companies brought to the market so called spectral CT devices. They are usually based on dual energy X-ray sources, and the comparison of the energy-dependent attenuation of signal can improve the soft tissue recognition and distinguish contrasts and the bone, which is not possible in classical CT devices [155]. These CT machines can utilize slightly lower radiation dose compared to previous generation. Due to the recent progress in the development of novel radiation detectors, there occurred a possibility to introduce a completely new radiation detection approach. Under international collaboration in CERN, the photon counting TimePix detectors were developed. The current generation of TimePix3 detectors allows a simultaneous detection of the exact position, energy and time of the photon interaction. These properties can be used for true spectral CT detection. Novel detectors are much more sensitive and have a high spatial resolution of 55µm. The CT image can be obtained very fast (in seconds compared to 20 to 30 min for high-resolution standard scans), thus significantly reducing the absorbed radiation dose. The filtering of noise allows to increase the signal-to-noise ratio. According to different attenuation tissue patterns, soft tissue recognition is easier even without contrast [156]. The high speed of TimePix3 detectors (1,700 images per second) and energy resolution is suitable for coincident event registration also in PET imaging [157]. The proof-of-principle of the use of the TimePix3 detectors has been published [158]. Several groups are testing the use of Compton cameras for SPECT imaging instead of collimated SPECT detection [159, 160]. Compton cameras allow to calculate the trajectory of incoming photons from original hit position and Compton scattering detected by a second detector layer. This allows to increase the sensitivity of SPECT from <0.1 to 80% for the most used SPECT isotope 99mTc. SPECT imaging thus only requires a fraction of currently used radiation activity, and the absence of collimator facilitates the construction of combined CT/PET/SPECT devices with just one ring of detectors that can simultaneously record fast fully trimodal hybrid whole body imaging with very low radiation load. The main limitation is still the high price of detectors which could be in future substantially lowered by demand for production in large series.

Most of the optical imagers allow to detect fluorophores from visible light up to the NIR region with the longest wavelengths around 850 to 900 nm. This covers the close NIR region with relatively good light penetration. The signal loss in deeper tissues does not permit quantitative data nor fully tomographic imaging. Mouse tissues are much more transparent for shortwave infrared light (SWIR) with wavelengths between 1,000 and 2,000 m. No autofluorescence, reduced light scattering and lack of absorption by blood are the main advantages of imaging in the SWIR region [161]. The first commercial in vivo optical scanner allowing imaging of contrasts with longer excitation wavelengths than 1,000 nm appeared on the market in 2019. The method is still limited by the low availability of fluorescent contrast agents for use in the SWIR region.

Besides, there is the current challenge of correlating additional beneficial but not yet readily/commercially available imaging modalities (same challenge as faced by the biological microscopy community). For example, while fusion of optical tomography data (DLIT, FLIT, or FMT) with CT can be achieved using commercial imaging systems, co-registration of OI and MRI is still in the developmental state. As OI and MRI are recorded in separate systems, relocations of animals between the recording sessions have to be conducted with great diligence to avoid anatomical distortion and positional changes. Chehade developed a shuttle made of CT- and MRI- compatible material, which allowed the relocations of mice while keeping them properly in place [77].

These challenges hold also true for in-vivo microscopy despite its impressive advances. It is still challenging to synergistically combine optical technologies in one platform since they do not match in imaging speed, size, resolution and contrast, and proper imaging pipelines have to be established. In this context, CMI platforms for label-free sample screening bear great potential, and working combinations of these modalities (such as OCT, RS, MPM, SHG—STATE-OF-THE-ART/Preclinical Hybrid Imaging) will need to be identified on the basis of their added value in tackling specific biomedical research questions.

### Novel CMI Pipelines

Novel CMI pipelines will continue to bridge in-vivo (preclinical) and ex-vivo (biological) imaging and allow to zoom in from physiological native tissue context to subcellular molecular resolution. This comes with several challenges to be tackled: (1) sample preparation that is compatible across imaging modalities without compromising data quality, (2) hard- and software solutions to relocate the same ROI after changing the imaging platform, (3) robust markers that can be detected in different imaging technologies, (4) lack of high throughput and automatization, (5) software solutions to correlate the imaging data and standards for data handling and storage, (6) availability of research infrastructure (i.e., cutting-edge imaging technologies from PET scanners down to cryo-EM).

(1) Sample preparation procedures are mainly an issue across ex-vivo imaging technologies since these require specific fixation and embedding that might be incompatible with other techniques. Typically, most in-vivo imaging protocols such as MRI, CT or US do not interfere with downstream processing for histologic and ultrastructural observation. This is true even when routine contrast agents are administered for in-vivo imaging. Typical example of incompatibility are the quenching of fluorophores by standard EM preparation protocols (compare section CLEM and Correlative Microscopy) or incompatibility of glutaraldehyde fixation or JB4 resin embedding with many protocols for immunohistochemistry. In general, fixation is a critical parameter in correlative workflows and requires optimization since it usually comes with sample-distorting artifacts, such as tissue shrinkage, swelling, hardening, and color change. Besides, it is of highest importance to fix the tissue or organism right after euthanasia to prevent autolysis and degradation of cellular structures [162]. To improve the penetration of fixatives, the tissue or sample might need to be incised. The sample should then be incubated in at least 20 times the sample volume, and, to facilitate correlation, be pre-embedded in 1% agarose using a casting mold prior to processing [163]. Ideally, both macroscopic and microscopic morphologies are preserved by the simultaneous stabilization of all cellular components as achieved by cryofixation. However, while preserving the close-to-native morphology, the drawback of cryofixation in comparison to chemical fixation is its limited depth to which samples can be well-frozen. High-pressure freezing allows to fix a thickness of maximally 0.6 mm. Continuing to work under cryo-conditions ensures close-to-native architecture, but poses additional challenges to potential follow-up microscopy technologies such as FM—since current cryoobjectives are limited in their optical performance (such as low numerical aperture) [164].

Importantly, all destructive staining needs to be avoided: An example is the perfusion of vasculature with heparin, formalin, NaCl and barium sulfate with gelatin to stain blood vessels via the ventricles after anesthesia in mice. Vasculature staining can be replaced in certain cases by post-mortem staining with Lugol's solution, a mixture of one part iodine and two parts potassium iodide in water [165].

Imaging thick tissues or entire organisms in vivo and subsequently zooming-in into the subcellular ultrastructure is facilitated by current advances in FM of non-transparent organisms, such as longer wavelengths for deeper penetrations depths [166], development of improved near-infrared probes [167], or photoacoustics [168]. While studying even thicker tissue—though in-vitro will be facilitated by further advancements in clearing larger samples using lipid extraction to reduce light scattering, it is questionable whether this will also facilitate correlative microscopy approaches since clearance of larger samples might interfere with ultrastructural preservation.

(2) Identifying the same ROI across diverse in- and ex-vivo imaging platforms is currently an inherent bottleneck of novel CMI approaches. Specifically, the relocation of a ROI in thick living tissue at high subcellular or molecular resolution presents the biggest challenge faced by CMI when bridging in-vivo preclinical imaging and ex-vivo biological microscopy. As outlined in 2.3, current strategies focus on NIRB as an intermediate step for volume CLEM. If no additional processing step is foreseen between modalities (which might induce distortions or even require reduction of the volume by sectioning), the straightforward approach to correlate the ROI across modalities is to use a joint transferable coordinating system. A well-established example is the annotation of FM to define ROIs with a dedicated CLEM module and import and relocate these coordination lists into the cryo-EM microscope using SerialEM [169]. Other approaches focus on using the same holder for different imaging modalities. Examples include immobilization beds for preclinical imaging in mice using PET and CT (where additionally <sup>22</sup>Na fiducial markers can be placed into stationary pegs at defined depths to provide a 3D references to simplify image registration), or the combination of x-ray spectromicroscopy with electron tomography, where Allende meteorite grains were deposited on a TEM grid and transferred between the electron microscope and the COSMIC soft x-ray beamline [170]. First "plug-and-play" holder solutions are being described that are compatible and even commercially available, which can fit in a variety of microscopes for correlative imaging without changing the holder. While it facilitates relocation of ROIs tremendously, this approach cannot overcome the main limits or CMI pipelines: To assess a ROI in thick tissue, the tissue will still need to be cut due to the limited penetration depths of most high-resolution ex-vivo microscopy techniques.

To facilitate ROI relocation, CMI also aims at setting up hardware-fused hybrid setups that inherently co-localize the same ROI due to their joint coordination system. Apart Walter et al. Correlated Multimodal Imaging in Life Sciences

from well-known commercially available PHI scanners (such as PET/CT, SPECT/CT, or PET/MRI), as described in section State-of-the-Art, examples include (i) a variety of hybrid AFM and FM setups [30, 171, 172], (ii) first implementations of integrated EM-FM setups [111, 173], (iii) several combined setups of OCT with photoacoustics or non-linear microscopy [174], and (iv) diverse hardwarebased approaches to combine OI techniques with CT or MRI [175]. Nevertheless, in certain cases, relocation across single modality systems using cross-platform transport beds for CMI may provide superior performance compared to hybrid systems where compromises may have been made in the integration process.

If hybrid setups are not available (as is mostly the case), correlation can be facilitated by imaging the exact same sample without intermediate processing steps. For example, instead of performing FM before EM fixation and embedding, EM sections could be imaged directly with FM if preserving fluorescence or immunolabeling them.

(3) There is a plethora of multimodal probes for correlative microscopy and preclinical imaging, but there are hardly any robust markers that can be detected across modalities when combining various contrast mechanism with high microscopic accuracy. For correlative microscopy, most commonly used markers include QDs or polymer beads. Other examples include biocompatible nanosized, fluorescent and electron-dense intracellular nanodiamonds (internalized in living cells via endocytosis) as probes for 3D CLEM [176]. For the combination of preclinical imaging modalities, mainly CT, MRI, and optical approaches, there is a variety of CMI probes: (1) Lipid-based markers, such as liposomes or lipoproteins as carriers; (2) macromolecular carriers where different contrast agents are attached to a common macromolecule (and its reactive amines, thiols, or carboxyls); (3) nanoparticles, such as QDs, iron oxide nanoparticles or nanoparticle carriers; or (4) small molecules where two or more probes are directly fused together with minimal intervening bonds [177]. Further examples include high-contrast, non-radioactive tungsten-based fiducial markers for multimodal brain imaging with MRI, PET and CT that are attached outside of the sample in close proximity of the ROI, and numerous dual PET and NIR fluorescence imaging probes [178, 179], such as fluorescence-labeled monoclonal antibodies (mAbs) and systemic applications of both mAbs and peptides in PET/SPECT in-vivo. While <sup>64</sup>Cu the prevalent isotope for systemic mAb imaging, <sup>18</sup>F and <sup>68</sup>Ga isotopes better match the targeting half-lives of peptides. Although dual agents for PET and NIR imaging are in its infancy and no agent has been approved by the FDA so far, several preclinical applications have been reported [179]. Examples for markers for advanced CMI pipelines include photo- or chemically-convertible tags (such as miniSOG or APEX) that can be detected in FM, CT and EM and were used to identify the ROI across multiple imaging modalities [180–182].

Most multimodal probes are exogenous. The ultimate goal is to have the organism or cells express their own probes after transfection. Fusions of GFP for fluorescence imaging and herpes simplex virus thymidine kinase (HSV-TK) for PET have been reported in several studies. HSV-TK can also be fused to other optical reporters and constructs of luciferase. With QDs and (NIR) fluorophores being used across preclinical and biological imaging modalities, these two markers appear most promising for advanced CMI pipelines. While a single CMI marker will guarantee the same pharmacokinetics and colocalization of the signal for each modality and reduces the stress on the blood clearance mechanisms of small animals (as induced by multiple doses of agents), the variations of sensitivities of different imaging modalities need to be considered when aiming for the detection of a single probe correlatively. In certain cases, it may not be practical to simply add all functionalities to one molecule [177].

For a rough alignment of untreated samples between relocation of imaging platforms, registration marks such as gridded coverslips or finder grids [183], or deposition of metal structures or engraving of the surface are commonly used. To provide rough orientation when dissecting ROIs from living organisms for subsequent ex-vivo analysis, the margins of adjacent tissue are demarcated in their bodily orientation with surgical ink or notches on the skin. For advanced CMI pipelines from thick tissue to 2D sections, endogenous landmarks or NIRB can be used. In volume CLEM, blood vessels, nuclei or myelinated axons can be used as endogenous fiducials since they show sufficient contrast both in light and electron microscopy and are distinctive in size and shape—as demonstrated for mouse brain imaging using a CMI pipeline with in-vivo 2-photon microscopy and FIB/SEM [6].

registrations and quantification, data handling specific to multimodality needs to be established, including universal imaging formats, ontologies, and data storage and repositories. While there is currently no universally established microscopy format with additional diversity between preclinical and biological imaging approaches, repositories and public archives for diverse imaging data are being implemented—from single molecules (EMPIAR) to tissues (Tissue-IDR). Correlative data sets (such as CLEM data to link functional information across spatial and temporal scales) will be included in the so-called addedvalue databases that are developed around the archive. They aim at gathering a greater understanding for specific biological areas through systematic integration of images [141]. Integration of such multimodal data sets and their interoperability will be facilitated by universal large-scale multi-granular imaging ontologies, whose need is being described in first publications [186, 187].

(6) Novel CMI pipelines require access to diverse imaging technologies. All these technologies and the necessary expertise are unlikely to be found in a single laboratory, which restricts the development and implementation of CMI. To facilitate access to complementary imaging technologies and exchange of knowledge, several initiatives have been established. Two prominent examples are (1) COMULIS (Correlated Multimodal Imaging in Life Sciences), a COST Action (CA17121) to foster CMI, and (2) Euro-BioImaging, a European Research Infrastructure Consortium providing open access to imaging technologies in biological and biomedical research.

## Correlation Software

As already underlined, most of the automated methods are developed ad hoc for a particular multimodal problem. Machine learning and deep learning are definitely moving the image analysis field a step forward [87] since their main interest is to create an ideal method of processing based on training data sets, translating the effort of developing ad-hoc computer vision or signal processing methods to the effort of annotating data and formalizing the problem as input/output. Note that deep learning is still in progress and a lot of research is still going on to optimize these methods and reduce the number of training datasets required, as well as taking into account the errors in annotations in training data sets [87]. Based on these approaches, a universal solution without any user input is not envisioned per se, but rather a universal framework for multimodal registration, or the creation of a giant bank of pretrained models. It could be envisioned to be set up with minimal user input, i.e., by providing registered data sets or at least identifying on both modalities what should be used for matching. There are ongoing approaches for a universal segmenting tool, based on deep learning, trained on different data, for example, for nuclei segmentation [188]. Interestingly, it has been shown that some of the models trained could be applied in a new different modality (even if all at the same microscopic scale), for example, with different staining without the need of further training.

Another challenge in the field is the integration of very heterogeneous data, such as CMI with very different dimensions (multiplexing or spectral data with hundreds of outputs for one spatial localization, temporal vs. static) with non-imaging data such as -omics data. This effort could be facilitated by two approaches: the single cells approaches, and the development of spatially localized proteomics or genomics which are now starting to appear as commercial platform and would facilitate these links. But then the analysis of this largely heterogeneous data still requires to develop new statistical tools.

Another trend is to use multimodal aligned images as training sets to construct inference models to reduce the needs for one or the other modality. For example, Li et al. [189] used a deep learning approach to generate PET images from MRI and demonstrated similar classification results using the generated PET images than the true PET images for Alzheimer Disease and Mild Cognitive impairment. In this preliminary study, a 3D convolutional neural network was trained with MRI patches as input and matching PET patches as output using half of a database of patients having both exams. The parameters of the network capture a relationship between both modalities. This trained network was then used to generate the predicted PET images from MRI images, and validated against the remaining half of the database. The same principles have been applied also to microscopy images, where for example restoration based on deep learning have shown impressive results [190], and even predicting fluorescent labeling from transmitted light images [191].

These approaches in the long term could then reduce the number of modalities required to answer a particular question. To achieve such a goal, sharing well-annotated aligned multimodal data is of particular importance. Efforts are on-going in this direction to share repositories and public archives ([141], Empiar, IDR).

## CONCLUSIONS AND OUTLOOK: FUTURE OF THE FIELD

To have an even bigger impact and to become a basic life science technology as FM is nowadays, it will be crucial for correlative microscopy to develop and disseminate automated workflows that can deal with huge amounts of data and seamlessly merge diverse data sets. This process will be facilitated by a number of factors: the improved capabilities of integrated systems, the adaption of standard file formats, and the deposition and sharing of these information-rich datasets.

For PHI, preclinical molecular in-vivo whole-body imaging is fully dependent on hybrid imaging and co-registration with anatomical images. Some devices are already multimodal in their hardware settings, but images from different techniques are taken sequentially and implemented automated co-registration often requires manual intervention to obtain the best results. Multimodal animal beds allow to scan the same anesthetized animal in different devices, co-register multiple imaging methods and obtain enhanced molecular information about the in-vivo processes. The implementation of new methods and contrast agents broadens the spectrum of imaging possibilities, and simultaneously acquired multiple hybrid images facilitate proper visualization. Besides, OI has seen significant expansion in biomedical and diagnostic applications, which go beyond simple visualization of the sample. Novel modalities allow label-free mapping of biomolecules in vivo providing a way to determine a stage of disease progression or enable tomographic assessment of deep tissue layers. Nevertheless, current research efforts are indicating that in many biomedical applications, a single modality is inadequate to provide a comprehensive picture of a disease. Instead a targeted combination of modalities, which give access to a set of (label-free) parameters is necessary.

In summary, CMI is a field under construction, relying on broad expertise. In particular, data processing, analysis and management need to be incorporated in the initial reflections leading to a project, and a continuous and iterative dialog has to take place during the whole project with image data analysts such that the communities can understand the requirements and needs of each other. Setting up standard approaches and sharing protocols and generated data will definitely be the key elements for achieving a smooth communication. A real holistic view will be achieved when other type of data will be also correlated with imaging data, but to reach such a goal the CMI community needs to develop its own solid ground.

Ideally, CMI will lead to multimodal platforms that allow to functionally and morphologically characterize the entire sample in-vivo, fast and non-destructively at high axial and lateral resolution and high penetration to gain a mechanistic understanding of organisms and diseases. By synergistically fusing complementary imaging techniques, CMI platforms can give insights into a variety of tissue properties during a single image acquisition, and better tissue characterization can be achieved than by the separate imaging modalities alone. Complementary information provided by the fused

## REFERENCES

imaging modalities and machine-learning-assisted data analysis will ultimately yield novel biomarkers by a multi-dimensional classification accelerating the discovery and translation of novel therapeutic strategies. Such hybrid platforms of high accuracy will correlate the modalities instantly without the need for post-processing correlation software. Surely, 3D cellular, ultrastructural and molecular tissue maps as acquired by CMI will substantially transform biomedical research and diagnostics in the future.

## AUTHOR CONTRIBUTIONS

AW initiated, coordinated, and supervised the manuscript and wrote all CMI sections, Introduction, Conclusion, and Abstract. LS wrote main parts of the PHI sections. PP-G wrote all software sections. PV and BP main parts of the correlative microscopy sessions. JM, MO, BP, PS, AU, SH, DF, and MM-D contributed to each section. KB, SG, MG, TW, and WW provided feedback and edits.

## FUNDING

This work has been supported by the Federal State of Upper Austria and "Land OÖ Basisfinanzierung" and Timed Center Upper Austria TC-HyperChol.

## ACKNOWLEDGMENTS

PP-G acknowledges ANR-18-CE45-0015 and the France-BioImaging infrastructure supported by the French National Research Agency (ANR-10-INBS-04). LS acknowledges the large RI Project LM2015062 Czech-BioImaging funded by MEYS, Czech Republic. KB acknowledges the FFG COMET center project 854174. All authors acknowledge their collaboration facilitated by COST as members of COMULS (CA17121).

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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Walter, Paul-Gilloteaux, Plochberger, Sefc, Verkade, Mannheim, Slezak, Unterhuber, Marchetti-Deschmann, Ogris, Bühler, Fixler, Geyer, Weninger, Glösmann, Handschuh and Wanek. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions

Robert J. Anderson<sup>1</sup> , Christopher M. Long<sup>1</sup> , Evan D. Calabrese<sup>2</sup> , Scott H. Robertson<sup>1</sup> , G. Allan Johnson<sup>1</sup> , Gary P. Cofer <sup>1</sup> , Richard J. O'Brien<sup>3</sup> and Alexandra Badea1,3 \*

*<sup>1</sup> Department of Radiology, Duke University, Durham, CA, United States, <sup>2</sup> Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States, <sup>3</sup> Department of Neurology, School of Medicine, Duke University, Durham, CA, United States*

### Edited by:

*Andreas Hess, University of Erlangen-Nuremberg, Germany*

#### Reviewed by:

*Boran Han, Harvard University, United States Silvia Capuani, Italian National Research Council, Italy*

> \*Correspondence: *Alexandra Badea<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Medical Physics and Imaging, a section of the journal Frontiers in Physics*

Received: *07 December 2019* Accepted: *11 March 2020* Published: *21 April 2020*

#### Citation:

*Anderson RJ, Long CM, Calabrese ED, Robertson SH, Johnson GA, Cofer GP, O'Brien RJ and Badea A (2020) Optimizing Diffusion Imaging Protocols for Structural Connectomics in Mouse Models of Neurological Conditions. Front. Phys. 8:88. doi: 10.3389/fphy.2020.00088* Network approaches provide sensitive biomarkers for neurological conditions, such as Alzheimer's disease (AD). Mouse models can help advance our understanding of underlying pathologies, by dissecting vulnerable circuits. While the mouse brain contains less white matter compared to the human brain, axonal diameters compare relatively well (e.g., ∼0.6µm in the mouse and ∼0.65–1.05µm in the human corpus callosum). This makes the mouse an attractive test bed for novel diffusion models and imaging protocols. Remaining questions on the accuracy and uncertainty of connectomes have prompted us to evaluate diffusion imaging protocols with various spatial and angular resolutions. We have derived structural connectomes by extracting gradient subsets from a high-spatial, high-angular resolution diffusion acquisition (120 directions, 43-µm-size voxels). We have simulated protocols with 12, 15, 20, 30, 45, 60, 80, 100, and 120 angles and at 43, 86, or 172-µm voxel sizes. The rotational stability of these schemes increased with angular resolution. The minimum condition number was achieved for 120 directions, followed by 60 and 45 directions. The percentage of voxels containing one dyad was exceeded by those with two dyads after 45 directions, and for the highest spatial resolution protocols. For the 86- or 172-µm resolutions, these ratios converged toward 55% for one and 39% for two dyads, respectively, with <7% from voxels with three dyads. Tractography errors, estimated through dyad dispersion, decreased most with angular resolution. Spatial resolution effects became noticeable at 172 µm. Smaller tracts, e.g., the fornix, were affected more than larger ones, e.g., the fimbria. We observed an inflection point for 45 directions, and an asymptotic behavior after 60 directions, corresponding to similar projection density maps. Spatially downsampling to 86µm, while maintaining the angular resolution, achieved a subgraph similarity of 96% relative to the reference. Using 60 directions with 86- or 172-µm voxels resulted in 94% similarity. Node similarity metrics indicated that major white matter tracts were more robust to downsampling relative to cortical regions. Our study provides guidelines for new protocols in mouse models of neurological conditions, so as to achieve similar connectomes, while increasing efficiency.

Keywords: diffusion imaging, MRI, connectivity (graph theory), mouse model, brain, neurodegenerative diseases

## INTRODUCTION

A growing body of evidence suggests that altered brain connectivity is present in a variety of neurologic and psychiatric diseases. For example neurological conditions such as AD [1, 2], autism spectrum disorders [3], and psychiatric conditions such as schizophrenia [4] can be viewed as connectopathies. Multimodal network analyses can help identify early alterations associated with pathological processes both at the functional and structural levels. Structural connectomes based on diffusion MRI may produce sensitive biomarkers to monitor these conditions since they integrate the effects of multiple pathologies (atrophy, myelination, toxicity of the environment due to, e.g., oligomers, microstructural changes) [5].

While studies on diffusion imaging in the human brain have previously addressed the tradeoffs associated with spatial vs. angular resolution, the mouse brain provides insight into white matter connectivity at a vastly different scale and, thus, deserves special attention. We need to compare 2-mm linear dimension voxel sizes in humans (corresponding to 8-mm<sup>3</sup> voxel volumes), vs. 0.2–0.043-mm linear dimension voxel sizes (corresponding to <sup>∼</sup><sup>8</sup> <sup>×</sup> <sup>10</sup>−<sup>2</sup> - 8 <sup>×</sup> <sup>10</sup>−<sup>5</sup> -mm<sup>3</sup> voxel volumes) required to distinguish similar levels of anatomical detail in mouse brains [6– 11]. This increased resolution enables the observation of brain architecture at the level of cellular layers, which, therefore, can help us gain insight into the mechanistic drivers behind the etiology and progression of disease, using animal models where axonal dimensions are relatively similar to humans [12, 13].

Yet, using sensitive methods for identifying pathways affected early on in animal models remains difficult. Difficulties in optimizing diffusion imaging protocols arise from the need for long acquisition times, to achieve sufficient signal to noise for smaller voxels. These are required for dissecting vulnerable pathways and networks, early on in the disease process, or subtle changes in time or following interventions.  The imaging protocols required for resolving small tract-based connections are, thus, demanding a high spatial resolution. In addition, increasing the angular sampling and the number of b values may reduce biases and produce more accurate models. This translates into long protocols, and subsequently costs in terms of time and money, which need to be balanced against the desired increases in spatial and angular resolution.

In this work, we have examined the balance between spatial and angular resolutions and inferred suggestions for recommended future protocols. In particular, we examined a set of nodes/brain regions that are relevant for neurodegenerative conditions such as AD. We used simulations based on downsampling a high-spatial, high-angular data set, and assessed the effect of relaxing these requirements/parameters on the accuracy of the reconstructed tracts and connectome, when compared against our reference protocol. We have focused our analyses on regions expected to be affected in AD, such as fimbria, fornix, and hippocampus, septum, hypothalamus, and also the lateral geniculate nucleus. We evaluated how different protocols led to increasingly more robust results and how fast errors change with spatial and angular sampling resolution. Our results can inform future population studies, in particular, for models of neurodegenerative disease.

## METHODS

Imaging was performed, as previously reported [14] on a 9.4- Tesla small-animal imaging system controlled by an Agilent VnmrJ 4 console. Diffusion imaging was accomplished using a 3D diffusion-weighted spin-echo pulse sequence with repetition time (TR) = 100 ms, echo time (TE) = 15 ms, and b value = 4,000 s/mm<sup>2</sup> . The acquisition matrix was 568 × 284 × 228 over a 24.4 × 12.2 × 9.8 mm field of view, resulting in isotropic 43 × 43 × 43µm voxels (referred from now on as 43-µm linear voxel dimension). The diffusion protocol included 120 diffusion directions [15, 16] and 11 non-diffusion-weighted (b0) measurements (i.e., one b0 every 12 diffusion measurements).

Angular downsampling of the original diffusion data set was performed by obtaining the optimal diffusion directions for each angular subset [15, 16] and extracting the closest gradient vector from the 120 unique diffusion directions. The closest gradient orientation was chosen by maximizing the dot product between the optimal gradient vector and the possible vectors found in the original gradient table.

Spatial downsampling of the original diffusion data set was performed by cropping the data in k space, resulting in three levels of isotropic spatial resolution: 43-, 86-, 172-µm linear voxel dimension.

The condition numbers for the gradient subset matrices, defining angular sampling, were calculated in MATLAB (Natick, MA), based on the ratio between the largest and smallest singular values for each matrix. The stability of the condition numbers was evaluated for each of the gradient subsets following 50,000 simulated rotations of these matrices.

Diffusion data processing was done on a high-performance computing cluster with 96 physical cores and 1.5 TB of RAM. All 131 image volumes were registered to the first b0 image using advanced normalization tools (ANTs) affine transformation [17] to correct for eddy current distortions. Scalar image volumes were reconstructed using FSL's DTIFIT [18]. Fiber data for probabilistic tractography were reconstructed using FSL's BEDPOSTX [19] with a maximum of four fiber orientations per voxel. In-home written scripts and FSL were used to estimate the numbers of tracts with one, two, three, or four dyads and to estimate errors/uncertainty based on dyad dispersion.

Automated atlas-based segmentation was performed [20] using an atlas, which combines the Waxholm Space atlas [21] for subcortical labels, and the Ullmann atlas of the neocortex [22], with a total of 332 regions—symmetrized relative to the midsagittal plane. Connectomes were constructed using SAMBA [23], and DSI Studio [24] for a subset of brain regions. These include for simplicity, the connectivity matrices generated pertaining solely to left-sided seed regions connecting to leftsided targets. We next evaluated the similarity of connectomes based on global Spearman correlation coefficients among 12 representative acquisition schemes (four angular sampling

TABLE 1 | The smallest mean condition numbers (CN) ranked the 120-direction scheme as optimal, followed by the 60, then 45-direction schemes.

schemes and three spatial resolution levels). Node wise similarity was evaluated as in Blondel et al. [25]. Specifically, the similarity matrices were obtained as the limit of the normalized even iterates of S<sup>k</sup> + 1 = BSkA <sup>T</sup> <sup>+</sup> <sup>B</sup> T SkA, where A and B are the two graph adjacency matrices, and S<sup>0</sup> is a matrix whose entries are equal to 1.

## RESULTS

We have evaluated the effect of different angular diffusion sampling schemes and spatial resolutions on mouse brain connectivity, from the point of view of stability, with reference to a published data set, and with the aim to identify balanced acquisition schemes in terms of cost and accuracy.

First, we have evaluated nine angular sampling schemes from the point of view of their stability, through their condition numbers, as well as their standard deviations (**Figure 1**). The condition numbers define the asymptotic worst-case relative change in output for a relative change in input.

The highest spatial and angular resolution scheme had the smallest condition numbers (1.58) and standard deviation (7.5 × 10−<sup>4</sup> ). The ranking in terms of condition number was followed by the 60- and 45-direction angular sampling schemes, while the standard deviation followed the ranking 100, 45, and 60 (**Table 1**).

We focused the rest of our analyses on the 45- and 60 direction schemes and compared the results against the reference 120-direction scheme, at three different spatial resolution levels throughout.

Downsampled diffusion data sets were used to estimate the total number of fibers per voxel in each set (**Figure 2**). The total fiber count increased from 7.4 <sup>×</sup> <sup>10</sup><sup>6</sup> for 12 directions to 1.07 <sup>×</sup> <sup>10</sup><sup>7</sup> for 45 and 1.13 <sup>×</sup> <sup>10</sup><sup>7</sup> for 60 directions and 1.23 <sup>×</sup> <sup>10</sup><sup>7</sup> for 120 directions. The steepest changes occurred below 45 directions.

Interestingly, while the number of voxels detected to have one fiber direction was dominant for small angular sampling schemes, this number was exceeded by the number of voxels identified to have two fiber directions, if the sampling schemes had more than 45 angular directions and at a spatial resolution of 43µm. The number of voxels with one dyad reached 42% for 45 directions, and 35% for 60 directions. The number of voxels with two dyads increased with increasing angular sampling, reaching 55% for 45 directions and 60% for 60 directions. Thus, these two curves intersected, and the ratio of one- to two-dyad voxels changed after ∼45 directions. However, the two curves did not intersect for the 86- and 172-µm spatial resolution scenarios. The number of voxels with one fiber direction plateaued at ∼55%, and the number of voxels with two fiber directions at ∼38% for 86- and 172-µm spatial resolutions, with modest increases between 45 and 60 angular samples, and <7% contribution from voxels with three directions. These two data sets had very similar behaviors in terms of the number of voxels with one or two dyads.

These effects were found to be region dependent, although we noted a consistent trend in the ratios between one, two, and three dyads across all three resolution samples (**Figure 3**).

We next focused our analysis on the fimbria and fornix because these tracts have been reported to be relevant in neurodegenerative conditions such as Alzheimer's disease. Our qualitative evaluation showed that tract density maps reconstructed from 12 directions did not capture the cortical– cortical connectivity with the same sensitivity as the 45-, 60-, or 120-direction schemes (**Figure 4**). While less striking, the loss of spatial resolution also resulted in a change in the anatomical definition for the projections, and loss of connectivity through smaller regions (e.g., alveus).

The connectivity of the left hippocampus (**Figure 5**) illustrated a striking similarity between the 120- and 60 directions schemes and loss of similarity for lower angular resolution schemes. In particular, cortical projections occupied

FIGURE 4 | The fimbria connectivity mapped as a function of spatial and angular resolution. The connectivity of the left fimbria was reconstructed for protocols with 12, 45, 60, and 120 directions, at the full 43-µm resolution, clearly illustrating limitations of smaller angular sampling protocols at capturing projections though the hippocampus (white arrows) and amygdala (yellow arrows). Less clear were the effects of spatial resolution in the range 43–172µm where SNR and partial volume effects both played a role. However, the projections through the hippocampus covered reduced areas, and projections into the alveus were partially lost in the lower resolution protocol (blue arrows). L, left; R, right.

smaller areas, especially those requiring interhemispheric connections. The loss of spatial resolution resulted in some clusters appearing larger (e.g., in the amygdala), or conversely, some of the connections were lost (e.g., hindbrain, cerebellum).

We based our quantitative error analysis on the dyad dispersion for the first- (**Figure 6A**) and second-order dyads (**Figure 6B**). Our results showed that errors decayed as the number of angular samples increased both for white matter and for gray matter regions, and the errors increased as we relaxed the spatial resolution. This effect toward increased dispersion was particularly important for thin regions such as the fornix in dyad one (**Figure 6A**). An inflection point was noticed in particular for gray mater regions for 45 directions, and the curves tended to converge toward an asymptotic behavior starting with 60 directions. The dispersion values were larger for the second-order dyad (**Figure 6B**), but the trends were consistent with those observed for dyad one, with an asymptotic behavior predominantly observed in gray matter regions for schemes with more than 60 directions.

Probabilistic tractography was used to generate connectivity matrices, based on the number of streamlines connecting selected regions that are likely to play a role in neurodegenerative disease models. In general, the connectivity differences between our reference and those of the 45- or 60-direction data sets were considerably smaller relative to those involving 12 directions (**Figure 7**). The sensitivity to capturing connectivity of smaller, and in particular, cortical gray matter regions was evident in the chord diagrams (**Figure 7**), which appeared sparser for 12 directions relative to 45, 60, and 120 directions. The sparsity was evident for gray matter-to-gray matter connections, in particular, for cortical domains. For small angular resolutions, the connectomes were dominated by wide bands involving myelinated white matter tract regions connecting to the hippocampus or septum. Gray matter-to-gray matter connections appeared more prominent with higher angular

affected more by angular resolution, especially for cortical regions (white arrows), but also in the ability to retrieve cross hemispheric projections (yellow arrows). L, left; R, right.

sampling, in particular for schemes with more than 45 directions, with a noticeable qualitative similarity between 60 and 120 angles.

To quantify the similarity between connectomes, we used non-parametric correlation analysis. The Spearman correlation among the connectivity matrices was significant (p < 0.001) for all comparisons tested.

The highest global correlation overall was found among the 86-µm spatial resolution connectomes acquired with 60 and 120 directions, 0.97, while the correlation between connectomes acquired at the same 86-µm spatial resolution with 45 and 120 directions was 0.92. When compared against the reference highspatial, high-angular resolution protocol (S1, A120), the highest correlations were with (S2, A120) at 0.96, followed by (S1, A60) and (S2, A60) at 0.94, then (S4, A120) at 0.93 (**Figure 8,** left). When comparing the 45-direction protocols against the reference (high-spatial, high-angular resolution) protocol (S1, A120), the highest correlation was obtained with (S2, A45) at 0.91 followed by (S1, A45) at 0.90 (**Figure 8,** right).

In our experiments (S1, A120) was robust to downsampling, achieving a correlation of 0.96 with (S2, A120) and 0.93 with (S4, A120). The angular resolution had a strong effect, with the similarity dropping to 0.7 with (S1, A12), 0.9 with (S1, A45), and 0.94 with (S1, A60). The lowest similarity from our subgraph comparison was between (S2, A120) and (S2, A12) at 0.64,

followed by (S2, A120) with (S1, A12) and (S2, A12) with (S4, A120) at 0.65. Our results suggest that higher-angular resolution schemes are generally more robust to detecting changes in animal models of neurological conditions.

Furthermore, we computed measures of similarity between individual graph vertices. The vertex-wise graph similarity pairwise for these subgraphs, shown in **Figure 9** after thresholding at a 0.1 level, illustrated differences in sparsity. Graphs were less sparse as the numbers of angular samples/directions increased above 45 to 60 and 120 directions. Based on the vertex score, the robustness of some nodes relative to others became evident when undergoing downsampling, first for fimbria, followed by the fornix. The similarity between the hippocampus (Hc) and fimbria (fi) and fornix (fx) was 0.1 and 0.07. For the 45-direction scheme, the similarity between full resolution and 86-µm resolution for fimbria was 0.45, while for

the fx, it was 0.18, and for the hippocampus and septum, it was 0.16 indicating more robustness for larger regions. Hc with fi and fx was 0.21 and 0.11, respectively. The similarity for fi (0.27) and fx (0.24) decreased with downsampling spatial resolution for the 120-direction schemes; however, more regions picked up more similar connections across these two graphs. For example, the hippocampus score was 0.21, and for septum, 0.15, with Hc to fi and fx being 0.17 and 0.12, respectively. These results suggest that while major white matter tracts were robust with respect to changes in spatial and angular resolution, this needs to be balanced against the vulnerability of smaller white matter tracts and cortical domains to such changes, and sparsity of the connectome and graph similarity matrices are important factors to consider.

In conclusion, our results support that angular and spatial resolution need to be balanced with respect to time- and costimposed demands to enable population studies, and parameters for efficient protocols with minimum loss of sensitivity should be recommended, suggesting that an acquisition with 60 angular samples provides a good compromise in terms of minimizing errors relative to a reference high-spatial, highangular resolution protocol. Halving both spatial and angular resolutions yields an eight-time speed factor. When time is a constraint, compromises in terms of spatial resolution may need to be made. Importantly, more efficient acquisitions are amenable to future population studies.

## DISCUSSION

Several studies using tractography-based connectomics have reported abnormal network organization associated with morphometric changes and AD pathologies [26, 27]. Still, to understand the etiology of human neurodegenerative diseases such as AD has proven difficult. This is due to AD's complex nature, multiple pathologies, and various associated comorbidities. In spite of their limitations and simplicity,

mouse models provide tools to examine the effect of singular pathologies, and the interaction of multiple factors in a well-controlled environment and, importantly, from early on.

Given the challenges in phenotyping mouse models of neurological conditions, imaging protocols are required to provide sensitive quantitative biomarkers with good spatial mapping abilities. In a previous study, we have defined a diffusion MRI data reference set with the highest reported resolution for the whole mouse brain [11]. This data set was acquired using 120 angular samples and 43-µm spatial resolution, requiring a scan time of 235 h. Replicating this acquisition for population studies is prohibitive in terms of both time and cost. To address this problem, we must define reduced protocols that ensure sufficient fidelity and compare well with the reference data set, but that can be acquired in a greatly reduced time. This will enable population studies and give insight into vulnerable networks that may provide early biomarkers and enable us to quantify disease progression or response to interventions. We, thus, performed a simulation study, examining subsets of angular samples from the reference set, and the effect of reducing spatial resolution.

We have focused on evaluating regions of interest in Alzheimer's research, from hippocampus, which represents 5% of the mouse brain volume, to fimbria and fornix, which represents only 0.05% of the mouse brain volume [13]. In our simulations, we have extracted nine gradient subsets to evaluate subsequent changes in several metrics. Our metrics evaluated the stability/noise of the diffusion schemes through the condition number; the relative number of detected voxels with one, two, three, and four directions or dyads, globally and on a per-region basis; the extent of projections for the fimbria and hippocampus; the dyad dispersion and connectome similarity globally, and on a per-node basis. Our results corroborated to support that schemes with 45 angular samples started to approximate well the reference connectome and that errors were substantially reduced and started plateauing for 60 angular samples acquired with 43 or 86-µm spatial resolution.

The performance of the 60-direction protocols was very similar to the reference set, both qualitatively and quantitatively, and executing such protocols would lead to substantial reductions in acquisition time (eight times when combined with a downsampled spatial resolution), providing a connectome with 0.94 correlation with the reference set. Further reductions in acquisition times come from compressed sensing [28], which several groups have implemented for mouse MRI [29– 31]. Such advances can help translate diffusion protocols into population studies [32] incorporating multiple biomarkers from morphometry [33], microstructural properties based on diffusion [13] or magnetic susceptibility [34], or network properties [35]. Such integrative studies may better predict changes in behaviors, modeling those observed in humans with neurodegenerative conditions [34].

Optimization studies are important when it comes to assess newly proposed diffusion models, acquisition schemes, various pathologies, and different animal models—to help understand the human brain [36, 37]. In this study, we were limited to a single specimen meta-analysis using extracted gradient subsets, but this approach provided useful information to help

particular, cortical domains. Our subgraph's regions of interest include the cingulate cortex (Cg), insular cortex (Ins), temporal association areas (TeA), entorhinal cortex (Ent), hippocampus (Hc), hypothalamus (Hyp), preoptic telencephalon (LPO), septum (Spt), lateral geniculate nucleus (LGN), fimbria (fi), fornix (fx), mamilothalamic tract (mt), and laterodorsal thalamic nuclei (LD). When comparing graphs for different spatial resolutions, the fi was most similar between 43- and 86-µm resolutions with a score of 0.85, and also relative to 172µm with a score of 0.82, but most other graph nodes were not. Among angular sampling schemes, the fimbria was also robust, with a similarity score between the 12 and 120 directions of 0.5, followed by the fornix, hippocampus, and septum with 0.1. The cortical regions were most likely to be dissimilar when changing spatial or angular sampling schemes. Median similarity scores are shown for each graph comparison.

devise future group-based analyses. We have used the condition number for our acquisition schemes as a measure of noise performance [38], but better schemes may be designed in the future to increase rotational stability. Our recommendations are based on a finite set of metrics for a subgraph selected based on relevance to AD and a single b value acquisition. More regions would need to be included to generalize the recommendations; however, our results point to robustness of major white matter tracts, in contrast to thin, small white matter tracts and cortical–cortical connections, which are particularly vulnerable to reduced acquisition schemes. Moreover, future studies may be improved by the addition of multiple diffusion shells and more complex diffusion models. A reduction by a factor of eight in acquisition time for half the resolution and 60 directions provided an attractive avenue. However, the number of voxels with more than one dyad could not be retrieved to the same extent at lower spatial resolution. Still the similarity of the subgraphs based on anatomical regions known to be involved in pathological aging approached 0.94. Our study benefitted from the use of high-performance computing, which enhances the speed of optimization and quality control studies required before establishing new protocols in both mice and humans [39].

Our methods are applicable to other DWI studies in rodent models, and we expect that the conclusions will remain valid in the same range of spatial resolutions and may change as we approach resolutions used in human brain imaging. Still, our results align with recommendations for human brain studies, where angular sampling schemes with more than 30 directions [40] are recommended for a robust estimation of the diffusion tensor orientations and mean diffusivity. More than 45 directions are required for spherical deconvolution using spherical harmonics of order eight [41, 42]. Acquiring more than 45 directions helps with fitting and avoiding issues with imperfections in the uniformity of the diffusion gradient directions and meet signal-to-noise requirements. Therefore, 40 directions or more are widely used for the human brain [26, 27], and state of the art protocols employ multiple diffusion shells/b values, e.g., n = 3, and with 60 directions per shell [43]. Harmonization efforts [44] are currently under way to establish diffusion imaging guidelines and/or translate among protocols, such as those used for ADNI3 [45].

While the field of mouse imaging is much smaller, and efforts for standardization are not yet widespread, we hope that our study can inform the design of future experiments using statistical connectomics in models of neurological conditions, such as Alzheimer's disease, and that network biomarkers will provide enhanced sensitivity to early and subtle changes arising due to multiple, interacting pathologies.

## DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the article/supplementary material.

## REFERENCES

## AUTHOR CONTRIBUTIONS

RA, CL, GC, and AB designed simulation experiments and wrote code. SR and EC contributed code. GJ is the director of CIVM, oversees resources, and provided input in the initial phases of the project. RO'B contributed insight into the brain networks to be evaluated. RA, CL, and AB wrote the manuscript, which the other authors reviewed and edited.

## FUNDING

This work was supported by the National Institutes of Health through K01 AG041211, R01 AG045422, R56 AG051765, R56 AG057895, RF1 AG057895, and R01 AG066184. CIVM was supported by P41 EB015897 until 2018 and since then by U24CA220245.

## ACKNOWLEDGMENTS

We thank Dr. Tobias Wood, and Jacques Stout for helpful discussions. We thank all CIVM-ers for their efforts to build and maintain this small animal imaging resource and a collaborative learning environment.

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Anderson, Long, Calabrese, Robertson, Johnson, Cofer, O'Brien and Badea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Impact of Attenuation Correction on Quantification Accuracy in Preclinical Whole-Body PET Images

Thomas Wanek, Lara Schöllbauer, Thomas Filip, Severin Mairinger, Michael Sauberer, Matthias Blaickner and Claudia Kuntner\*

Preclinical Molecular Imaging, AIT Austrian Institute of Technology GmbH, Seibersdorf, Austria

Background: Whole-body PET images can be obtained by using the "step-and-shoot" (SaS) method (using multiple bed positions) or continuous bed motion (CBM). As transmission scans are not always feasible, an alternative method where attenuation data can be generated via emission-based attenuation correction (AC) maps is of interest. The aim of this preclinical study was to investigate the influence of the acquisition method and AC on the quantitation accuracy of [18F]FDG-PET.

#### Edited by:

Kris Thielemans, University College London, United Kingdom

#### Reviewed by:

Daniele Panetta, Italian National Research Council, Italy Francesca Leek, University College London, United Kingdom

> \*Correspondence: Claudia Kuntner<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Medical Physics and Imaging, a section of the journal Frontiers in Physics

Received: 13 December 2019 Accepted: 30 March 2020 Published: 24 April 2020

#### Citation:

Wanek T, Schöllbauer L, Filip T, Mairinger S, Sauberer M, Blaickner M and Kuntner C (2020) Impact of Attenuation Correction on Quantification Accuracy in Preclinical Whole-Body PET Images. Front. Phys. 8:123. doi: 10.3389/fphy.2020.00123 Methods: [ <sup>18</sup>F]FDG-PET phantom images were acquired using either SaS or CBM. Transmission scans were recorded for the SaS method using a <sup>57</sup>Co-point source. Emission-based attenuation sinograms were obtained from the images after segmentation and inverse Fourier rebinning. PET images were reconstructed without AC, transmission based (TX-AC) and emission-based (EM-AC) attenuation correction. Moreover, [18F]FDG-PET scans of rats bearing mammary carcinomas acquired using either SaS or CBM were analyzed retrospectively and quantification in tissues was compared.

Results: Phantom recovery coefficients (RC) varied greatly, ranging from 0.49 ± 0.01 to 1.15 ± 0.07, dependent on acquisition method, reconstruction algorithm and AC method. In CBM acquired images, EM-AC improved quantification accuracy when compared to no-AC images in the phantom studies (R<sup>C</sup> 0.79 ± 0.02 vs. 0.49 ± 0.01, respectively) and in tumors of rats (DMBA model: 1.16±0.42 SUV vs. 0.86±0.28 SUV, respectively).

Conclusion: The method of AC has a strong influence on the quantification of [18F]FDG. Our data indicates that EM-AC improves quantification in images obtained by CBM and SaS. However, the obtained values were still underestimated when compared to TX-AC corrected images.

Keywords: positron emission tomography, attenuation correction, quantification, FDG, emission-based attenuation

## INTRODUCTION

Quantitative whole-body examinations are one of the key strengths of preclinical positron emission tomography (PET) and computed tomography (CT) systems. However, due to the restricted axial field of view (FOV) of several centimeters both in clinical and preclinical PET scanners, a whole-body scan in humans or larger animals such as rats or rabbits cannot be recorded in

**106**

one single scan acquisition. Therefore, for obtaining a whole-body scan, two different scanning methods are commonly applied.

In the first method, the "step-and-shoot" (SaS) method, several emission scans are recorded using multiple bed positions to cover the whole body [1]. For this, the subject is positioned on the bed, moved to the first bed position and coincidences are recorded for a predefined time (e.g., 10–15 min). Then the bed is moved to the next position and the procedure is repeated. Typically, the bed positions overlap each other for several cm in order to facilitate subsequent processing of the images. To correct for photon attenuation in the scanned subject, a separate attenuation correction (AC) scan is recorded for each single bed position in singles mode using either a transmission source such as <sup>57</sup>Co (TX-AC) or a CT-based attenuation map [2].  The advantage of this method is that it usually follows established laboratory procedures for emission scans as this type of scanning is routinely performed and it provides a higher slice sensitivity [3]. However, scanning time is increased for each scanned individual which significantly decreases the daily imaging throughput.

An alternative method to the SaS-method is the continuous bed motion (CBM) method. Here, the bed is continuously moved horizontally back and forth through the axial FOV between a specified start- and end-position and coincidences are recorded. The main advantage of this method is that it provides a more uniform sensitivity over the entire image and offers better image quality due to over sampling [4], which often aids in the identification of smaller tumors or affected lymph nodes. Preclinical PET systems have implemented CBM since 2009 [5, 6], whereas the clinical implementation just started more recently [7–10]. The main disadvantage of CBM in preclinical imaging is that it usually does not offer the possibility to perform a transmission scan required for AC. This is of special relevance for stand-alone preclinical PET scanners, which are not combined with a CT and can thus not use the CT image to generate a µ-map for attenuation correction. Consequently, the improved image quality provided by CBM comes at the cost of a decreased quantification accuracy. One way to overcome this issue is the generation of emission-based attenuation correction (EM-AC) factors or maps [11]. Here, image data is first segmented assuming a uniform attenuation in manually delineated regions and then forward projected to generate an attenuation sinogram, which is then used for attenuation correction during image reconstruction [12]. This method is relatively fast, easy to use and implemented in most preclinical PET systems. In addition, EM-AC eliminates the time required for an extra attenuation measurement, resulting in a significant reduction in overall scanning time.

In this study we compared No-AC, TX-AC, and EM-AC and their influence on image quantification by phantom measurements acquired using the SaS and CBM method. We furthermore investigated the biological relevance of our findings by performing a retrospective analysis of a dataset of [18F]FDG-PET scans in rats bearing chemically induced breast cancer tumors. We first evaluated brain and liver uptake in whole-body scans of rats acquired using the SaS method and applied no-AC, TX-AC, or EM-AC during reconstruction of the images. Secondly, we assessed whole-body scans of rats acquired using the CBM method and compared the quantification in tumors in reconstructed images using either filtered back projection (FBP) or ordered subset expectation maximization (OSEM) with or without EM-AC.

## METHODS

## Phantom Study

A cylindrical hollow phantom made from polyethylene with a diameter of 7 cm, a length of 16.5 cm and a wall thickness of 0.2 cm was filled with 36.1 <sup>±</sup> 8.0 MBq aqueous [18F]FDG solution and placed on the scanner bed of a preclinical PET scanner (micro PET Focus 220, Siemens Healthineers, Knoxville, TN, USA), which provides 7.6 cm axial and 22 cm transaxial FOV [13]. After moving the phantom to the center FOV, PET emission scans at 3 consecutive bed positions were acquired for 10 min/bed position with an energy window of 350–750 keV and a coincidence time window of 6 ns. The horizontal bed position was moved between the measurements in axial direction for ∼6.6 cm to cover a total axial FOV of 20.8 cm. A horizontal overlap of 1 cm was applied to the images to facilitate further stitching of the images. Afterwards, a 10-min transmission scan using a rotating <sup>57</sup>Co point source (120–125 keV energy window) was recorded in singles mode for each bed position. CBM measurements were performed by placing the phantom on the scanner bed and a subsequent emission recording for a time of 20 min (350–750 keV energy window, 6 ns timing window). The phantom was moved through the field of view for four times back and forth (8 passes) to cover the same region of interest multiple times during data acquisition spanning an axial FOV of 27.1 cm.

## Animal Studies Chemicals

Radiosynthesis and quality control of [18F]FDG was performed using standard methods [14]. 1-Methyl-1-nitrosourea (MNU) and 1-methyl-1 nitrosourea (DMBA) were obtained by Sigma-Aldrich (Schnelldorf, Germany). For in vivo application, MNU was dissolved in sterile saline solution and acidified to pH 5.0 with acetic acid. DMBA was dissolved in sesame oil.

## Research Animals and Tumor Induction

Female 5–6 weeks old Sprague-Dawley rats (Crl:CD, Charles River, Sulzfeld, Germany) were initially used in the study. Animals were housed in groups in a temperature and humiditycontrolled facility on a 12 h light/ dark cycle and fed a standard laboratory diet. Food and water were available ad libitum and animals were weighed weekly during the experiment. Tumors were generated following published procedures by the administration of the carcinogens MNU [15, 16] or DMBA as follows [17, 18]. MNU was injected intraperitoneally at a dose of 50 mg/kg and a volume of 5 mL/kg body weight (n = 12). If animals did not exhibit tumors within 3 weeks, MNU injection was repeated on day 29 and day 57 after initiation of the study. DMBA was administered by oral gavage at a dose of 20 mg/kg (n = 8) or 50 mg/kg BW (n = 12) at a volume of 5 mL/kg. Animals were observed regularly to determine the development and localization of tumors. All experiments involving laboratory animals were approved by the respective authorities and the study procedures were in full accordance with the European Communities Council Directive of September 22, 2010 (2010/63/EU). All procedures were in compliance with the institutional biosecurity and radiation safety regulations.

#### PET Imaging Procedure

At the time point of PET imaging, mean body weight of rats were 236 ± 33 g. The following standardized imaging setup was applied for all [18F]FDG scans [19–21]. In brief, rats were deprived of food for 18 <sup>±</sup> 4 h prior [18F]FDG administration. Rats had access to drinking water at all times. Body warming was achieved by placing the entire cage on a heating pad kept at 38◦C. Warming was initiated ∼30 min before tracer injection and continued throughout the uptake and image acquisition period. Rats were pre-anesthetized by inhalation anesthesia (1–4% isoflurane in oxygen) in an induction box. Subsequently, 0.1 mL containing 30.0 <sup>±</sup> 12.5 MBq [18F]FDG solution diluted with saline was administered intravenously via tail vein injection under isoflurane anesthesia. Rats were kept anesthetized throughout the whole uptake and imaging period and the isoflurane concentration level was adjusted to obtain a breathing rate of 20–60 breath/min during the PET acquisition, indicating a comparable level of anesthesia.

PET emission data with an energy window of 350–750 keV and a coincidence time window of 6 ns were acquired at 1 h after [ <sup>18</sup>F]FDG injection into rats (<sup>n</sup> <sup>=</sup> 9) using the SaS method for 10 min/bed position with three bed positions to cover the whole body of the animal. The bed was moved from one position to the next by 6.6 cm covering in total an axial FOV of 20.8 cm. Overlap in the resulting images between the positions was 1 cm. Before the emission scan, an attenuation measurement was done with the rotating <sup>57</sup>Co point source for 10 min at each position. Total scan time per animal was 60 min.

In a second cohort of rats (n = 20) a 20 min scan (350–750 keV energy window, 6 ns timing window) was acquired at 1 h after [18F]FDG injection using the CBM tool to cover the whole body of the rats. Thereby, the bed was slowly moved through the FOV for four times back and forth (8 passes) covering an axial FOV of 27.1 cm.

## Image Data Generation and Analysis

As the rat images were analyzed retrospectively, some of the original raw data (list mode files and sinograms) were no longer available. Thus, all emission sinograms used for comparing different AC methods were generated by inverse Fourier rebinning from the available image files, which were originally reconstructed using Fourier rebinning (FORE) followed by filtered back projection (FBP), using the forward projection tool implemented in the image analysis software ASIPro VM 6.0 (Siemens Healthineers). To be consistent, we applied the same procedure to the acquired phantom images. The thus generated emission sinograms were further used for image reconstruction as described below. To validate this approach, we also compared image files reconstructed from the generated emission sinograms with image files from the original emission sinograms (when available).

The generated emission sinograms were reconstructed using FORE followed by FBP algorithm with ramp filter or OSEM at an image zoom of 3.163 and an image matrix of 128 × 128 × 95 (SaS) or 128 × 128 × 351 (CBM) resulting in a voxel size of 0.6 <sup>×</sup> 0.6 <sup>×</sup> 0.796 mm<sup>3</sup> . The standard data correction protocol (normalization, decay correction, and injection decay correction) was applied to the data. Scatter correction was not applied to the emission data as the used reconstruction software does not permit scatter correction of non-AC data to allow for a comparable ratio between the AC and non-AC corrected images.

For calculation of the EM-AC file, the emission calibration and segmentation tool implemented in ASIPro VM 6.0 was used. PET emission images were segmented to obtain the outer border of the phantom or the rat. The area inside the segmented phantom or rat was assumed to be water and assigned an attenuation coefficient of 0.095 cm−<sup>1</sup> (511 keV), the area outside was assumed to be background and assigned an attenuation coefficient of 0 cm−<sup>1</sup> . After segmentation was completed, a 2D sinogram was projected and inverse Fourier rebinning with a span of 47 and a ring difference of 23 was performed. The obtained sinogram was defined as emission-based sinogram (EM-AC sinogram) for attenuation correction. The flow chart of the processed image data, calculated sinograms and reconstructed image files is depicted in **Figure 1**. Images were reconstructed without (No-AC), with transmission-based (TX-AC) and with emissionbased (EM-AC) attenuation correction. Images obtained with the SaS technique were stitched together using the stitching tool implemented in ASIPro VM 6.0.

A calibration factor for converting units of PET images into absolute radioactivity concentration units was generated by imaging a phantom filled with a known concentration of [18F]FDG at identical measurements parameters and reconstructed using FBP including TX-AC. The activity concentration in the phantom was calculated from the activity measured in a dose calibrator (CRC-25R, Capintec Pittsburgh, PA, USA) and the fill volume of the phantom.

Three cylindrical volumes of interest (VOI) with a diameter of 55 mm and 10 mm height were outlined on multiple planes of the PET images from the phantom study using the image analysis software Amide [22]. For evaluation of the effects of different AC methods the recovery coefficients (RC) as the ratio of measured activity concentration to known activity concentration for each AC method were calculated with the following equations:

$$R\_C = \frac{C\_T}{C\_{known}}$$

where C<sup>T</sup> is the radioactivity concentration derived from the target VOI in kBq/cc in the uncorrected image (No-AC), TX-AC, and the EM-AC corrected image, respectively. Cknown is the known radioactivity concentration calculated from the administered radioactivity (kBq) measured in a dose calibrator

and the fill volume of the phantom (mL) under the assumption of a density of water of 1 g∗mL−<sup>1</sup> .

## RESULTS

In the animal studies, predefined ellipsoidal or spherical VOIs were applied for whole brain (12 <sup>×</sup> <sup>20</sup> <sup>×</sup> 8 mm<sup>3</sup> ) and liver (10 mm diameter) whereas the tumor was manually delineated on multiple planes of the PET images. The derived [18F]FDG concentration values were calculated from the mean and max value of the VOI and quantified in terms of standardized uptake value (SUVmean and SUVmax) using the following equation:

$$SUV = \frac{C\_T}{D\_{I\text{inj}}} \times W\_S$$

where C<sup>T</sup> is the mean or maximum radioactivity concentration in the target VOI in kBq/cc, DInj the administered dose in kBq and W<sup>S</sup> the body weight of the animal in gram.

For comparing the effect of the different AC methods on tumor quantification, individual tumor values are derived either from one animal with one or multiple tumors or from animals, which were scanned multiple times.

## Statistics

All values are given as mean values (± standard deviation). The obtained AC-corrected values were compared to No-AC corrected values using a paired t-test in GraphPad Prism 8.3.0.; p < 0.05 was considered statistically significant.

As not all emission sinograms were available at the time of analysis, the emission sinograms used for all AC techniques (phantom and rats, No-AC, EM-AC, and TX-AC) were generated from the original images by applying inverse Fourier rebinning. In order to validate this approach images reconstructed from the generated sinograms were compared to images reconstructed from the original sinograms (phantom and rat SaS). Quantitative analysis of phantom images based on the generated or original sinograms were in good agreement (SUVmean < 1%). In rats, similar differences of liver and brain activity (SUVmean < 1%) between images based on the generated or original sinogram were observed. In contrast, maximum activity concentration (SUVmax) were significantly lower using the inverse Fourier rebinning approach compared to the original sinograms on both, phantom and rat images.

The recovery coefficients (RC) calculated from the set of reconstructed images, applying No-AC, TX-AC or EM-AC of the cylindrical phantom filled with [18F]FDG solution scanned with the SaS method and CBM are summarized in **Table 1**. Moreover, phantom images acquired using the SaS and the CBM method and reconstructed using No-AC, EM-AC, and TX-AC (only SaS method) are shown in **Figures 1B,C**. As shown in the phantom images, axial image uniformity is superior in the phantom images acquired using CBM as compared to phantom images acquired using SaS. In the later, the overlapping bed position are clearly TABLE 1 | Recovery coefficients (RC) for mean radioactivity concentrations of the phantom studies which were acquired either with the SaS method or continuous bed motion (CBM) and the images reconstructed using different method of attenuation correction (AC).

For calculation of the R<sup>C</sup> please refer to the materials and method section. n.a., not assessed.

visible in the images. Applying no attenuation correction during image reconstruction leads to an underestimation of mean radioactivity concentrations in the investigated VOI of 50 or 37% in CBM and SaS acquired emission scans, independent from the applied image reconstruction algorithm (FBP or OSEM). Implementing transmission-based attenuation correction (TX-AC) to the identical images acquired in SaS mode increased the quantification accuracy as assessed by the R<sup>C</sup> value to 1.02 ± 0.08 and 1.07 ± 0.01 for FBP and OSEM reconstructed images, respectively. Emission-based AC showed comparable improvements in quantification accuracy in SAS acquired images (R<sup>C</sup> of 1.15 ± 0.07 and 1.15 ± 0.07) for FBP and OSEM. In phantom images acquired with CBM, EM-AC increased the quantification accuracy in FBP (R<sup>C</sup> of 0.79 ± 0.02) and OSEM (R<sup>C</sup> of 0.80 ± 0.02) reconstructed images compared to No-AC images.

The results from the retrospective analysis using No-AC, EM-AC, or TX-AC for quantification of brain and liver [18F]FDG uptake in rat images acquired with the SaS method (n=9) are shown in **Figures 2A,B**. Rat FDG images are shown in **Figure 2C**. Mean brain uptake (SUVmean) was increased by 40% (p < 0.0001) and 18% (p < 0.0001) when applying the TX-AC and EM-AC method, respectively, compared to No-AC (**Figure 2A**). In the liver VOI the effect of AC was even more pronounced with increases of 60% (<sup>p</sup> <sup>&</sup>lt; 0.0001) and 32% (<sup>p</sup> <sup>=</sup> 0.008) in mean liver uptake (SUVmean) values after TX-AC and EM-AC, respectively (**Figure 2B**). Overall in both analyzed VOIs, [18F]FDG uptake values (SUVmean) were significantly lower in the EM-AC images as compared to the TX-AC images.

Whole-body rat images acquired using the CBM method are shown in **Figure 3B**. The obtained uptake values in tumors, calculated from whole-body rat images acquired using CBM and reconstructed using either No-AC or EM-AC are shown in **Figure 3A**. In parallel to the phantom study, EM-AC generally increased the mean uptake values irrespective of the image reconstruction algorithm used. For example, [18F]FDG SUVmean tumor uptake values in FBP reconstructed images were increased by 33 ± 10% (p = 0.0034) in the DMBA group (n = 14) and by 36 ± 8% (p = 0.001) in the MNU group (n = 6) after EM-AC. When performing OSEM reconstruction, EM-AC led to an increase in SUVmean tumor uptake values by 68% (<sup>p</sup> <sup>&</sup>lt; 0.0001) and 78% (<sup>p</sup> <sup>=</sup> 0.001) in the DMBA and MNU group, respectively. Interestingly, EM-AC had an effect in the same order of magnitude on the SUVmax tumor values (∼38% increase) when using FBP reconstruction, whereas in the OSEM reconstructed images, SUVmax tumor values exhibited an increase of ∼100% after applying EM-AC.

## DISCUSSION

In contrast to human PET studies, where the CBM acquisition method was just recently introduced and is now used for diagnostic [18F]FDG-PET [9, 23], this method is rarely applied in preclinical whole-body imaging. The main reason for this is that in preclinical imaging either the transaxial FOV of the preclinical PET scanner is large enough for a whole-body scan of the scanned subject (e.g., mouse), the exact imaging region is known to the examiner (e.g., in subcutaneous tumor studies) or the quantitative properties of PET imaging are prioritized over data on whole-body distribution, which might be the case in imaging of larger animals such as rats or rabbits. However, CBM offers the opportunity to increase image throughput, as the total measurement time required for a CBM acquisition is significantly lower than the cumulative effort for emission and transmission acquisitions at multiple bed positions. Compared to SaS, imaging time in CBM could be reduced by a factor of two, therefore doubling the putative imaging output per day which consequently effects the study statistics and allows for higher group numbers—factors that are of high importance in oncology studies. Additionally, the anesthesia duration for the scanned subjects can be shortened in CBM, which decreases the overall burden for the laboratory animals under investigation, again an important factor in oncology studies where repeated measurements over study days (e.g., therapy monitoring studies) are required. Moreover, it has been reported and demonstrated in the phantom study that CBM offers the advantage of a more uniform sensitivity over the entire image and therefore results in a better image quality when compared to SaSacquired images.

In this study we investigated alternative approaches for AC to achieve accurate estimates of radiotracer concentration in different regions of interest in images acquired with CBM. Initially, phantom studies using a rat-sized hollow cylinder filled with a known activity concentration of [18F]FDG in aqueous solution were performed. PET emission images were recorded using both acquisition methods, SaS and CBM, and corresponding transmission scans were performed for the SaS images. Furthermore, the originally FBP-reconstructed images were segmented, and attenuation coefficients were manually assigned to the corresponding parts of the image which was subsequently inverse Fourier rebinned into an emission-based attenuation sinogram. This EM-AC sinogram was then used for AC in the reconstruction of the phantom images using FBP and OSEM algorithm.

During data search for "real" rat imaging data to compare phantom data with, it was recognized that not all original raw data were still available for retrospective analysis. Therefore, the possibility to generate emission sinograms out of the original image files by inverse Fourier rebinning was investigated, initially in the phantom dataset, as well as subsequently in the rat images. Comparison of the quantification result of the inverse Fourier rebinned sinograms (and further reconstructed images) with the original image files showed an excellent agreement. Consequently, the generation of the emission sinograms out of the image files can be a feasible approach if the original listmode or sinogram raw data is missing and images need to be reconstructed with a different reconstruction algorithm or parameters.

Quantitative evaluation of the phantom data and comparison of the derived concentration values to the expected values revealed the influence of the acquisition mode (SAS vs. CBM) and attenuation correction on those data. Applying no attenuation correction to the phantom data showed a reduction in quantitative accuracy by −37% in SaS acquired images reconstructed using FBP or OSEM. These values are quite in line with literature data where it was reported that the accuracy of the activity concentration is reduced by −38% in the uncorrected vs. the attenuation corrected image [24]. Moreover, a simulation study using the Moby phantom with increasing diameters from 2.1 cm to 6.4 cm also showed a 30–40% error in soft tissue quantification starting from 4.7 cm diameter, which is roughly the diameter of a rat [25]. The reduction was even more pronounced in the phantom CBM data, were a decrease of −50% in quantitation accuracy was observed. Applying TX-AC to SaS acquired data increased quantification accuracy to acceptable levels of <10% for all reconstruction algorithms. Interestingly, EM-AC overestimated the activity concentration by 15% in SaS acquired images. However, the such derived values are well within the tolerance range for PET measurements keeping in mind that especially FDG-PET scans in rodents are heavily influenced by animal handling of up to a factor of 2 as shown in a recent study [21]. In CBM data, EM-AC greatly improved image quantitation for both reconstruction algorithms, even though these values are still below the expected concentrations or compared to TX-AC data.

As a next step, the effect of transmission-based (TX-AC) vs. emission-based (EM-AC) attenuation correction in SaS acquisitions of whole-body rat PET images were assessed. In brain and liver VOIs the activity concentration without AC and with TX-AC reflected the results of the phantom studies as such, that mean brain and liver [18F]FDG uptake values were increased by 40 and 60%, respectively, after applying TX-AC. Application of EM-AC to the same data increased the SUVmean in brain and liver by 18 and 32%, respectively. This is in contrast to the phantom study were both AC methods achieved a comparable result in terms of quantification accuracy. A possible explanation for this observation might be an incorrect segmentation. As the skull itself was not visible in the FDG rat images, it was not feasible to include bone material into the segmented images and thus leading to an underestimation of brain FDG uptake. A previous study suggests a careful evaluation of EM-AC in applications where a uniform structure cannot be assumed [26].

When using CBM for tumor-bearing rats this study found that the [18F]FDG uptake after EM-AC was increased with respect to uncorrected images by 34% in the tumors VOI's. Similar results were also obtained by other groups in subcutaneous tumor mouse models [27], where they reported an attenuation recovery of ∼13% in tumors. This value is slightly lower as the results from this study, but as mice were used in contrast to rats, a lower attenuation recovery might be expected. D'Ambrosio et al. [28] compared two attenuation correction methods based on CT and segmentation of emission images in phantom, mice and rat images. In line with this study, it was reported that EM-AC leads to lower uptake values (lower ROI counts) as compared to TX-AC. For example, differences between the EM-AC and CT-AC method of <9% for the myocardium and left ventricle VOI in rats were reported. This difference is much lower as the difference found in the liver and brain VOI in this study (∼17%). However, this might be due to the different position of the VOI inside the body and the attenuation effect of the surrounding tissue.

However, even with this underestimation in [18F]FDG organ and tumor uptake, EM-AC did improve tumor identification and tumor delineation in CBM acquired images. Especially small tumors with lower [18F]FDG uptake can be easily overlooked without AC (see **Figure 3B**). This is of special relevance in chemically induced tumor models, where tumors can develop spontaneously, and the exact tumor locations are unknown. More importantly, the EM-AC method could be an important alternative in preclinical PET/MR scanners where no attenuation correction is available.

## CONCLUSION