The algorithm is demonstrated on images produced from transversal cortical histological slices stained for ED1 (microglial cell marker) and GFAP (astrocytic cell marker). The principle of the algorithm is similar to the previous work of Blacher et al. (2001), which employed distance maps to determine the effects of small molecules (Blacher et al., 2001) or enzymes (Chabottaux et al., 2006) on vascularization. In contrast to the works of Blacher et al., we did not attempt segmenting individual glial cells, since their processi form an overlapping mesh. The application, which is presented here, is the measurement of the amount and distribution of glial cells in a histological section encompassing an implanted microelectrode. Following chronic implantation, the brain matter around the electrode forms glial scar spanning about 500 um from the electrode (review in Polikov et al. (2005)). Shortly after implantation, the astroglial and microglial cells undergo activation and hypertrophy and invade the region of primary injury. The distribution of glial cells around a lesion in the brain can be used to measure the extent of reactive gliosis and compare effects of treatments with anitinflammatory agents after such an implantation. In particular, such distributions can be used to study the brain tissue response to chronically implanted silicon microelectrode arrays (Welkenhuysen,

Tissue slices were stained using the following protocol: preincubation - 4h in 10 % normal goat serum, containing 0.1 % Triton-X 100; incubation - rabbit GFAP antibody (1:500, Dakocytomation) + mouse ED1 (1:200, Abcam) overnight at room temperature. Following

*h*(*g*) · *g* (6.7)

*G* = 1 *Ar*

**6. Spatial distribution of labeled cells around a lesion site**

and Tissue Structures. Selected Algorithms in ImageJ

contours:

2011).

**6.1 Imaging protocol**

where *Ar* denotes the are of the ring.

The action of a parametric thresholding operator *Trα* is conditioned on the ratio between the area of the labeled pixels and the total ROI area:

$$\operatorname{Tr}\_{\mathfrak{A}}\left[\mathbf{x}\in\operatorname{ROI}\,\middle|\,\frac{H\_{\operatorname{ROI}}\left(T\_{\min}\right)}{A\_{\operatorname{ROI}}}\geq 1-\mathfrak{a}\right]:\operatorname{g}(\mathbf{x})\to\operatorname{I}(\mathbf{x})\tag{5.4}$$

where *AROI* denotes the ROI area, *Tmin* is the locally-computed threshold level and *H*() the cumulative histogram of the ROI (Prodanov & Feirabend, 2008).

### **5.3 Granulometric filtering**

The algorithm is implemented as an *ImageJ* plugin. Particles of different sizes can be extracted from an image if two images from a granulometric image sequence are subtracted (see Appendix 12). Accordingly, the *granulometric filtering* is defined as:

$$D = I \circ SE[d\_{low}] - I \circ SE[d\_{high}]$$

where ◦ denotes *morphological opening* and *SE* is a structuring element defining the neighborhood around a pixel.

### **5.4 Differential Contrast Enhancement**

The algorithm assumes that one of the images samples only background *Ibackground* while the other samples background and objects of interest *Iobjects*. Further, it is assumed that the histograms of the background and the objects overlap to an extent so that it is not-possible to separate them by thresholding. The contrast of the image (channel) of interest is rescaled with a coefficient assuring that 0 /∈ *Confidence*95[*Ibackground* − *Iobjects*]. This condition is achieved if the original image is contrast-rescaled by a factor

$$k\_b = \frac{E[I\_{obj\_{icbits}}]}{P\_{95}[I\_{background}]} \tag{5.5}$$

where *P*95 denotes the 95*th* percentile of the empirical pixel intensity distribution and *E*[] denotes the expectation. Next, to obtain the objects, the contrast-rescaled image is subtracted from the objects image in order to produce an output image *R*.

$$R = I\_{\text{objects}} - k\_{\text{b}} \cdot I\_{\text{background}} \tag{5.6}$$

### **5.5 Distance map transformations**

Distance map are produced from binary images. The map is an image derived from the original image where every pixel in the background is assigned the shortest possible distance to a foreground pixel given a certain metrics. The most popular distance maps are derived from the city-block (e.g. having diamond equidistant contours), the chessboard (e.g. having square equidistant contours) or the euclidean (e.g. having circular equidistant contours) metrics. In mathematical terms:

$$D(\mathbf{x}) = \min\_{p} \{ \operatorname{dist}(\mathbf{x}, p) \mid \mathbf{g}(p) = 1 \}$$

The action of a parametric thresholding operator *Trα* is conditioned on the ratio between the

where *AROI* denotes the ROI area, *Tmin* is the locally-computed threshold level and *H*() the

The algorithm is implemented as an *ImageJ* plugin. Particles of different sizes can be extracted from an image if two images from a granulometric image sequence are subtracted (see

*D* = *I* ◦ *SE*[*dlow*] − *I* ◦ *SE*[*dhigh*]

where ◦ denotes *morphological opening* and *SE* is a structuring element defining the

The algorithm assumes that one of the images samples only background *Ibackground* while the other samples background and objects of interest *Iobjects*. Further, it is assumed that the histograms of the background and the objects overlap to an extent so that it is not-possible to separate them by thresholding. The contrast of the image (channel) of interest is rescaled with a coefficient assuring that 0 /∈ *Confidence*95[*Ibackground* − *Iobjects*]. This condition is achieved if

*kb* = *E*[*Iobjects*]

where *P*95 denotes the 95*th* percentile of the empirical pixel intensity distribution and *E*[] denotes the expectation. Next, to obtain the objects, the contrast-rescaled image is subtracted

Distance map are produced from binary images. The map is an image derived from the original image where every pixel in the background is assigned the shortest possible distance to a foreground pixel given a certain metrics. The most popular distance maps are derived from the city-block (e.g. having diamond equidistant contours), the chessboard (e.g. having square equidistant contours) or the euclidean (e.g. having circular equidistant contours)

*D*(**x**) = *minp*{*dist*(**x**, *p*) | *g*(*p*) = 1}

≥ 1 − *α*

: *g*(**x**) → 1(**x**) (5.4)

*P*95[*Ibackground*] (5.5)

*R* = *Iobjects* − *kb* · *Ibackground* (5.6)

**x** ∈ *ROI*| *HROI* (*Tmin*)

cumulative histogram of the ROI (Prodanov & Feirabend, 2008).

Appendix 12). Accordingly, the *granulometric filtering* is defined as:

*AROI*

area of the labeled pixels and the total ROI area:

*Trα*

**5.3 Granulometric filtering**

neighborhood around a pixel.

**5.4 Differential Contrast Enhancement**

**5.5 Distance map transformations**

metrics. In mathematical terms:

the original image is contrast-rescaled by a factor

from the objects image in order to produce an output image *R*.

where 1 denotes the foreground label, **x** and **p** denote pixel coordinates, and *g* denotes intensity. From this equation a naive implementation can be derived directly. However, its execution time scales very badly with the image size and is of little practical use. *ImageJ* implements an approximate Euclidean distance map algorithm as part of its EDM plugin. The EDM algorithm is similar to the 8SSEDT in Leymarie & Levine (1992). The algorithm provides a fast approximation of the EDM, with the deviation from a full calculation being between -0.09 and 0.

## **6. Spatial distribution of labeled cells around a lesion site**

Distance distribution of cell structures around an object can be used to demonstrate spatially-distributed effects or interactions. The distance from an arbitrary pixel set (for example a connected ROI representing the objects of interest) can be computed using the (approximate) Euclidean distance map transformation. In such case, the quantity of the effect (e.g. the amount of immuno-reactivity) can be given by the support volume. Since the area of a ring enclosed by two equidistant levels in the distance map increases monotonously with distance, to allow for comparison one needs to normalize the integral intensity by the ring area. Such normalization produces so called *support volume density*, having the same mathematical expression as the mean intensity inside the ring formed between 2 concentric contours:

$$\overline{G} = \frac{1}{A\_r} \sum\_{\mathcal{g}=0}^{255} h(\mathcal{g}) \cdot \mathcal{g} \tag{6.7}$$

where *Ar* denotes the are of the ring.

The algorithm is demonstrated on images produced from transversal cortical histological slices stained for ED1 (microglial cell marker) and GFAP (astrocytic cell marker). The principle of the algorithm is similar to the previous work of Blacher et al. (2001), which employed distance maps to determine the effects of small molecules (Blacher et al., 2001) or enzymes (Chabottaux et al., 2006) on vascularization. In contrast to the works of Blacher et al., we did not attempt segmenting individual glial cells, since their processi form an overlapping mesh.

The application, which is presented here, is the measurement of the amount and distribution of glial cells in a histological section encompassing an implanted microelectrode. Following chronic implantation, the brain matter around the electrode forms glial scar spanning about 500 um from the electrode (review in Polikov et al. (2005)). Shortly after implantation, the astroglial and microglial cells undergo activation and hypertrophy and invade the region of primary injury. The distribution of glial cells around a lesion in the brain can be used to measure the extent of reactive gliosis and compare effects of treatments with anitinflammatory agents after such an implantation. In particular, such distributions can be used to study the brain tissue response to chronically implanted silicon microelectrode arrays (Welkenhuysen, 2011).

## **6.1 Imaging protocol**

Tissue slices were stained using the following protocol: preincubation - 4h in 10 % normal goat serum, containing 0.1 % Triton-X 100; incubation - rabbit GFAP antibody (1:500, Dakocytomation) + mouse ED1 (1:200, Abcam) overnight at room temperature. Following

Fig. 3. Spatial distribution of GFAP immunoreactivity around an electrode insertion track

**7.1 Imaging protocol**

A – ED1-labeled neuronal cell bodies (red) overlaid on GFAP-positive glial cells (green). The electrode insertion track can be recognized as the tissue defect in the center of the image. B – mask of the insertion track segmented as a background object in the center of the image; C – Euclidean distance map of the insertion track. The closest distance to a point on the track border is represented by the intensity value of the pixel. D – concentric equidistant regions overlaid on the GFAP-image. Original image resolution 1.217 pixels/*μm*; scale bar – 100 *μm*.

195 Automated Segmentation and Morphometry of Cell

and Tissue Structures. Selected Algorithms in ImageJ

Micro island cortical cultures were prepared from embryonic day 18 murine embryos. Cultures were stained with antibodies against a marker for dendrites, microtubule-associated protein 2 (MAP-2), and a marker for synaptic vesicles, synapsin I. Cultures were fixed by 4% paraformaldehyde, washed with 0.1M Phosphate buffered saline (PBS) and incubated in 0.1% Triton X-100 followed by 4% fetal calf serum for 20 minutes. After washing with PBS, cultures were incubated for 1 hour at room temperature in a mixture containing 0.1% Triton X-100, mouse monoclonal anti-MAP-2, 1:200 (Boehringer, Alkmaar, The Netherlands) and rabbit polyclonal anti-synapsin I, 1:1000 antibodies diluted in PBS. After washing with PBS, the cells were incubated for 1 hour at room temperature in secondary antibodies conjugated to anti-rabbit-Cy5 or anti-mouse-Alexa546 (Molecular Probes, Oregon, USA). Finally, the slides

were washed in PBS and cover-slipped with Dabco-Mowiol (Prodanov et al., 2006).

Samples were analyzed on a Zeiss 510 Meta confocal microscope (Carl Zeiss, Heidelberg, Germany). A set of high-resolution digital images of different cultures was recorded at a

secondary incubation with antibodies for 4h with a cocktail of anti-mouse Alexa-444 and Alexa-568 (1:500, Molecular Probes) in blocking buffer, the sections were cover-slipped with Vectashield-DAPI (Vector Labs) mounting medium.

Confocal images were acquired on LSM5 Pascal confocal microscope (Carl Zeiss Microimaging GmbH, Germany) using Plan Apochromat 5x/0.16 objective. The images were composed of 2 acquisition channels (having 8-bit dynamic range each) for demonstration of Alexa-444 (green LUT) and Alexa-568 emission (red LUT), respectively. The implant side was centered in the field of view (Fig. 3A). Exposure times were optimized to provide best compromise between cell contrast and saturation fraction. Images were imported in *ImageJ* using the LSM Reader plugin.

## **6.2 Outline of the algorithm**

The following algorithm was used for computation of the binary mask from the ED1 image:

The resulting ring-like images are then overlaid in turn onto the GFAP image (Fig. 3D).

Proposed approach can be used to investigate the factors which contribute to the loss of signal in chronic microelectrode recordings. From the side of the brain this phenomenon can be caused by (i) spatial shift caused by the formation of the glial scar, (ii) neuronal cell death around the implanted probe or (iii) silencing of the surrounding neurons (review in Braeken & Prodanov (2010)). Activation of microglial cells results in a substantial increase in their phagocytic capacity. So-described process could lead to changes of the neuronal activity of the remaining neurons and may lead to atrophy or cell death of the neuronal cell bodies and axons, respectively. This in turn increases the variability of recorded bioelectrical signals and impedes the interpretation of the acquired physiological data. Moreover, the glial scar in the brain has growth-inhibitory properties for the regenerating axons, which could additionally impede the signal readout.

## **7. Morphometry of cultured neurons**

To establish the dose- or time- dependence of a specific pharmacological effect often a large number of different experimental groups are needed. This rapidly increases the number of individual samples to be evaluated. Because of the high variability of the localizations of synapses on dendrites and the clustering of boutons, the stereological assumption of homogeneity of spatial distribution is violated. Therefore, complete dendritic trees are counted. To facilitate counting in cell cultures we have developed a reproducible and robust method for automatic identification and morphometry of synaptic boutons (Prodanov et al., 2006). The method is further illustrated with identification of synaptic boutons marked for synapsin I immuno-fluoresecence from micro-island cultures of neocortical neurons.

secondary incubation with antibodies for 4h with a cocktail of anti-mouse Alexa-444 and Alexa-568 (1:500, Molecular Probes) in blocking buffer, the sections were cover-slipped with

Confocal images were acquired on LSM5 Pascal confocal microscope (Carl Zeiss Microimaging GmbH, Germany) using Plan Apochromat 5x/0.16 objective. The images were composed of 2 acquisition channels (having 8-bit dynamic range each) for demonstration of Alexa-444 (green LUT) and Alexa-568 emission (red LUT), respectively. The implant side was centered in the field of view (Fig. 3A). Exposure times were optimized to provide best compromise between cell contrast and saturation fraction. Images were imported in *ImageJ*

The following algorithm was used for computation of the binary mask from the ED1 image:

7. This mask is then transformed by Euclidean distance mapping and serially thresholded at

Proposed approach can be used to investigate the factors which contribute to the loss of signal in chronic microelectrode recordings. From the side of the brain this phenomenon can be caused by (i) spatial shift caused by the formation of the glial scar, (ii) neuronal cell death around the implanted probe or (iii) silencing of the surrounding neurons (review in Braeken & Prodanov (2010)). Activation of microglial cells results in a substantial increase in their phagocytic capacity. So-described process could lead to changes of the neuronal activity of the remaining neurons and may lead to atrophy or cell death of the neuronal cell bodies and axons, respectively. This in turn increases the variability of recorded bioelectrical signals and impedes the interpretation of the acquired physiological data. Moreover, the glial scar in the brain has growth-inhibitory properties for the regenerating axons, which could additionally

To establish the dose- or time- dependence of a specific pharmacological effect often a large number of different experimental groups are needed. This rapidly increases the number of individual samples to be evaluated. Because of the high variability of the localizations of synapses on dendrites and the clustering of boutons, the stereological assumption of homogeneity of spatial distribution is violated. Therefore, complete dendritic trees are counted. To facilitate counting in cell cultures we have developed a reproducible and robust method for automatic identification and morphometry of synaptic boutons (Prodanov et al., 2006). The method is further illustrated with identification of synaptic boutons marked for

synapsin I immuno-fluoresecence from micro-island cultures of neocortical neurons.

The resulting ring-like images are then overlaid in turn onto the GFAP image (Fig. 3D).

Vectashield-DAPI (Vector Labs) mounting medium.

1. Perform Gaussian filtering, kernel diameter 5 pixels; 2. Perform morphological erosion with SE of d=5 pixels; 3. Perform morphological dilation with SE of d=10 pixels;

5. Do magic wand selection from a point in the lesion site.

4. Do median thresholding (Doyle, 1962) (Fig. 3B).

using the LSM Reader plugin.

**6.2 Outline of the algorithm**

6. Clear the non selected area.

increasing distances.

impede the signal readout.

**7. Morphometry of cultured neurons**

Fig. 3. Spatial distribution of GFAP immunoreactivity around an electrode insertion track

A – ED1-labeled neuronal cell bodies (red) overlaid on GFAP-positive glial cells (green). The electrode insertion track can be recognized as the tissue defect in the center of the image. B – mask of the insertion track segmented as a background object in the center of the image; C – Euclidean distance map of the insertion track. The closest distance to a point on the track border is represented by the intensity value of the pixel. D – concentric equidistant regions overlaid on the GFAP-image. Original image resolution 1.217 pixels/*μm*; scale bar – 100 *μm*.

## **7.1 Imaging protocol**

Micro island cortical cultures were prepared from embryonic day 18 murine embryos. Cultures were stained with antibodies against a marker for dendrites, microtubule-associated protein 2 (MAP-2), and a marker for synaptic vesicles, synapsin I. Cultures were fixed by 4% paraformaldehyde, washed with 0.1M Phosphate buffered saline (PBS) and incubated in 0.1% Triton X-100 followed by 4% fetal calf serum for 20 minutes. After washing with PBS, cultures were incubated for 1 hour at room temperature in a mixture containing 0.1% Triton X-100, mouse monoclonal anti-MAP-2, 1:200 (Boehringer, Alkmaar, The Netherlands) and rabbit polyclonal anti-synapsin I, 1:1000 antibodies diluted in PBS. After washing with PBS, the cells were incubated for 1 hour at room temperature in secondary antibodies conjugated to anti-rabbit-Cy5 or anti-mouse-Alexa546 (Molecular Probes, Oregon, USA). Finally, the slides were washed in PBS and cover-slipped with Dabco-Mowiol (Prodanov et al., 2006).

Samples were analyzed on a Zeiss 510 Meta confocal microscope (Carl Zeiss, Heidelberg, Germany). A set of high-resolution digital images of different cultures was recorded at a

**8. Spatial mapping of tracer-filled axons**

and Tissue Structures. Selected Algorithms in ImageJ

harvested and fixed in 4% paraformaldehyde.

2008).

filtering.

**8.1 Imaging protocol**

transformed according to Eq. 5.6.

8-connected neighborhoods.

**8.2 Outline of the algorithm**

1. Perform DCE (Sec. 5.4).

et al., 2007).

Specific populations of myelinated nerve fibers can be investigated by retrograde tracing from a muscle followed by morphometric assessment of the labeled fibers at different anatomical levels. Transversal sections of so-labeled nerves would then represent spatial maps of the specific muscle representations (Fig. 5). To facilitate such mapping studies we have developed a reproducible approach for image segmentation and morphometry (Prodanov & Feirabend,

197 Automated Segmentation and Morphometry of Cell

Gastrocnemius muscles of adult rats were injected under anesthesia with the retrograde tracer Fluoro-Gold. After a survival period of 3 days, the sciatic nerves and spinal roots were

The samples were sectioned at 14 *μm*, mounted on glass slides and inspected on an Axioplan (Carl Zeiss Microimaging GmmbH, Germany) fluorescence microscopes. The fluorescent signal from the specimens was recorded on 2 channels. Fluoro-Gold signal was recorded using a standard filter set for DAPI (4',6-Diamidino-2-phenylindole) detection (excitation 320 - 370 nm), see Fig. 5/Fluoro-Gold. The signal from the myelin sheaths (comprising autofluorescence) was recorded using a standard filter set for FITC (Fluorescein) detection (excitation 450 - 490 nm), see Fig. 5/FITC. Tracer-positive axonal profiles were isolated from the resulting images by means of Differential Contrast Enhancement and Granulometric

The following procedure was employed to identify the Fluoro-Gold-positive fibers. Due to the excitation characteristics of Fluoro-Gold its signal was absent from the FITC channel (Fig. 5/FITC). Therefore, it was possible to isolate the Fluoro-Gold using the DCE implemented as the Channel Filter plugin. For the optimal differential brightness enhancement a thin rectangle ROI was drawn across the image and the intensities of the pixels were averaged in the direction perpendicular to the larger side of the rectangle and the input images were

3. Identify the scale of interest by the pattern of the peaks in G(d); select *Ilow* = *S* ◦ *Elow* and

5. Construct disjoint grains from the pixels that are above the threshold based on their

6. Enumerate and measure the grains constructed in this way. Produce derivative images

Spatial maps obtained in this way were uploaded to *LabIS* using the SQL Results plugin. The data were imported to Matlab and the spatial distribution of the Fluoro-Gold-positive axons was further studied on demand using custom code (Prodanov & Feirabend, 2008; Prodanov

The algorithm employed in the study can be outlined in the following main steps:

2. Perform granulometry on the resulting image and compute *G(d)*.

where the identification numbers of the positive fibers are visible.

*Ihigh* = *S* ◦ *Ehigh* and subtract the images. 4. Interactively threshold the resulting image.

resolution of 4.45 pixels per *μm*. Images were acquired on 2 channels - *cyan*, comprising anti-synapsin staining and *red*, comprising anti-MAP-2 staining.

## **7.2 Outline of the algorithm**

The algorithm employed in the study can be outlined in the following main steps:

The algorithm was applied to the synapsin channel of every image. The identified boutons were numbered and their areas, equivalent diameters, and planar co-ordinates were automatically measured and recorded. The final measurements were uploaded in the *LabIS* system using the SQL Results plugin (see Sec. 10.3).

Fig. 4. Illustration of the granulometric filtering technique on a real image. A - Co-localization of synapsin I (cyan) and MAP-2 (red); B - co-localization of the detected synaptic boutons (red outline) with the inverted image from A. Scale bar - 5 *μm*.

As the fluorescent patches in the real images were round, granulometry was performed with a family of flat disk-shaped SEs ranging from *d*=1 to *d*=25 (0.2 *μm* - 5.6 *μm*). For the construction of *Ilow* values of either *dlow*=3 or *dlow*=5 were selected depending on the amount of debris in an image; *dhigh*=11 was selected as a parameter for *Ihigh*. Based on the discrimination of dark background, auto-fluorescing cell mass, and synapsin positive grains *three* brightness classes were used during k-means clustering. During the area thresholding, sufficient overlap of the thresholded particles with the actual synapsin grains was typically achieved for *α* = 0.8. The brightest class was selected for the construction of the mask. A typical result of the detection procedure is presented in Fig. 4B.

An advantage of the proposed automated approach is that, apart from the number of synaptic boutons in a light microscopic image, it also allows to be measured morphological characteristics such as area, circularity, and maximal diameter.

## **8. Spatial mapping of tracer-filled axons**

Specific populations of myelinated nerve fibers can be investigated by retrograde tracing from a muscle followed by morphometric assessment of the labeled fibers at different anatomical levels. Transversal sections of so-labeled nerves would then represent spatial maps of the specific muscle representations (Fig. 5). To facilitate such mapping studies we have developed a reproducible approach for image segmentation and morphometry (Prodanov & Feirabend, 2008).

Gastrocnemius muscles of adult rats were injected under anesthesia with the retrograde tracer Fluoro-Gold. After a survival period of 3 days, the sciatic nerves and spinal roots were harvested and fixed in 4% paraformaldehyde.

## **8.1 Imaging protocol**

14 Molecular Imaging

resolution of 4.45 pixels per *μm*. Images were acquired on 2 channels - *cyan*, comprising

1. Perform granulometry of the image and compute its granulometric size density *G(d)*

2. Identify the scale of interest by the pattern of the peaks in *G(d)*; select low bound *Ilow* =

6. Construct disjoint grains from the pixels that are above the threshold based on their

7. Enumerate and measure the grains constructed in this way. Produce derivative images

The algorithm was applied to the synapsin channel of every image. The identified boutons were numbered and their areas, equivalent diameters, and planar co-ordinates were automatically measured and recorded. The final measurements were uploaded in the *LabIS*

5

2 1

6

3

4

The algorithm employed in the study can be outlined in the following main steps:

*S* ◦ *Elow* and high bound images *Ihigh* = *S* ◦ *Ehigh* and subtract the images. 3. Construct the binary "mask" using the k-means clustering segmentation algorithm. 4. Delete irrelevant structures by superimposing the mask on the original image using

5. Threshold the resulting image using the area thresholding (Sec. 5.2).

where the identification numbers of the positive fibers are visible.

Fig. 4. Illustration of the granulometric filtering technique on a real image. A -

Co-localization of synapsin I (cyan) and MAP-2 (red); B - co-localization of the detected synaptic boutons (red outline) with the inverted image from A. Scale bar - 5 *μm*.

As the fluorescent patches in the real images were round, granulometry was performed with a family of flat disk-shaped SEs ranging from *d*=1 to *d*=25 (0.2 *μm* - 5.6 *μm*). For the construction of *Ilow* values of either *dlow*=3 or *dlow*=5 were selected depending on the amount of debris in an image; *dhigh*=11 was selected as a parameter for *Ihigh*. Based on the discrimination of dark background, auto-fluorescing cell mass, and synapsin positive grains *three* brightness classes were used during k-means clustering. During the area thresholding, sufficient overlap of the thresholded particles with the actual synapsin grains was typically achieved for *α* = 0.8. The brightest class was selected for the construction of the mask. A typical result of the detection

An advantage of the proposed automated approach is that, apart from the number of synaptic boutons in a light microscopic image, it also allows to be measured morphological

anti-synapsin staining and *red*, comprising anti-MAP-2 staining.

bit-plane logical AND operation (masking).

system using the SQL Results plugin (see Sec. 10.3).

**A B**

8-connected neighborhoods.

procedure is presented in Fig. 4B.

characteristics such as area, circularity, and maximal diameter.

**7.2 Outline of the algorithm**

(Appendix 12).

The samples were sectioned at 14 *μm*, mounted on glass slides and inspected on an Axioplan (Carl Zeiss Microimaging GmmbH, Germany) fluorescence microscopes. The fluorescent signal from the specimens was recorded on 2 channels. Fluoro-Gold signal was recorded using a standard filter set for DAPI (4',6-Diamidino-2-phenylindole) detection (excitation 320 - 370 nm), see Fig. 5/Fluoro-Gold. The signal from the myelin sheaths (comprising autofluorescence) was recorded using a standard filter set for FITC (Fluorescein) detection (excitation 450 - 490 nm), see Fig. 5/FITC. Tracer-positive axonal profiles were isolated from the resulting images by means of Differential Contrast Enhancement and Granulometric filtering.

The following procedure was employed to identify the Fluoro-Gold-positive fibers. Due to the excitation characteristics of Fluoro-Gold its signal was absent from the FITC channel (Fig. 5/FITC). Therefore, it was possible to isolate the Fluoro-Gold using the DCE implemented as the Channel Filter plugin. For the optimal differential brightness enhancement a thin rectangle ROI was drawn across the image and the intensities of the pixels were averaged in the direction perpendicular to the larger side of the rectangle and the input images were transformed according to Eq. 5.6.

## **8.2 Outline of the algorithm**

The algorithm employed in the study can be outlined in the following main steps:

Spatial maps obtained in this way were uploaded to *LabIS* using the SQL Results plugin. The data were imported to Matlab and the spatial distribution of the Fluoro-Gold-positive axons was further studied on demand using custom code (Prodanov & Feirabend, 2008; Prodanov et al., 2007).

**9. Outlook**

algorithms.

algorithms.

user community.

**10. Management of the information flow**

information system solutions on the research laboratory level.

Presented algorithms can be developed further in two directions.

and Tissue Structures. Selected Algorithms in ImageJ

**Parallelization:** As the sizes of available images increase and the parallel computer architectures become increasingly available a logical step would be the development of parallel implementations. Such parallelizations can be especially useful in zonal and map algorithms where the same elementary operations are repeated multiple times on different pixels. Notably we expect substantial speedups for image convolutions and morphology operations. Parallelization can be implemented either using general purpose semi-automatic parallelization libraries, or optimized at a low level for the specific

199 Automated Segmentation and Morphometry of Cell

**Multidimensionality:** Another direction for future development is the support of multidimensional kernels and structuring elements in zonal operations. Multidimensional imaging data are increasingly used in life sciences but at present they are not well supported by the data model in *ImageJ*, which assumes at best a collection of loosely-coupled 2D images. Such support can be achieved by development of a multidimensional data model in combination with generic zonal image transform

It is expected that the development and support of *ImageJ* will continue in the future as the user and developer base of the program is growing and there are structured institutional efforts to sustain and support this trend. Therefore, both directions will be pursued further in the context of *ImageJ* and the developed plugins and libraries will be made available to the

Until recently, management of the data produced along the life science workflow (Fig. 1) was not perceived as a pressing issue in the academic environments. Traditionally, the scientific publications were considered as the *only* sufficient reports of the experimental findings. However, the scale of current imaging experiments and the volumes of data generated by them would also require persistent data management and collaboration solutions. This is a situation already recognized in brain mapping studies (Bjaalie, 2002). On the second place, sharing of raw data and code gains momentum in Neuroscience. The exchange of raw imaging data between groups offers the opportunity to differently re-analyze previously collected data and encourage new interpretations (Eckersley et al., 2003). While something new for Neurosciences such practices are common in other fields like Physics and to some extend in Bioinformatics, i.e. in Proteomics. With the increase of experimental complexity and the article size restrictions imposed by the scientific publishers, frequently essential experimental details are omitted from the final peer reviewed publications. This eventually impedes the reproduction of the original experiments and could eventually lead to unnecessary reproduction of experiments and waste of time and resources. In contrast, data sharing can reduce the experimental and analytical errors. However, both high-level data archiving and the raw data sharing will have limited utility if they are not backed by

In the following sections we present an integrated information system for image measurements, *LabIS*. The system was publicly demonstrated for the first time at the 6th FENS Forum of European Neuroscicence, although developmental versions existed since

Fig. 5. Gastrocnemius topography in the sciatic and tibial nerves revealed by the Differential Contrast Enhancement algorithm (Prodanov & Feirabend, 2008)

Left pane: **FITC** – showing the myelin sheath auto-fluorescence; FG – showing **Fluoro-Gold** fluorescence and **Enhanced** – showing the subtraction of the other two. Images are composed of several "stitched" complete microscopical fields. Stitching was performed semi-automatically using the the 2D stitching plugin (Preibisch et al., 2009). Fascicle borders are enhanced by dotted lines. **Enhanced** image was generated by the Channel Filter plugin. Right pane: Gastrocnemius axons inside a fascicle of the tibial nerve (top - **Tibial/FG**) and a microscopic field in the sciatic nerve (bottom - **Inset/FG**). The corresponding inset is outlined in the Enhanced image. **Tibial/FG** – the Gastrocnemius fascicle borders are outlined by a dotted line. Scale bars – 100 *μm*.

## **9. Outlook**

16 Molecular Imaging

Fig. 5. Gastrocnemius topography in the sciatic and tibial nerves revealed by the Differential

Left pane: **FITC** – showing the myelin sheath auto-fluorescence; FG – showing **Fluoro-Gold** fluorescence and **Enhanced** – showing the subtraction of the other two. Images are composed of several "stitched" complete microscopical fields. Stitching was performed semi-automatically using the the 2D stitching plugin (Preibisch et al., 2009). Fascicle borders are enhanced by dotted lines. **Enhanced** image was generated by the Channel Filter plugin. Right pane: Gastrocnemius axons inside a fascicle of the tibial nerve (top - **Tibial/FG**) and a microscopic field in the sciatic nerve (bottom - **Inset/FG**). The corresponding inset is outlined in the Enhanced image. **Tibial/FG** – the Gastrocnemius fascicle borders are outlined by a

Contrast Enhancement algorithm (Prodanov & Feirabend, 2008)

dotted line. Scale bars – 100 *μm*.

Presented algorithms can be developed further in two directions.

It is expected that the development and support of *ImageJ* will continue in the future as the user and developer base of the program is growing and there are structured institutional efforts to sustain and support this trend. Therefore, both directions will be pursued further in the context of *ImageJ* and the developed plugins and libraries will be made available to the user community.

## **10. Management of the information flow**

Until recently, management of the data produced along the life science workflow (Fig. 1) was not perceived as a pressing issue in the academic environments. Traditionally, the scientific publications were considered as the *only* sufficient reports of the experimental findings. However, the scale of current imaging experiments and the volumes of data generated by them would also require persistent data management and collaboration solutions. This is a situation already recognized in brain mapping studies (Bjaalie, 2002). On the second place, sharing of raw data and code gains momentum in Neuroscience. The exchange of raw imaging data between groups offers the opportunity to differently re-analyze previously collected data and encourage new interpretations (Eckersley et al., 2003). While something new for Neurosciences such practices are common in other fields like Physics and to some extend in Bioinformatics, i.e. in Proteomics. With the increase of experimental complexity and the article size restrictions imposed by the scientific publishers, frequently essential experimental details are omitted from the final peer reviewed publications. This eventually impedes the reproduction of the original experiments and could eventually lead to unnecessary reproduction of experiments and waste of time and resources. In contrast, data sharing can reduce the experimental and analytical errors. However, both high-level data archiving and the raw data sharing will have limited utility if they are not backed by information system solutions on the research laboratory level.

In the following sections we present an integrated information system for image measurements, *LabIS*. The system was publicly demonstrated for the first time at the 6th FENS Forum of European Neuroscicence, although developmental versions existed since

Fig. 6. *LabIS* user interface and data upload clients

and Tissue Structures. Selected Algorithms in ImageJ

the web or by database queries in an intranet.

of mixed client-server interoperability.

11 http://www.ebi.ac.uk/ontology-lookup/

**10.2 Annotations of measurements**

The information system can be accessed through the web user interface by a web browser. The user interface of the *ImageJ* SQL access plugin for measurement upload and annotation is

201 Automated Segmentation and Morphometry of Cell

context, interactions with *LabIS* are executed either by exchange of structured messages over

**Measurement type annotations** Uploaded measurements can be annotated by ontology entries. Support of 3rd party ontologies is provided using the publicly available Ontology Lookup Service (OLS) registry web site 11. This support is realized on 2 levels: (i) individual measurement types can be annotated with ontology keys, for example using the Unit Ontology; (ii) the complex measurement objects can be annotated using terms of any of the ontologies supported by OLS. The integration with OLS is transparent for the user and is realized using a cascade of client and 3rd party server calls. This is an example

**Spatial annotations** *LabIS* provides also atlas mapping and registration functionality. Major features include the possibility to associate a *Results collection* to an atlas and to map individual measurement entries of the collection to the atlas imaging space. An example is demonstrated in Fig. 7 where some measurements are mapped to a rat coronal histological

overlaid onto a web browser window displaying the Morphometry module.

2004. Previous versions of the system supported data analyzed in publications about: motor endplate distributions in (Prodanov, 2006; Prodanov et al., 2005); synaptic boutons morphometry in (Prodanov, 2006; Prodanov et al., 2006); peripheral axon spatial distributions in (Prodanov, 2006; Prodanov & Feirabend, 2008; Prodanov et al., 2007). Development of *LabIS* has the objectives to automate (i) the process of storage, annotation and querying of image measurements and (ii) to provide means for data sharing with 3rd party applications utilizing measurement data (Prodanov, 2008). A developmental snapshot of the system is available through the website Sourceforge.net at http://labinfsyst.sourceforge.net/.

## **10.1 System realization**

*LabIS* realizes 3-tier architecture with a relational database back-end and an application logic middle tier realizing web-based user interface for reporting and annotation and a web service communication layer. *LabIS* is a distributed Internet and intranet application. It can be accessed (i) through the web user interface by a web browser, (ii) through web-service or (iii) database clients (Fig. 6). *LabIS* is based on commonly-accepted open-source software technologies and open communication and data storage protocols. In order to enforce data organization in a structured manner, *LabIS* realizes a centralized data storage model. *LabIS* is designed and developed in a modular manner and the data model for communication with 3rd party applications is also extendable.

Interaction with users is executed via dynamically-generated web pages. All user-interface modules are organized in a similar manner: users can generate reports, enter data or annotate already present database records. The *Project planning* module is used for management of the records of research projects. The users can perform tasks, such as deployment of new and/or changing the state or the attributes of ongoing projects. Groups of results can be organized in results collections. The *Subject management* module manages the records for experimental animals. The users can perform tasks, such as registration of new subjects/animals, editing of records, introduction of new species etc. The subjects can be assigned to projects and to experimental groups. Dynamic reports can be generated for arbitrary periods. The *Manipulation management* module manages the records for performed manipulations. The users can perform tasks, such as registration and editing of manipulations. Dynamic reports can be generated for arbitrary periods. The *Image Measurements and Morphometry* module manages uploaded measurement records. Uploaded measurements can be associated to a project, an experimental subject, experimental group, sample, result collection, or paired to other measurements. The measurements can be queried by the name of the measured image, by the internal ID or simply browsed. There are possibilities for flexible reporting of the performed measurements. The *Administration* module manages the user roles, the maintenance of the database, and the system configuration. The users can also define custom ontologies.

It should be noted that *LabIS* is not a raw image database. The raw images are let to reside in remote repositories, such as on a local client file system or 3rd party file server, while only references to them are stored centrally. In contrast, the imaging meta data and the produced measurements are stored centrally in the relational database. Such an approach provides a definite advantage for the integration of 3rd party imaging data, such as large scale digital atlases. It also increases the portability of the system since its entire database can be easily copied from one host to another. Most notable characteristics of *LabIS* are the possibilities to annotate and collate measurements and its interoperability with 3rd party software. In this

2004. Previous versions of the system supported data analyzed in publications about: motor endplate distributions in (Prodanov, 2006; Prodanov et al., 2005); synaptic boutons morphometry in (Prodanov, 2006; Prodanov et al., 2006); peripheral axon spatial distributions in (Prodanov, 2006; Prodanov & Feirabend, 2008; Prodanov et al., 2007). Development of *LabIS* has the objectives to automate (i) the process of storage, annotation and querying of image measurements and (ii) to provide means for data sharing with 3rd party applications utilizing measurement data (Prodanov, 2008). A developmental snapshot of the system is available through the website Sourceforge.net at http://labinfsyst.sourceforge.net/.

*LabIS* realizes 3-tier architecture with a relational database back-end and an application logic middle tier realizing web-based user interface for reporting and annotation and a web service communication layer. *LabIS* is a distributed Internet and intranet application. It can be accessed (i) through the web user interface by a web browser, (ii) through web-service or (iii) database clients (Fig. 6). *LabIS* is based on commonly-accepted open-source software technologies and open communication and data storage protocols. In order to enforce data organization in a structured manner, *LabIS* realizes a centralized data storage model. *LabIS* is designed and developed in a modular manner and the data model for communication with

Interaction with users is executed via dynamically-generated web pages. All user-interface modules are organized in a similar manner: users can generate reports, enter data or annotate already present database records. The *Project planning* module is used for management of the records of research projects. The users can perform tasks, such as deployment of new and/or changing the state or the attributes of ongoing projects. Groups of results can be organized in results collections. The *Subject management* module manages the records for experimental animals. The users can perform tasks, such as registration of new subjects/animals, editing of records, introduction of new species etc. The subjects can be assigned to projects and to experimental groups. Dynamic reports can be generated for arbitrary periods. The *Manipulation management* module manages the records for performed manipulations. The users can perform tasks, such as registration and editing of manipulations. Dynamic reports can be generated for arbitrary periods. The *Image Measurements and Morphometry* module manages uploaded measurement records. Uploaded measurements can be associated to a project, an experimental subject, experimental group, sample, result collection, or paired to other measurements. The measurements can be queried by the name of the measured image, by the internal ID or simply browsed. There are possibilities for flexible reporting of the performed measurements. The *Administration* module manages the user roles, the maintenance of the database, and the system configuration. The users can also define custom

It should be noted that *LabIS* is not a raw image database. The raw images are let to reside in remote repositories, such as on a local client file system or 3rd party file server, while only references to them are stored centrally. In contrast, the imaging meta data and the produced measurements are stored centrally in the relational database. Such an approach provides a definite advantage for the integration of 3rd party imaging data, such as large scale digital atlases. It also increases the portability of the system since its entire database can be easily copied from one host to another. Most notable characteristics of *LabIS* are the possibilities to annotate and collate measurements and its interoperability with 3rd party software. In this

**10.1 System realization**

ontologies.

3rd party applications is also extendable.

Fig. 6. *LabIS* user interface and data upload clients

The information system can be accessed through the web user interface by a web browser. The user interface of the *ImageJ* SQL access plugin for measurement upload and annotation is overlaid onto a web browser window displaying the Morphometry module.

context, interactions with *LabIS* are executed either by exchange of structured messages over the web or by database queries in an intranet.

## **10.2 Annotations of measurements**

11 http://www.ebi.ac.uk/ontology-lookup/

*ImageJ* plugins. After the end of the measurement session this object together with a JPEG-encoded thumbnail view of the active image are upload either using the SQL client or using the web service client. Known measurement unit types are associated automatically to terms in 3rd party ontologies, i.e. the Unit Ontology. If a new measurement type is encountered it is also automatically included in the database. Such a new type can be later annotated using the web UI and the ontology terms lookup service. The GUI front-end clients were implemented as a set of plugins: the SQL Results plugin and web-service plugins SOAP Results and JSON Results plugins. The SQL-plugin implements a MySQL client that interacts directly with the database server. It is intended for use in intranet environments. The web-service plugins interact with the Object Server interfaces of *LabIS*

203 Automated Segmentation and Morphometry of Cell

and Tissue Structures. Selected Algorithms in ImageJ

(Fig. 6). This functionality is an example of interoperability on the client side.

hosting services.

**11. Summary**

applications.

analysis of astroglial distribution.

**MatlabR** can directly query *LabIS* via its web-service interface. Since its R2007 version, MatlabR provides client functionality for web services. The generation of client scripts is fully automated and transparent to the end-user by the service discovery mechanisms.

On the application level, advantages of the system are the use of open communication standards and the integration of server and client technologies, which are transparent for the end-user. On the system level, advantages of *LabIS* are the extendable data model, the independence of a particular programming language and the scalability of the component technologies. On the level of exploitation and deployment, advantages of the system are the use of open-source platforms, which are available as standard hosting options in the most web

This chapter presents several techniques for morphometry of microscopic and non-optical images. The main focus of the manuscript is the automation of image processing and measurement steps along the experimental workflow. Main algorithms are implemented as either plugins or macros for the public domain image processing program *ImageJ*. We present several generic processing and segmentation techniques, which can be used in a variety of

Notably, the utility of mathematical morphology was demonstrated in several image filtering and segmentation algorithmic steps. Morphology-based Granulometric filtering was used to facilitate segmentation of globular structures, such as synaptic boutons or cross sections of axons. In the first application, morphological granulometry was used to estimate size of synaptic boutons, which are then segmented by means of granulometric filtering followed by thresholding. Using this approach quantitative effects of different treatments of cell cultures can be measured. In the second application, Differential Contrast Enhancement and granulometry were used to segment images of fluorescent tracer filled axons. An additional application of the morphological image simplification were the preprocessing steps in the

The computation of spatial parametric maps was demonstrated in the context of MRI data sets. Such maps are produced by parametric fitting to time-varying spatial measurements. The utility of the Local Area Thresholding was demonstrated in the context of segmentation of histological images. The Differential Contrast Enhancement algorithm can be used to reduce

atlas. This is achieved by integration with public atlas datasets, for example those available at Brain Maps web site12 (Mikula et al., 2007) .

## **10.3 Interoperability**

Interoperability with 3rd party software is realized both on the client and server sides.

*ImageJ* can directly upload measurements to *LabIS* via specialized plugin clients. In such way, the entire image processing functionality of *ImageJ* can be made available to the end user. The user can perform arbitrary measurements using any type of built-in or customized

Fig. 7. Spatial annotation and atlas registration

Three types of atlas annotations are demonstrated in the figure. A ROI annotation delineating a region in the frontal cortex is displayed in *solid blue*, Nissl stating, source Brain Maps, contributed by Edward Jones, 2007. In *red* are demonstrated provisional annotations produced by mouse clicks on the client side. In *black* are demonstrated measurements already stored on the server. Spatial annotation storage and retrieval can be done using the user interface in the bottom left part of the web page. Spatial navigation in the atlas dataset can be done using the icons in the top left part of the web page.

12 http://brainmaps.org/

*ImageJ* plugins. After the end of the measurement session this object together with a JPEG-encoded thumbnail view of the active image are upload either using the SQL client or using the web service client. Known measurement unit types are associated automatically to terms in 3rd party ontologies, i.e. the Unit Ontology. If a new measurement type is encountered it is also automatically included in the database. Such a new type can be later annotated using the web UI and the ontology terms lookup service. The GUI front-end clients were implemented as a set of plugins: the SQL Results plugin and web-service plugins SOAP Results and JSON Results plugins. The SQL-plugin implements a MySQL client that interacts directly with the database server. It is intended for use in intranet environments. The web-service plugins interact with the Object Server interfaces of *LabIS* (Fig. 6). This functionality is an example of interoperability on the client side.

**MatlabR** can directly query *LabIS* via its web-service interface. Since its R2007 version, MatlabR provides client functionality for web services. The generation of client scripts is fully automated and transparent to the end-user by the service discovery mechanisms.

On the application level, advantages of the system are the use of open communication standards and the integration of server and client technologies, which are transparent for the end-user. On the system level, advantages of *LabIS* are the extendable data model, the independence of a particular programming language and the scalability of the component technologies. On the level of exploitation and deployment, advantages of the system are the use of open-source platforms, which are available as standard hosting options in the most web hosting services.

## **11. Summary**

20 Molecular Imaging

Interoperability with 3rd party software is realized both on the client and server sides.

*ImageJ* can directly upload measurements to *LabIS* via specialized plugin clients. In such way, the entire image processing functionality of *ImageJ* can be made available to the end user. The user can perform arbitrary measurements using any type of built-in or customized

Three types of atlas annotations are demonstrated in the figure. A ROI annotation delineating a region in the frontal cortex is displayed in *solid blue*, Nissl stating, source Brain Maps, contributed by Edward Jones, 2007. In *red* are demonstrated provisional annotations produced by mouse clicks on the client side. In *black* are demonstrated measurements already stored on the server. Spatial annotation storage and retrieval can be done using the user interface in the bottom left part of the web page. Spatial navigation in the atlas dataset can be done using the

at Brain Maps web site12 (Mikula et al., 2007) .

Fig. 7. Spatial annotation and atlas registration

icons in the top left part of the web page.

12 http://brainmaps.org/

**10.3 Interoperability**

atlas. This is achieved by integration with public atlas datasets, for example those available

This chapter presents several techniques for morphometry of microscopic and non-optical images. The main focus of the manuscript is the automation of image processing and measurement steps along the experimental workflow. Main algorithms are implemented as either plugins or macros for the public domain image processing program *ImageJ*. We present several generic processing and segmentation techniques, which can be used in a variety of applications.

Notably, the utility of mathematical morphology was demonstrated in several image filtering and segmentation algorithmic steps. Morphology-based Granulometric filtering was used to facilitate segmentation of globular structures, such as synaptic boutons or cross sections of axons. In the first application, morphological granulometry was used to estimate size of synaptic boutons, which are then segmented by means of granulometric filtering followed by thresholding. Using this approach quantitative effects of different treatments of cell cultures can be measured. In the second application, Differential Contrast Enhancement and granulometry were used to segment images of fluorescent tracer filled axons. An additional application of the morphological image simplification were the preprocessing steps in the analysis of astroglial distribution.

The computation of spatial parametric maps was demonstrated in the context of MRI data sets. Such maps are produced by parametric fitting to time-varying spatial measurements.

The utility of the Local Area Thresholding was demonstrated in the context of segmentation of histological images. The Differential Contrast Enhancement algorithm can be used to reduce

Fig. 8. Prinicipal morphological operations

and Tissue Structures. Selected Algorithms in ImageJ

Fig. 9. Granulometry of a synthetic image

and for negative *d opening* is replaced by *closing*.

{*h*(*g*)}. In particular, for unsigned 8-bit images *g* ∈ [0, 255] and

is calculated as

as a plugins for *ImageJ*.

Granulometric image sequence; from left to right: original image, d=5; d=10, d=15, d=20, d=25

205 Automated Segmentation and Morphometry of Cell

is termed antigranulometry. If the SEs are homothetic, that is *E*[*d*] = *d* · *E*, the granulometry

*d*

where *d* denotes the proportionality parameter of the homothety. By convention *G*0(*S*) = *S*

For greyscale images, the measure of the interaction with SE is the support volume removed

*GSD* (*d*) = *V*[*I* ◦ *dE*] − *V*[*I* ◦ (*d* − 1)*E*]

where *g* is the intensity. The support volume is estimated from the image histogram *H* =

255 ∑ *g*=0

Gray-level morphological operations erosion, dilation, opening and closing are implemented

*h*(*g*) · *g*

*I* ◦ (*d* · *E*) (12.10)

*V*[*I*] (12.11)

*Gd*(*I*) =

after opening. This can be expressed by the granulometric (size) density distribution:

*V*[*I*] =

bleed-through artifacts from a background channel to a signal channel. Presented algorithm was used in the context of image enhancement in fluorescent tracer studies.

The utility of distance transform for analysis of spatial distribution of objects was demonstrated in the context of estimation of radial distributions of the glial fraction of the neuropil.

In addition, we also present an information system, which is capable of managing the measurements uploaded from *ImageJ*. The system provides a structured information context for querying and retrieval of primary morphometric information, which goes beyond acquisition metadata. In such way, the increase of the information complexity along the experimental workflow is supported. The system is a step towards providing the link between the raw image, the performed measurements and their meaning in the context of the experiment.

## **12. Appendix**

## **A. Mathematical morphology**

Mathematical morphology is a branch of mathematics applicable to image processing. Morphology operators are a class of zonal operations where the pixels of a certain neighborhood (Structure Element) are combined to produce a single output value. Principal operations in mathematical morphology are *erosion*, *dilation*, *opening* and *closing* (Matheron, 1975; Serra, 1982).

Grey level images can be represented by 3D geometric bodies (called umbras) where the elevation conventionally signifies intensity. Morphology operations of gray level images can be constructed from ordering and ranking of the pixels. If *S*(*x*) is the umbra of the image S and *E*(*x*) is a *Structuring Elements* (SEs) set, *erosion* (�) and *dilation* (⊕) are defined by:

$$S \ominus E = \left\{ \min\_{\mathbf{b} \in \hat{E}} \left[ \mathcal{S} \left( \mathbf{x} + \mathbf{b} \right) + \hat{E} \left( \mathbf{b} \right) \right] \right\} \tag{12.8}$$

$$S \oplus E = \left\{ \max\_{\mathbf{b} \in \hat{E}} \left[ \mathbf{S} \left( \mathbf{x} - \mathbf{b} \right) - \hat{E} \left( \mathbf{b} \right) \right] \right\} \tag{12.9}$$

where *E*ˆ is the complement of the structure element and **x** or **b** denote vector coordinates.

*Opening* is defined as *I* ◦ *E* = (*I* � *E*) ⊕ *E* and *closing* as *I* • *E* = (*I* � *E*) ⊕ *E*.

An example of the four major morphological operations is given in Fig. 8.

As seen from their definition, erosion and closing increase the number of dark pixels and decrease image brightness, while dilation and opening do the opposite.

Sizes of different objects present in an image can be collectively studied by the operation of granulometry, introduced by Matheron (1975).

In a way similar to sieving grains, pixels comprising an image are "sieved" according to their connectivity imposed by a certain SE. Granulometry is formally defined as a set of openings by a family of SEs, characterized by certain scale parameter *d*. In the case of closings, the set

bleed-through artifacts from a background channel to a signal channel. Presented algorithm

The utility of distance transform for analysis of spatial distribution of objects was demonstrated in the context of estimation of radial distributions of the glial fraction of the

In addition, we also present an information system, which is capable of managing the measurements uploaded from *ImageJ*. The system provides a structured information context for querying and retrieval of primary morphometric information, which goes beyond acquisition metadata. In such way, the increase of the information complexity along the experimental workflow is supported. The system is a step towards providing the link between the raw image, the performed measurements and their meaning in the context of

Mathematical morphology is a branch of mathematics applicable to image processing. Morphology operators are a class of zonal operations where the pixels of a certain neighborhood (Structure Element) are combined to produce a single output value. Principal operations in mathematical morphology are *erosion*, *dilation*, *opening* and *closing* (Matheron,

Grey level images can be represented by 3D geometric bodies (called umbras) where the elevation conventionally signifies intensity. Morphology operations of gray level images can be constructed from ordering and ranking of the pixels. If *S*(*x*) is the umbra of the image S

*S* (**x** + **b**) + *E*ˆ (**b**)

*S* (**x** − **b**) − *E*ˆ (**b**)

>

(12.8)

(12.9)

and *E*(*x*) is a *Structuring Elements* (SEs) set, *erosion* (�) and *dilation* (⊕) are defined by:

where *E*ˆ is the complement of the structure element and **x** or **b** denote vector coordinates.

As seen from their definition, erosion and closing increase the number of dark pixels and

Sizes of different objects present in an image can be collectively studied by the operation of

In a way similar to sieving grains, pixels comprising an image are "sieved" according to their connectivity imposed by a certain SE. Granulometry is formally defined as a set of openings by a family of SEs, characterized by certain scale parameter *d*. In the case of closings, the set

*S* � *E* =

*S* ⊕ *E* =

 min *b*∈*E*ˆ

 max *b*∈*E*ˆ

*Opening* is defined as *I* ◦ *E* = (*I* � *E*) ⊕ *E* and *closing* as *I* • *E* = (*I* � *E*) ⊕ *E*. An example of the four major morphological operations is given in Fig. 8.

decrease image brightness, while dilation and opening do the opposite.

granulometry, introduced by Matheron (1975).

was used in the context of image enhancement in fluorescent tracer studies.

neuropil.

the experiment.

**12. Appendix**

1975; Serra, 1982).

**A. Mathematical morphology**

Fig. 8. Prinicipal morphological operations

Fig. 9. Granulometry of a synthetic image

Granulometric image sequence; from left to right: original image, d=5; d=10, d=15, d=20, d=25

is termed antigranulometry. If the SEs are homothetic, that is *E*[*d*] = *d* · *E*, the granulometry is calculated as

$$G\_d(I) = \bigcup\_d I \circ (d \cdot E) \tag{12.10}$$

where *d* denotes the proportionality parameter of the homothety. By convention *G*0(*S*) = *S* and for negative *d opening* is replaced by *closing*.

For greyscale images, the measure of the interaction with SE is the support volume removed after opening. This can be expressed by the granulometric (size) density distribution:

$$GSD\left(d\right) = \frac{V\left[I \diamond dE\right] - V\left[I \diamond (d-1)E\right]}{V\left[I\right]}\tag{12.11}$$

where *g* is the intensity. The support volume is estimated from the image histogram *H* = {*h*(*g*)}. In particular, for unsigned 8-bit images *g* ∈ [0, 255] and

$$V[I] = \sum\_{g=0}^{255} h(g) \cdot g$$

Gray-level morphological operations erosion, dilation, opening and closing are implemented as a plugins for *ImageJ*.

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## **B. Resources**

```
Grayscale Morphology http://rsbweb.nih.gov/ij/plugins/gray-morphology.html
Granulometric filtering http://rsbweb.nih.gov/ij/plugins/gran-filter.html
Color Histogram http://rsbweb.nih.gov/ij/plugins/color-histogram.html
MRI Processor http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:mri_processor:start
DCE http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:differential_contrast_enhancement:start
```
Table 2. Lsit of Plugins

## **13. References**

URL: http://dx.doi.org/10.1038/nrn790

URL: http://dx.doi.org/10.1007/s00018-010-0440-5

Chabottaux, V., Sounni, N. E., Pennington, C., English, W. R., van den Brûle, F., Blacher, S., Gilles, C., Munaut, C., Maquoi, E., Lopez-Otin, C., Murphy, G., Edwards, D. R., Foidart, J.-M. & Noël, A. (2006). Membrane-type 4 matrix metalloproteinase promotes breast cancer growth and metastases., *Cancer Res* 66(10): 5165–5172.

URL: http://dx.doi.org/10.1158/0008-5472.CAN-05-3012

DCE http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:differential\_contrast\_enhancement:start

Abramoff, M., Magalhaes, P. & Ram, S. (2004). Image processing with ImageJ, *Biophotonics*

Ashburner, J., Barnes, G., Chen, C.-C., Daunizeau, J., Flandin, G., Friston, K., Gitelman, D.,

Bjaalie, J. G. (2002). Opinion: Localization in the brain: new solutions emerging., *Nat Rev*

Blacher, S., Devy, L., Burbridge, M. F., Roland, G., Tucker, G., Noël, A. & Foidart, J. M. (2001).

Braeken, D. & Prodanov, D. (2010). *New Developments in Biomedical Engineering*, In-Teh,

Chabottaux, V., Sounni, N. E., Pennington, C., English, W. R., van den Brûle, F., Blacher,

Denk, W., Strickler, J. H. & Webb, W. W. (1990). Two-photon laser scanning fluorescence

Dougherty, P. (2005). Extensions of DAMAS and benefits and limitations of deconvolution

Doyle, W. (1962). Operation useful for similarity-invariant pattern recognition, *J. ACM* 9: 259

Eckersley, P., Egan, G. F., Amari, S., Beltrame, F., Bennett, R., Bjaalie, J. G., Dalkara, T.,

in beamforming, 11th AIAA/CEAS Aeroacoustics Conference, pp. Dougherty, R.P., "Extensions of DAMAS and benefits and limitations of deconvolution in

Schutter, E. D., Gonzalez, C., Grillner, S., Herz, A., Hoffmann, K. P., Jaaskelainen, I. P., Koslow, S. H., Lee, S.-Y., Matthiessen, L., Miller, P. L., da Silva, F. M., Novak, M., Ravindranath, V., Ritz, R., Ruotsalainen, U., Subramaniam, S., Toga, A. W., Usui, S., van Pelt, J., Verschure, P., Willshaw, D., Wrobel, A., Tang, Y. & on Neuroinformatics,

probes for recording and stimulation of excitable cells, pp. 311 – 339. Canaria, C. A. & Lansford, R. (2010). Advanced optical imaging in living embryos., *Cell Mol*

URL: http://dx.doi.org/10.1007/s00018-010-0440-5

Collins, T. J. (2007). ImageJ for microscopy., *Biotechniques* 43(1 Suppl): 25–30.

Kiebel, S., Kilner, J., Litvak, V., Moran, R., Penny, W., Stephan, K., Gitelman, D., Henson, R., Hutton, C., Glauche, V., Mattout, J. & Phillips, C. (2010). *SMP8 Manual*,

Improved quantification of angiogenesis in the rat aortic ring assay., *Angiogenesis*

Vukovar, chapter New trends and challenges in the development of microfabricated

S., Gilles, C., Munaut, C., Maquoi, E., Lopez-Otin, C., Murphy, G., Edwards, D. R., Foidart, J.-M. & Noël, A. (2006). Membrane-type 4 matrix metalloproteinase promotes breast cancer growth and metastases., *Cancer Res* 66(10): 5165–5172. URL: http://dx.doi.org/10.1158/0008-5472.CAN-05-3012 Ciliberti, C. (2009). *MRI-evaluation of implantable neural probes: Software tools and data analysis*,

Grayscale Morphology http://rsbweb.nih.gov/ij/plugins/gray-morphology.html Granulometric filtering http://rsbweb.nih.gov/ij/plugins/gran-filter.html Color Histogram http://rsbweb.nih.gov/ij/plugins/color-histogram.html

MRI Processor http://imagejdocu.tudor.lu/doku.php?id=plugin:filter:mri\_processor:start

**B. Resources**

Table 2. Lsit of Plugins

*International* 11(7): 36 – 42.

*Neurosci* 3(4): 322–325.

*Life Sci* 67(20): 3489–3497.

Master's thesis, University of Calabria.

microscopy., *Science* 248(4951): 73–76.

beamforming", 2961.

– 267.

4(2): 133–142.

Functional Imaging Laboratory.

URL: http://dx.doi.org/10.1038/nrn790

**13. References**

	- URL: http://rsbweb.nih.gov/ij/docs/guide/index.html

URL: http://ric.uthscsa.edu/mango/index.html

URL: http://dx.doi.org/10.1093/bioinformatics/btr390

Marcsisin, E. J. S., Uttero, C. M., Miljkovi´c, M. & Diem, M. (2010). Infrared microspectroscopy of live cells in aqueous media., *Analyst* 135(12): 3227–3232.

URL: http://dx.doi.org/10.1039/c0an00548g

URL: http://dx.doi.org/10.1016/j.neuroimage.2006.11.053

URL: http://dx.doi.org/10.1093/bioinformatics/btn346

URL: http://dx.doi.org/10.1016/j.jneumeth.2005.08.015

Preibisch, S., Saalfeld, S. & Tomancak, P. (2009). Globally optimal stitching of tiled 3D microscopic image acquisitions., *Bioinformatics* 25(11): 1463–1465.

**Part 2**

**Specific Applications with Clinical Examples**

URL: http://dx.doi.org/10.1016/j.brainres.2008.07.049

URL: http://dx.doi.org/10.1002/mus.20378

URL: http://dx.doi.org/10.1016/j.neuroimage.2004.07.051

## **Part 2**

## **Specific Applications with Clinical Examples**

26 Molecular Imaging

208 Molecular Imaging

Prodanov, D. (2006). *Morphometric analysis of the rat lower limb nerves. Anatomical data forneural prosthesis design*, PhD thesis, Twente University, Enschede, The Netherlands. Prodanov, D. (2008). Bidirectional communication of imaging data over the internet using

Prodanov, D. & Feirabend, H. K. P. (2008). Automated characterization of nerve fibers

Prodanov, D., Nagelkerke, N. & Marani, E. (2007). Spatial clustering analysis

Prodanov, D., Roussel, S., Trekker, J., Dresselaers, T. & Himmelreich, U. (2010). ImageJ in

Prodanov, D., Thil, M.-A., Marani, E., Delbeke, J. & Holsheimer, J. (2005). Three-dimensional

Schindelin, J. (2008). Fiji is just ImageJ – batteries included, *in* A. Jahnen & C. Moll (eds), *Proceedings of the 2nd ImageJ User and Developer Conference*, Luxembourg. Serra, J. (1982). *Image Analysis and Mathematical Morphology*, Academic Press Ltd, London. Sezgin, S. & Sankur, B. (2004). Survey over image thresholding techniques and quantitative

Smith, S., Jenkinson, M., Woolrich, W., Beckmann, C., Behrens, T., Johansen-Berg, H.,

URL: http://dx.doi.org/10.1016/j.neuroimage.2004.07.051 van Meer, G., Stelzer, E. H., van Resandt, R. W. W. & Simons, K. (1987). Sorting

Welkenhuysen, M. (2011). *Electrical brain stimulation and microrecording: Research in animal*

White, J. G., Amos, W. B. & Fordham, M. (1987). An evaluation of confocal versus

Bannister, P., Luca, M. D., Drobnjak, I., Flitney, D., Niazy, R., Saunders, J., Vickers, J., Zhang, Y., Stefano, N. D., Brady, J. M. & Matthews, P. (2004). Advances in functional and structural MR image analysis and implementation as FSL., *Neuroimage* 23 Suppl

of sphingolipids in epithelial (madin-darby canine kidney) cells., *J Cell Biol*

*models of psychiatric disorders and in vivo evaluation of a neural probe*, PhD thesis, Catholic

conventional imaging of biological structures by fluorescence light microscopy., *J Cell*

URL: http://dx.doi.org/10.1016/j.brainres.2008.07.049 Prodanov, D., Heeroma, J. H. & Marani, E. (2006). Automatic morphometry of synaptic

*Conference*, pp. 76 – 84.

*Methods* 151 (2): 168 – 177.

distribution., *J Neurosci Methods* 160(1): 93–108.

*ImageJ User and Developer Conference*, pp. 20 – 27.

URL: http://dx.doi.org/10.1002/mus.20378

performance evaluation, *J. Electron. Imaging* 13(1): 146–165.

1233: 35–50.

32(3): 292–302.

1: S208–S219.

105(4): 1623–1635.

*Biol* 105(1): 41–48.

university of Leuven, KU Leuven, Leuven.

ImageJ, *in* A. Jahnen & C. Moll (eds), *Proceedings of the 2nd ImageJ User and Developer*

labeled fluorescently: determination of size, class and spatial distribution., *Brain Res*

boutons of cultured cells using granulometric analysis of digital images, *J. Neurosci.*

in neuroanatomy: applications of different approaches to motor nerve fiber

biomedical imaging applications, *in* A. Jahnen & C. Moll (eds), *Proceedings of the 3rd*

topography of the motor endplates of the rat gastrocnemius muscle., *Muscle Nerve*

**9**

*1Switzerland 2France 3,4Iran 5USA*

**Molecular Imaging of Stem Cells:**

*1Department of Neurosurgery, University of Lausanne, Lausanne, 2Department of Neurosurgery, University of Paris 7, Paris, 3Craniomaxillofacial Research Center, Shariati Hospital,*

*4Dental Section, Farzan Clinical Research Institute, Tehran, 5Department of Neurology, Baylor College of Medicine, Houston,*

Nora Sandu1,2, Fatemeh Momen-Heravi3, Pooyan Sadr-Eshkevari4,

Nowadays, new imaging modalities such as molecular imaging have enabled both practitioners and researchers to visualize, trace and measure biological processes at the level of cells or even single molecules. Instruments of nuclear medicine, like Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and gamma scintigraphy have largely contributed to the evolving and promising era of personalized medicine. Such recently introduced molecular imaging techniques take advantage of an additional potential for broad application in imaging molecular or cellular events in vivo: gene/protein function and regulation, signal transduction, transcriptional regulation and characterization of transgenic animals (Jacobs et al., 2003). Most importantly, molecular imaging will render possible the identification of potential molecular therapeutic targets, in the development of new treatment strategies, and in their successful implementation into clinical application (Jacobs et al., 2003). As a description, molecular imaging is a process of getting signals originating from live organisms after the administration of specific agents that target specific markers found at tissue or cellular level. Such nuclear imaging is the most well-known

instrument of all molecular imaging methods and has been set as the gold standard.

molecular imaging techniques into experimental research and clinical practice.

In this chapter, the authors will discuss PET and other more recent molecular imaging techniques such as near infrared fluorescence (NIRF). Moreover, we will put emphasis on the application of molecular imaging in visualization of stem cell transplantation as a ground breaking treatment modality for neurodegenerative and other neurological diseases. The authors stand among the very first who has implemented recently introduced

**1. Introduction**

**A New Area for Neuroscience**

Ali Arvantaj5 and Bernhard Schaller2

*Tehran University of Medical Sciences*

## **Molecular Imaging of Stem Cells: A New Area for Neuroscience**

Nora Sandu1,2, Fatemeh Momen-Heravi3, Pooyan Sadr-Eshkevari4, Ali Arvantaj5 and Bernhard Schaller2

*1Department of Neurosurgery, University of Lausanne, Lausanne, 2Department of Neurosurgery, University of Paris 7, Paris, 3Craniomaxillofacial Research Center, Shariati Hospital, Tehran University of Medical Sciences 4Dental Section, Farzan Clinical Research Institute, Tehran, 5Department of Neurology, Baylor College of Medicine, Houston, 1Switzerland 2France 3,4Iran 5USA*

## **1. Introduction**

Nowadays, new imaging modalities such as molecular imaging have enabled both practitioners and researchers to visualize, trace and measure biological processes at the level of cells or even single molecules. Instruments of nuclear medicine, like Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and gamma scintigraphy have largely contributed to the evolving and promising era of personalized medicine. Such recently introduced molecular imaging techniques take advantage of an additional potential for broad application in imaging molecular or cellular events in vivo: gene/protein function and regulation, signal transduction, transcriptional regulation and characterization of transgenic animals (Jacobs et al., 2003). Most importantly, molecular imaging will render possible the identification of potential molecular therapeutic targets, in the development of new treatment strategies, and in their successful implementation into clinical application (Jacobs et al., 2003). As a description, molecular imaging is a process of getting signals originating from live organisms after the administration of specific agents that target specific markers found at tissue or cellular level. Such nuclear imaging is the most well-known instrument of all molecular imaging methods and has been set as the gold standard.

In this chapter, the authors will discuss PET and other more recent molecular imaging techniques such as near infrared fluorescence (NIRF). Moreover, we will put emphasis on the application of molecular imaging in visualization of stem cell transplantation as a ground breaking treatment modality for neurodegenerative and other neurological diseases. The authors stand among the very first who has implemented recently introduced molecular imaging techniques into experimental research and clinical practice.

Molecular Imaging of Stem Cells: A New Area for Neuroscience 213

different tissue functions, especially in neoplasms (Cornelius et al., 2011). The main substances for radio-labeling are fluorine-18 (F), gadolinium-68 (Ga) and Carbon-11 (C). The advantages that they confer upon other positron emitting isotopes such as O, N and C are their longer half-life time which results in superior tissue distribution and consequently a dynamic imaging of higher accuracy (de Langen et al., 2008, Keiding et al., 2010, Floeth et

Among metabolic substrates, F-fluorodeoxyglucose is the most renowned molecular tracer in oncology. It has been shown that F-FDG uptake may increase in neoplasmic cells which are believed to be indicative of an increased glycolysis and a subsided respiratory rate of tumoral tissues (Di Chiro et al., 1987). It has been demonstrated that amino acid synthesis is up regulated in tumor cells in comparison to normal tissues. Until now, F-Tyrosine and C-Methionine uptakes are studied at different grades of oncologic tumors such as

C-colin, another marker which represents phospholipid synthesis, is hiked in malignant tumors. It has been shown that the metabolism and proliferation of C-colin are strongly correlated with breast and prostate cancers, indicating high uptake as a measurement of cellular proliferation (Cornelius et al., 2011). Other markers, with a better theoretical profile, have also been introduced in neuroscience but are mostly used only in a limited number of experiments, so that no knowledge is about their potential for imaging stem cell

NIRF confers similar capability for tracing of imaging agents as PET. NIRF dyes are chemically conjugated on targeting molecule such as antibodies, carbohydrates and peptides. After administration into tissues, the dye molecules are excited by the radiation of

Considering radioactive decay, a photon of lower energy which has a higher wavelength is emitted and the fluorescent dye relaxes back to its basic state, ready to repeat the same process. Each fluorescent dye absorb excitation light and afterward, just in a nanosecond, produce fluorescent light. Accordingly, each fluorescent dye molecule provides about 100,000,000 photons per second for collection and image formation. As a consequence, lowenergy fluorescent photons are slightly absorbed and maximally scattered, reduced by the tissues between the targeted tissue and the tissue surface before being collected by detectors in a few milliseconds. In contrast with radionuclides, a fluorescent dye has no physical halflife. Moreover, this methodology is not clinically approved yet and no devices is available for human imaging with NIRF contrast agents, and NIRF contrast imaging agents do not posses approval for human study as well. Interestingly, some investigators utilized NIRF imaging for human applications such as functional lymphatic imaging, sentinel lymph node mapping, activated probes, and promising advances in 3D imaging (Sevick-Muraca EM, 2012).

Many surgical techniques are mainly based on histological grading of the tumor. Molecular imaging enables surgeons to have the gross picture of these tumors. Pre-surgical molecular

al., 2011, Henze et al., 2005).

transplantation.

meningimoma (Cremerius et al., 1997).

**4. Near-InfRared Fluorescence (NIRF) imaging**

tissue penetrating NIR excitation light (750-900nm).

**5. Potential clinical applications of molecular imaging**

## **2. Cell therapy**

Cell therapy, as a promising method using live components of human tissue for treatment of neurological diseases, is one of the mainstays of the wide field of regenerative medicine.. Among stem cell sources available up to date, mesenchymal stem cells (MSCs) have generated a great deal of interest because of their potency in terms of regeneration and tissue engineering (Wislet-Gendebien et al., 2012).

Neurodegenerative disease is a term which encompasses a wide range of acute and chronic neurological diseases such as Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis (ALS), Alzheimer's disease, multiple sclerosis (MS), stroke, and spinal cord injury. Because of the fact that central nervous system reveals limited capacity of regeneration and tissue replacement, cell therapy and tissue regeneration of injured or degenerated tissue, as a potential new therapeutic modality, draws lots of attention (Wislet-Gendebien et al., 2012). Stem cells have the potential to differentiate into many different cell types. Recent research has concerned an exciting opportunity to apply MSCs especially in the management of neurodegenerative diseases, because of their potential to differentiate into neurons (Lee et al., 2010).

It has been shown that the injection of MSCs could be profitable in the treatment of neurodegenerative diseases such as Parkinson's disease (Levy et al.), Spinal Stroke and cerebral ischemia (Sieber-Blum, 2010), Alzheimer's disease (Lee et al., 2010), Multiple Sclerosis (Grigoriadis et al., 2011), Huntington's Disease (Snyder et al., 2010). The advantages of using stem cells for regenerative medicine are not only the eases of harvesting from bone marrow or other donor sites but also the minimal risk of tissue rejection because of the application of autologous cells.

Interestingly, recent data suggest that intravenously injected MSCs could migrate into the brain and pass blood-brain barrier (BBB), which is basically formed by brain microvascular endothelial cells (BMECs), and restrict the passage of immune cells and different molecules into the brain. The exact mechanism of this phenomenon, whether it is based on Integrinmediated adhesion or is matrix metalloproteinase dependent extracellular matrix degradation, is yet not clear (Krampera et al., 2007, Matsushita et al., 2011).

One of the main clinical problems of stem cell transplantation is the difficulty of detection, localization, and examination of the stem cells in vivo at both cellular and molecular levels (Sandu et al.). State-of-the-art molecular imaging techniques provide new superior means of noninvasive, repeated, and quantitative tracking of stem cell implants or transplants (Sandu et al.). From initial deposition to the survival, migration, and differentiation of the transplant/implanted stem cells, current molecular imaging methods allow monitoring of the infused cells in the same live recipient bed over time (Sandu et al.).

## **3. Positron Emission Tomography (PET)**

Over the past two decades, PET has been introduced and applied widly in the milieu of diagnosis including head and neck diseases. Along with a growing interest to use PET in tracing different types of molecules such as amino acids, receptor ligands, glucose and other metabolic substrates which follow specific metabolic pathways have been utilized to assess

Cell therapy, as a promising method using live components of human tissue for treatment of neurological diseases, is one of the mainstays of the wide field of regenerative medicine.. Among stem cell sources available up to date, mesenchymal stem cells (MSCs) have generated a great deal of interest because of their potency in terms of regeneration and

Neurodegenerative disease is a term which encompasses a wide range of acute and chronic neurological diseases such as Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis (ALS), Alzheimer's disease, multiple sclerosis (MS), stroke, and spinal cord injury. Because of the fact that central nervous system reveals limited capacity of regeneration and tissue replacement, cell therapy and tissue regeneration of injured or degenerated tissue, as a potential new therapeutic modality, draws lots of attention (Wislet-Gendebien et al., 2012). Stem cells have the potential to differentiate into many different cell types. Recent research has concerned an exciting opportunity to apply MSCs especially in the management of neurodegenerative diseases, because of their potential to differentiate into neurons (Lee et

It has been shown that the injection of MSCs could be profitable in the treatment of neurodegenerative diseases such as Parkinson's disease (Levy et al.), Spinal Stroke and cerebral ischemia (Sieber-Blum, 2010), Alzheimer's disease (Lee et al., 2010), Multiple Sclerosis (Grigoriadis et al., 2011), Huntington's Disease (Snyder et al., 2010). The advantages of using stem cells for regenerative medicine are not only the eases of harvesting from bone marrow or other donor sites but also the minimal risk of tissue rejection because

Interestingly, recent data suggest that intravenously injected MSCs could migrate into the brain and pass blood-brain barrier (BBB), which is basically formed by brain microvascular endothelial cells (BMECs), and restrict the passage of immune cells and different molecules into the brain. The exact mechanism of this phenomenon, whether it is based on Integrinmediated adhesion or is matrix metalloproteinase dependent extracellular matrix

One of the main clinical problems of stem cell transplantation is the difficulty of detection, localization, and examination of the stem cells in vivo at both cellular and molecular levels (Sandu et al.). State-of-the-art molecular imaging techniques provide new superior means of noninvasive, repeated, and quantitative tracking of stem cell implants or transplants (Sandu et al.). From initial deposition to the survival, migration, and differentiation of the transplant/implanted stem cells, current molecular imaging methods allow monitoring of

Over the past two decades, PET has been introduced and applied widly in the milieu of diagnosis including head and neck diseases. Along with a growing interest to use PET in tracing different types of molecules such as amino acids, receptor ligands, glucose and other metabolic substrates which follow specific metabolic pathways have been utilized to assess

degradation, is yet not clear (Krampera et al., 2007, Matsushita et al., 2011).

the infused cells in the same live recipient bed over time (Sandu et al.).

**3. Positron Emission Tomography (PET)**

**2. Cell therapy**

al., 2010).

tissue engineering (Wislet-Gendebien et al., 2012).

of the application of autologous cells.

different tissue functions, especially in neoplasms (Cornelius et al., 2011). The main substances for radio-labeling are fluorine-18 (F), gadolinium-68 (Ga) and Carbon-11 (C). The advantages that they confer upon other positron emitting isotopes such as O, N and C are their longer half-life time which results in superior tissue distribution and consequently a dynamic imaging of higher accuracy (de Langen et al., 2008, Keiding et al., 2010, Floeth et al., 2011, Henze et al., 2005).

Among metabolic substrates, F-fluorodeoxyglucose is the most renowned molecular tracer in oncology. It has been shown that F-FDG uptake may increase in neoplasmic cells which are believed to be indicative of an increased glycolysis and a subsided respiratory rate of tumoral tissues (Di Chiro et al., 1987). It has been demonstrated that amino acid synthesis is up regulated in tumor cells in comparison to normal tissues. Until now, F-Tyrosine and C-Methionine uptakes are studied at different grades of oncologic tumors such as meningimoma (Cremerius et al., 1997).

C-colin, another marker which represents phospholipid synthesis, is hiked in malignant tumors. It has been shown that the metabolism and proliferation of C-colin are strongly correlated with breast and prostate cancers, indicating high uptake as a measurement of cellular proliferation (Cornelius et al., 2011). Other markers, with a better theoretical profile, have also been introduced in neuroscience but are mostly used only in a limited number of experiments, so that no knowledge is about their potential for imaging stem cell transplantation.

## **4. Near-InfRared Fluorescence (NIRF) imaging**

NIRF confers similar capability for tracing of imaging agents as PET. NIRF dyes are chemically conjugated on targeting molecule such as antibodies, carbohydrates and peptides. After administration into tissues, the dye molecules are excited by the radiation of tissue penetrating NIR excitation light (750-900nm).

Considering radioactive decay, a photon of lower energy which has a higher wavelength is emitted and the fluorescent dye relaxes back to its basic state, ready to repeat the same process. Each fluorescent dye absorb excitation light and afterward, just in a nanosecond, produce fluorescent light. Accordingly, each fluorescent dye molecule provides about 100,000,000 photons per second for collection and image formation. As a consequence, lowenergy fluorescent photons are slightly absorbed and maximally scattered, reduced by the tissues between the targeted tissue and the tissue surface before being collected by detectors in a few milliseconds. In contrast with radionuclides, a fluorescent dye has no physical halflife. Moreover, this methodology is not clinically approved yet and no devices is available for human imaging with NIRF contrast agents, and NIRF contrast imaging agents do not posses approval for human study as well. Interestingly, some investigators utilized NIRF imaging for human applications such as functional lymphatic imaging, sentinel lymph node mapping, activated probes, and promising advances in 3D imaging (Sevick-Muraca EM, 2012).

## **5. Potential clinical applications of molecular imaging**

Many surgical techniques are mainly based on histological grading of the tumor. Molecular imaging enables surgeons to have the gross picture of these tumors. Pre-surgical molecular

Molecular Imaging of Stem Cells: A New Area for Neuroscience 215

several cases inflammation trigger degeneration. Also, endogenous neural stem cells, which have the capacity of renewing cells of CNS, may migrate to the affected area and robust the process of repair. This scenario drives scientists to seek a natural healing framework based on Trans stem cell therapy. In fact, it may improve the efficacy and decrease the toxicity of

Alzheimer's disease (AD) is describes as a chronic and complex progressive neurodegenerative disorder, and is the most common cause of dementia among the elderly. A great body of evidences indicates that neuronal degeneration in AD progresses in a certain way while the pathology advances over several decades. Establishing an accurate dynamic map of this sequence is vital for the understanding and mapping the complex endophenotype of AD, providing a basis for successful treatments designed to resist or

PET scanning has long been used to trace alterations in cerebral blood flow and metabolism in AD, and is useful for the differential diagnosis of dementia in individual patients. For instance, hypometabolism of the posterior cingulate cortex could be observed early in AD using fluoro-deoxyglucose (FDG) PET scanning, even while MRI results are normal (Ewers et al., 2011, Mosconi et al., 2004). As disease progresses, there is diminished cerebral metabolism and perfusion in posterior cingulate and association cortices, but the basal ganglia and thalamus, cerebellum, and primary sensorimotor cortices are largely spared until later stages of the disease. Some studies have tracked AD with PET, using new molecular probes sensitive to amyloid-beta (Aβ) or neurofibrillary tangle pathology, or both types of pathology (Braskie et al., 2010, Klunk et al., 2004, Mintun et al., 2006, Small et al.,

Multiple sclerosis appears to result from defective innate and adaptive immune system actions in the CNS, likely begins with a kind of autoimmune response that leads to demyelination and neuronal degeneration. It seems that auto activity of immune system followed by neuroinflamation gradually causes death of oligodendrocytes. Increasing body of evidence represents that the brain's inability to repair is one of the main factors that make this disease irreversible and causes permanent damages. This claim is in accordance with the fact that the progression of disease and state of remyelination is different in every patient. Consistently, Patrikios et al. (Patrikios et al.), recently performed 51 autopsies of MS patients with different clinical courses and disease durations and traced variable extents of remyelination. In 20% of patients – both relapsing and progressive patients – the extent of

remyelination was extensive with 60–96% of the global lesion area remyelinated.

Several attempts have been made since about four decades to replace demieylinated cells with myelin forming cells; however, as neural cells are highly differentiated and have limited ability for growth and expansion, these efforts have been mostly abortive in vivo. With the advent of stem cell regenerative medicine, scientists further discovered the

treatment modalities which are the main concepts of personalized medicine.

**7. Molecular imaging in Alzheimer's disease**

prevent disease progression.

**8. Stem cell in Multiple Sclerosis (MS)**

2006).

imaging of neurological tumors enables us to establish better surgical procedure and strategy. Also, it would allow physicians to have a better pre-assessment of prognosis and risk benefit ratio. Moreover, PET might be helpful in the early discovery of molecular-level changes, which are undetectable in CT and MRI. Also, within a specific tumor, the most metabolically active and aggressive tumor areas could be mapped.

Intriguingly, emerging data suggest that molecular imaging could be used as an outcome assessment tool for different treatment modalities. For instance, a recent study compared MRI response assessment with metabolic O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) PET response assessment during antiangiogenic treatment in patients with recurrent high-grade glioma (see table 1). Good results are already known for (18)FDG-PET, but results revealed that (18)F-FET PET, on the other hand, seems also to be predictive of treatment failure in that it contributes important information to response assessment based only on MRI and response assessment in neurooncology (RANO) criteria (Hutterer et al., 2011).

Table 1. (18)F-FDG Positron Emission Tomography with a Gold Standard of Biopsy or Radiography Follow-up (Schaller et al., 2008)

## **6. Stem cell imaging at neurodegenerative pathways and pathologies**

Regenerative medicine is a developing era that seeks to restore or replace damaged tissue and organs via natural or bioengineered tools. Regenerative medicine via stem cells is considered as one of the most promising means to repair tissues. Stem cells could be isolated from both adult human and embryonic tissues. There is a great body of evidence demonstrating successful cases of expanded stem cell in vitro and then grafted in vivo for regenerative purposes at different tissues such as bone, cartilage, and myocardium.

It seems that inflammation and degeneration are the two main aspects of CNS pathogenesis. It seems that there is an intrinsic connection between these two phenomena because in

imaging of neurological tumors enables us to establish better surgical procedure and strategy. Also, it would allow physicians to have a better pre-assessment of prognosis and risk benefit ratio. Moreover, PET might be helpful in the early discovery of molecular-level changes, which are undetectable in CT and MRI. Also, within a specific tumor, the most

Intriguingly, emerging data suggest that molecular imaging could be used as an outcome assessment tool for different treatment modalities. For instance, a recent study compared MRI response assessment with metabolic O-(2-(18)F-fluoroethyl)-L-tyrosine ((18)F-FET) PET response assessment during antiangiogenic treatment in patients with recurrent high-grade glioma (see table 1). Good results are already known for (18)FDG-PET, but results revealed that (18)F-FET PET, on the other hand, seems also to be predictive of treatment failure in that it contributes important information to response assessment based only on MRI and

metabolically active and aggressive tumor areas could be mapped.

response assessment in neurooncology (RANO) criteria (Hutterer et al., 2011).

Table 1. (18)F-FDG Positron Emission Tomography with a Gold Standard of Biopsy or

**6. Stem cell imaging at neurodegenerative pathways and pathologies**

regenerative purposes at different tissues such as bone, cartilage, and myocardium.

Regenerative medicine is a developing era that seeks to restore or replace damaged tissue and organs via natural or bioengineered tools. Regenerative medicine via stem cells is considered as one of the most promising means to repair tissues. Stem cells could be isolated from both adult human and embryonic tissues. There is a great body of evidence demonstrating successful cases of expanded stem cell in vitro and then grafted in vivo for

It seems that inflammation and degeneration are the two main aspects of CNS pathogenesis. It seems that there is an intrinsic connection between these two phenomena because in

Radiography Follow-up (Schaller et al., 2008)

several cases inflammation trigger degeneration. Also, endogenous neural stem cells, which have the capacity of renewing cells of CNS, may migrate to the affected area and robust the process of repair. This scenario drives scientists to seek a natural healing framework based on Trans stem cell therapy. In fact, it may improve the efficacy and decrease the toxicity of treatment modalities which are the main concepts of personalized medicine.

## **7. Molecular imaging in Alzheimer's disease**

Alzheimer's disease (AD) is describes as a chronic and complex progressive neurodegenerative disorder, and is the most common cause of dementia among the elderly. A great body of evidences indicates that neuronal degeneration in AD progresses in a certain way while the pathology advances over several decades. Establishing an accurate dynamic map of this sequence is vital for the understanding and mapping the complex endophenotype of AD, providing a basis for successful treatments designed to resist or prevent disease progression.

PET scanning has long been used to trace alterations in cerebral blood flow and metabolism in AD, and is useful for the differential diagnosis of dementia in individual patients. For instance, hypometabolism of the posterior cingulate cortex could be observed early in AD using fluoro-deoxyglucose (FDG) PET scanning, even while MRI results are normal (Ewers et al., 2011, Mosconi et al., 2004). As disease progresses, there is diminished cerebral metabolism and perfusion in posterior cingulate and association cortices, but the basal ganglia and thalamus, cerebellum, and primary sensorimotor cortices are largely spared until later stages of the disease. Some studies have tracked AD with PET, using new molecular probes sensitive to amyloid-beta (Aβ) or neurofibrillary tangle pathology, or both types of pathology (Braskie et al., 2010, Klunk et al., 2004, Mintun et al., 2006, Small et al., 2006).

## **8. Stem cell in Multiple Sclerosis (MS)**

Multiple sclerosis appears to result from defective innate and adaptive immune system actions in the CNS, likely begins with a kind of autoimmune response that leads to demyelination and neuronal degeneration. It seems that auto activity of immune system followed by neuroinflamation gradually causes death of oligodendrocytes. Increasing body of evidence represents that the brain's inability to repair is one of the main factors that make this disease irreversible and causes permanent damages. This claim is in accordance with the fact that the progression of disease and state of remyelination is different in every patient. Consistently, Patrikios et al. (Patrikios et al.), recently performed 51 autopsies of MS patients with different clinical courses and disease durations and traced variable extents of remyelination. In 20% of patients – both relapsing and progressive patients – the extent of remyelination was extensive with 60–96% of the global lesion area remyelinated.

Several attempts have been made since about four decades to replace demieylinated cells with myelin forming cells; however, as neural cells are highly differentiated and have limited ability for growth and expansion, these efforts have been mostly abortive in vivo. With the advent of stem cell regenerative medicine, scientists further discovered the

Molecular Imaging of Stem Cells: A New Area for Neuroscience 217

scientists to monitor different phase of repair and stem cell therapy. This allows not only the imaging in real time of stem cell migration, but also its integration, and therapeutic effects at the single cell level. Also, the above-mentioned capacities for tracing and monitoring of grafted stem cell behavior, enable us to assess both quantitatively and

Molecular imaging will gain more and more importance in neurosciences. PET is currently the gold standard and that will not change very soon. However, the different methods have different advantages and disadvantages, but it is not only for the researcher but also for the physician important to know this differences. In the near future, imaging on the molecular but also on the cellular level will not be an exception, as it is still now, but rather the rule. Such improvements open widely the door to a personalized medicine, a fact that has special

Molecular imaging has gained step-by-step importance in neuroscience. To know this

Akiyama, Y., Radtke, C., Honmou, O. & Kocsis, J. D. 2002a. Remyelination of the spinal cord following intravenous delivery of bone marrow cells. *Glia,* 39**,** 229-36. Akiyama, Y., Radtke, C. & Kocsis, J. D. 2002b. Remyelination of the rat spinal cord by

Braskie, M. N., Klunder, A. D., Hayashi, K. M., Protas, H., Kepe, V., Miller, K. J., Huang, S.

Brekke, C., Williams, S. C., Price, J., Thorsen, F. & Modo, M. 2007. Cellular multiparametric MRI of neural stem cell therapy in a rat glioma model. *NeuroImage,* 37**,** 769-82. Carney, B. J. & Shah, K. 2011. Migration and fate of therapeutic stem cells in different brain

Cornelius, J. F., Langen, K. J., Stoffels, G., Hanggi, D., Sabel, M. & Steiger, H. J. 2011. Pet

Cremerius, U., Bares, R., Weis, J., Sabri, O., Mull, M., Schroder, J. M., Gilsbach, J. M. & Buell,

Imaging Of Meningioma In Clinical Practice: Review of literature and future

U. 1997. Fasting improves discrimination of grade 1 and atypical or malignant meningioma in FDG-PET. *Journal of nuclear medicine : official publication, Society of*

transplantation of identified bone marrow stromal cells. *The Journal of neuroscience :*

C., Barrio, J. R., Ercoli, L. M., Siddarth, P., Satyamurthy, N., Liu, J., Toga, A. W., Bookheimer, S. Y., Small, G. W. & Thompson, P. M. 2010. Plaque and tangle imaging and cognition in normal aging and Alzheimer's disease. *Neurobiology of*

method in detail is demanding but important in the area of personalized medicine.

*the official journal of the Society for Neuroscience,* 22**,** 6623-30.

qualitatively the interaction between stem cell and its environment.

**10. View to the future**

importance in neurosciences.

**11. Take-home-message**

*aging,* 31**,** 1669-78.

directions. *Neurosurgery*.

*Nuclear Medicine,* 38**,** 26-30.

disease models. *Neuroscience,* 197**,** 37-47.

**12. References**

functional and special characteristics of adult stem cells and attempted to utilize them for the treatment of neurodegenerative multiple sclerosis.

Adult stem cells have not only provided us with a readily available cell source for cell therapy but also with much safer and far less toxic tumor treatment measures. There are two basic routes for cell administration including in-site cell transplantation or blood injection (circulation). Tangible examples for the former include Parkinson's disease, acute spinal cord injury and brain trauma. For multifocal diseases such as MS and epilepsy, the model of in-site injection is not effective; rather blood circulation or cerebrospinal fluid circulation should be used. There are several studies demonstrating successful results with the transplantation of hematopietic, mesenchymal and neural stem cells into the CNS injured areas in MS, SCI, epilepsy, and stroke cases (Pluchino et al., 2009).

## **9. Mechanisms of Stem Cell Repairing**

A great amount of data demonstrates that HSCs have a great capacity to differentiate into different cell types including muscle, skin, neural tissue and lung (Pluchino et al., 2009). Several studies used HSCs injection as a treatment modality for hematological malignancies and shows that HSCs have the capability of entering the brain and as well as differentiating and producing new neural cells including neurons and microglia; these findings were in concordance by the detection of Y chromosome- positive Purkinje cells in the cerebellum of female rats who have undergone bone marrow transplantation from male donors (Koshizuka et al., 2004 2005). In addition, in an animal studies on rats suffering from a demyelinated lesion of the spinal cord, intravenous or intraparenchimal HSCs therapy lead to different degrees of remyelination which was proportional to the number of injected stem cells (Akiyama et al., 2002a 2002, Inoue et al, 2003, Akiyama et al., 2002b 2002, Inoue et al, 2003).

Several studies have challenged the idea of cell replacement in stem cell regeneration theories. Despite the fact that MSCs can exert various therapeutic effects, some data suggest that they protect immune system from demyelination via alternative mechanism to cell replacement.

The suggested therapeutic effect is based on the intrinsic capacities of such cells to secret a large number of cytokines and chemokines. It has been shown that the pattern of secretion changes as early as transplanted stem cell encounter new (micro) environment in vivo. Some studies have shown that mesenchymal stem cells mainly act by suppressing inflammation, via cell-to-cell contact as well as through the activation of anti-inflammatory pathways; and consequently the proliferation, migration and differentiation of endogenous progenitor cells are enhanced.

Utilizing fluorescent dyes and imaging modalities in humans is not possible due to limited depth of tissue penetration. On the other hand, magnetic resonance imaging (MRI) and positron emission tomography (PET) have shown promising value and also great feasibility for treatment via stem cell. As well as above PET scan imaging, stem cells could be tagged with ferromagnetic material or with gadolinium rhodamine dextran and then detect via MRI (Brekke et al., 2007) (Carney and Shah, 2011). These imaging modalities along with alteration of genes via genetic engineering and consequent labeling methods, enabled scientists to monitor different phase of repair and stem cell therapy. This allows not only the imaging in real time of stem cell migration, but also its integration, and therapeutic effects at the single cell level. Also, the above-mentioned capacities for tracing and monitoring of grafted stem cell behavior, enable us to assess both quantitatively and qualitatively the interaction between stem cell and its environment.

## **10. View to the future**

216 Molecular Imaging

functional and special characteristics of adult stem cells and attempted to utilize them for

Adult stem cells have not only provided us with a readily available cell source for cell therapy but also with much safer and far less toxic tumor treatment measures. There are two basic routes for cell administration including in-site cell transplantation or blood injection (circulation). Tangible examples for the former include Parkinson's disease, acute spinal cord injury and brain trauma. For multifocal diseases such as MS and epilepsy, the model of in-site injection is not effective; rather blood circulation or cerebrospinal fluid circulation should be used. There are several studies demonstrating successful results with the transplantation of hematopietic, mesenchymal and neural stem cells into the CNS injured

A great amount of data demonstrates that HSCs have a great capacity to differentiate into different cell types including muscle, skin, neural tissue and lung (Pluchino et al., 2009). Several studies used HSCs injection as a treatment modality for hematological malignancies and shows that HSCs have the capability of entering the brain and as well as differentiating and producing new neural cells including neurons and microglia; these findings were in concordance by the detection of Y chromosome- positive Purkinje cells in the cerebellum of female rats who have undergone bone marrow transplantation from male donors (Koshizuka et al., 2004 2005). In addition, in an animal studies on rats suffering from a demyelinated lesion of the spinal cord, intravenous or intraparenchimal HSCs therapy lead to different degrees of remyelination which was proportional to the number of injected stem cells (Akiyama et al.,

Several studies have challenged the idea of cell replacement in stem cell regeneration theories. Despite the fact that MSCs can exert various therapeutic effects, some data suggest that they protect immune system from demyelination via alternative mechanism to cell

The suggested therapeutic effect is based on the intrinsic capacities of such cells to secret a large number of cytokines and chemokines. It has been shown that the pattern of secretion changes as early as transplanted stem cell encounter new (micro) environment in vivo. Some studies have shown that mesenchymal stem cells mainly act by suppressing inflammation, via cell-to-cell contact as well as through the activation of anti-inflammatory pathways; and consequently the proliferation, migration and differentiation of endogenous progenitor cells

Utilizing fluorescent dyes and imaging modalities in humans is not possible due to limited depth of tissue penetration. On the other hand, magnetic resonance imaging (MRI) and positron emission tomography (PET) have shown promising value and also great feasibility for treatment via stem cell. As well as above PET scan imaging, stem cells could be tagged with ferromagnetic material or with gadolinium rhodamine dextran and then detect via MRI (Brekke et al., 2007) (Carney and Shah, 2011). These imaging modalities along with alteration of genes via genetic engineering and consequent labeling methods, enabled

the treatment of neurodegenerative multiple sclerosis.

**9. Mechanisms of Stem Cell Repairing**

replacement.

are enhanced.

areas in MS, SCI, epilepsy, and stroke cases (Pluchino et al., 2009).

2002a 2002, Inoue et al, 2003, Akiyama et al., 2002b 2002, Inoue et al, 2003).

Molecular imaging will gain more and more importance in neurosciences. PET is currently the gold standard and that will not change very soon. However, the different methods have different advantages and disadvantages, but it is not only for the researcher but also for the physician important to know this differences. In the near future, imaging on the molecular but also on the cellular level will not be an exception, as it is still now, but rather the rule. Such improvements open widely the door to a personalized medicine, a fact that has special importance in neurosciences.

## **11. Take-home-message**

Molecular imaging has gained step-by-step importance in neuroscience. To know this method in detail is demanding but important in the area of personalized medicine.

## **12. References**

Molecular Imaging of Stem Cells: A New Area for Neuroscience 219

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**10**

**Molecular MRI**

*The Netherlands*

**of Atherosclerosis**

B.C. Te Boekhorst and K. Nicolay

*Department of Biomedical Engineering, Eindhoven University of Technology,*

Atherosclerosis and its consequences contribute to more than 50% of mortality in Europe, the United States of America and many developed or developing countries (Schafers et al., 2010). The main pathologic substrate in atherosclerosis is the atherosclerotic lesion. The classical high-risk plaque is characterized by a large lipid core, a thin fibrous cap and many macrophages in the shoulder regions of the plaque (Falk et al., 1995; Stary et al., 1995). Rupture of the fibrous cap or, more subtle, erosion leads to exposure of the thrombogenic sub-endothelial matrix to blood leading to thrombus formation. Recurrent thrombosis or major atherothrombosis may lead to an acute cardiovascular event via sub(total) occlusion of the vessel of interest. Macrophages are the key mediators of plaque inflammation and progression (Lusis, 2000; Ross, 1999). Inflammatory activity can be evaluated at the cellular (macrophages and neutrophils) or sub-cellular (secreted enzymes, membrane-bound proteins) level. Sensitive and specific biomarkers of plaque inflammation and instability are highly desired for more accurate definition of high-risk plaques in high risk patient populations. At present, serologic markers do not accomplish this need sufficiently. Therefore, there is a need for noninvasive imaging techniques for identification and quantification of local plaque biomarkers, which can direct interventional cardiologists and

Atherosclerosis is characterized by various more or less orderly processes taking place during the development from a fatty streak to a rupture-prone or ruptured plaque (Ross, 1999). Several morphological stages of plaque development related to risk of rupture were described by the American Heart Association (Stary et al., 1995). However, recently other plaques than the classical vulnerable plaque with a large lipid core and thin fibrous cap were also recognized as high-risk plaque, like highly calcified and obstructive plaques (Naghavi et al., 2003a, 2003b). During the process of atherogenesis, inflammation aggravates endothelial dysfunction and *vice versa*. First, low density lipoproteins are deposited in the subintima, where rare neutrophils and macrophages produce myeloperoxidase. This leads to generation of tyrosyl radicals and aldehydes resulting in oxidation of LDL. Oxidized LDL (ox-LDL) irritates endothelial cells which become dysfunctional, leading to inflammatory cell migration and activation. Macrophage activation leads to release of

vascular surgeons to diseased sites and can monitor therapeutic response.

**1. Introduction**

**2. Atherosclerosis**

## **Molecular MRI of Atherosclerosis**