Discussion
MR, DNN-based
In this post-market multi-vendor study using commercially available DNN-based parallel imaging reconstruction, the FSE-DNN reconstruction of highly accelerated MRI scan reduced acquisition time by an overall 41.0% for a 2D FSE image of the knee MRI. This algorithm is an image-based DNN-reconstruction, which does not require the The strength of our study is that it is a prospective, multi-reader, multi-vendor study as a post-market surveillance. We applied the reconstruction algorithm to knee MRI sequences from all three vendors, showing the possibility of application to multi-vendor MRI applications in radiology. With this strength of this FSE-DNN model, this image-based DNN reconstruction can be easily employed in the radiologic workflow of multi-vendor MRI with various MRI parameters. By changing the parallel imaging factor or number of phase encoding steps of the conventional routine MR sequence, which is easily applicable in the clinical MR imaging protocol, this DNN image reconstruction can reduce scan times with non-inferior image quality and comparable diagnostic performance.In the quantitative evaluation, FSE-DNN reconstructed images showed higher SNR and CNR, corresponding with previous FSE-DNN studiesIn our study, overall image qualities of anatomical structures were better or equal in most cases. By utilizing deep learning reconstruction of MRI, it is possible to reduce scan time and minimize patient movement, resulting in motion-less imaging. However, the artifacts related with parallel imaging can be pronounced, and these artifacts may persist in some patients. This highlights the need for optimized MRI sequences tailored for accelerated MR imaging. Further research in this direction is imperative in the future. This optimization of MRI sequences involves the choice of CS and the parallel imaging factor, and optimized In the diagnostic performance of lesion analysis, FSE-DNN reconstructed images showed non-inferiority compared to FSE-conventional images. In our study, no significant difference was observed in the diagnostic performance between FSE-conventional and FSE-DNN images. In cartilage evaluation, FSE-DNN showed under-graded lesion (n = 1) and over-graded lesion (n = 1) in small numbers (n = 2/45) among 45 image sets. However, there is no statistical difference between FSE-DNN reconstructed images and FSE-conventional images in this 45-case study. This under-grade or over-grade of cartilage may have originated from acceleration artifacts and image degradation rather than the DNN- reconstruction in our early clinical validation with routine clinical MRI protocols (Fig. There were several limitations of this study as well. First, the acquisition parameter modifications were not the same between the three vendors. For example, compressed sensing is routinely used in only one of the scanners, whereas the other two scanners utilize parallel imaging only. We intended the DNN-based reconstruction application to the current MRI sequences, reflecting the clinical practice. Secondly, we set a denoising level of low (51% reduction) according to a preceding internal study on noise reduction level for knee MRI. However, optimal image reduction level should be further investigated in clinical MRI, which could depend on the scanner, imaging joint or target, and radio-frequency coil. Thirdly, our diagnostic evaluations were not confirmed arthroscopically in all patients. We conducted this study with radiologists’ consensus as a gold standard of diagnostic performance. Despite evaluation in limited number of patients and limited pathologic findings, this prospective study supports the possibility and the generalizability of DNN reconstruction of highly accelerated MRI to reduce the MRI scan time in clinical practice. Fourthly, the measurements for SNR and CNR were conducted by conventional approach. While optimized methods are available for sensitivity map-based parallel imagingIn conclusion, DNN can be applied to accelerated knee imaging in multi-vendor MRI scanners, with reduced scan time and comparable image quality. Cartilage grading could be under- or over-graded while good agreements in ligamentous and meniscal evaluations. Therefore, the readers should be cautious in utilizing in DNN-accelerated MRI for some lesion evaluation. This study suggests the potential for routine MRI protocols applications to DNN-accelerated knee MRI in clinical practice.