2023年6月にカナダ，トロントで開催されたISMRM（国際磁気共鳴医学会）において，量子科学技術研究開発機構（QST）の青木伊知男先生および名古屋大学の田岡俊昭先生がSenior Fellowに選出されました．

　青木先生および田岡先生のSenior Fellowの受賞は，5年連続での日本磁気共鳴医学会会員の受賞となり，近年の日本のMR研究が国際的に認められている証と考えられます．

　この度，日本磁気共鳴医学会会員のISMRM Senior Fellow選出を記念して，お二人とご縁の深い先生方から特別寄稿をいただきましたので，ここに掲載させていただきます．

　Using the Dixon method, we developed a new post-processing image, named “T2 Dixon opposed-fat subtraction image.” The Dixon method allows four types of images : opposed, in-phase, water, and fat. The proposed method generates an image by subtracting the fat image from the opposed image. Further, it emphasizes bone lesions with much water by degenerated normal tissues than “water image” while reducing the signal of peri-lesioned tissues with less water content. This suggests that the generated image may help MR findings owing to the high contrast-to-noise ratio in lesions with substantially higher water content. Moreover, since additional image acquisition is not required, it can provide useful information without extending the examination time.

　The definitional equations of the distortion of diffusion-weighted imaging (DWI) were re-examined. We examined the relationship between echo space (ESP) and parameters related to distortion (frequency matrix, receiver bandwidth, number of shots, field of view (FOV), and parallel imaging (PI) factor). The results showed that there was no change in the distortion ratio when each frequency matrix and receiver bandwidth was changed for the same values of ESP. Conversely, the distortion ratio changed when FOV and the number of shots changed, even when ESP was constant. In addition, the distortion ratio was lower when PI was used. Furthermore, ESP values were similar with and without PI. Therefore, distortion in DWI can be expressed as distortion∝change in resonance frequency×ESP×FOV in the phase direction. Moreover, the parameters related to ESP are frequency matrix and receiver bandwidth; and those related to FOV in the phase direction are FOV, number of shots, phase-FOV ratio, and PI factor.

　Corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) are sporadic atypical parkinsonian disorders associated with 4-repeat tauopathies. These neurodegenerative conditions closely overlap in their clinical information, pathology, and genetic risk factors ; therefore, it is difficult to accurately diagnose CBS and PSP. Recently, an unsupervised machine-learning technique, called Subtype and Stage Inference (SuStaIn), has been proposed to reveal the data-driven disease phenotypes with distinct temporal progression patterns from widely available cross-sectional data. To clarify the differences in the temporal white matter (WM) degeneration patterns between CBS and PSP, this study applied SuStaIn for fractional anisotropy (FA) in regional WM, which was sensitive to WM degeneration, based on cross-sectional brain diffusion MRI (dMRI) data.

　We obtained dMRI data from 15 healthy controls, 26 patients with CBS, and 25 patients with PSP. FA was calculated after fitting the diffusion tensor model to the corrected dMRI data for susceptibility and eddy-current induced geometric distortions and inter-volume subject motion. SuStaIn was applied to the cross-sectional regional WM tract FAs to identify both the disease subtypes and their trajectories with distinct WM degeneration patterns. To assess the performance of SuStaIn, the classification accuracy and sensitivity for CBS and PSP were calculated.

　SuStaIn revealed that the CBS degeneration started from the fornix and stria terminalis (FSTs) and corpus callosum (CC), followed by the posterior corona radiata (PCR), posterior thalamic radiation (PTR), and cerebral peduncle (CP), and subsequently extended to the cingulum. Finally, it reached the superior cerebral peduncle (SCP) and corticospinal tract (CST). In contrast, the PSP degeneration started from the SCP and cingulum, followed by the CST, and subsequently extended to the FST and CC. Eventually, it reached the PCR, PTR, and CP. Accordingly, SuStaIn classified CBS and PSP with 0.863 accuracy (sensitivity : CBS, 0.885 ; PSP, 0.840). The results suggested the utility of SuStaIn for classifying patients with CBS and PSP and identifying temporal WM degeneration patterns in patients with CBS and PSP.

　When analyzing the multi-site diffusion MRI (dMRI) data, metrics should be harmonized to remove the site effect. In this study, we applied the combined association test (ComBat), which used the regression of covariates with an empirical Bayes framework for diffusion metrics based on diffusion tensor imaging (DTI), neurite orientation dispersion, and density imaging (NODDI). The results showed that the ComBat could harmonize the site-related effects in DTI and NODDI metrics based on multi-site dMRI while preserving the biological information of the participant, such as sex differences and correlation with age. Thus, the ComBat could be applied in large multi-site studies to identify subtle white matter changes.