Aw4.6 ge workstation

Hyperintense putaminal rim (HPR), putaminal atrophy, and hot cross bun were visually assessed by two radiologists (NL and LY, 2 and 6 years of experience, respectively) and two neurologists (SL and KL, 20 and 12 years of experience, respectively). The T2-hyperintense rim, located predominantly at the dorsolateral margin of the putamen, was evaluated as HPR in our study.
Magnetization transfer ratio asymmetry (MTR_asym) was further performed on an AW4.6 GE workstation. The APT MTR_asym was calculated as MTR_asym (3.5 ppm) = Ssat (−3.5 ppm)/S0 − Ssat (+3.5 ppm)/S0, in which Ssat is the signal intensity with selective saturation, while S0 is not (Zhou et al., 2013 (link)). B0 inhomogeneity was corrected on a pixel-by-pixel basis. The data of APT MTR_asym were presented in percentage (%).
T2WI was used to evaluate the function of APT images in our study mainly because T2WI could show SN and RN better than T1WI. Two radiologists (NL and LY) analyzed the quantitative MTR_asym images and compared the difference between MSA-P patients and NCs. Six regions of interest (ROIs), including RN, SN, globus pallidus, thalamus, caudate, and putamen of both hemispheres, were manually drawn based on T2WI images. ROIs from one representative subject overlaid on top of his own T2WI images and APT MTR_asym maps are as shown in Figures 1, 2.

To calculate the quantitative parameters, including the gray matter volume (GMV), white matter volume (WMV), and CBF values of different brain regions, we followed an atlas-based image processing approach. CBF maps were further calculated from the 3D-PCASL data using an AW4.6 GE Workstation. Second, we performed rigid registration between CBF maps and 3D-T1WI data using SPM12 software (http://www.fil.ion.ucl.ac.uk/spm/) based on MATLAB (MathWorks, Natick, MA, USA). Third, 3D-T1WI images were segmented and nonlinearly normalized into MNI space using the CAT12 toolbox (http://www.neuro.uni-jena.de/cat/) implemented in SPM12 to obtain tissue probability maps and normalized CBF quantitative maps. Finally, the brain was parcellated into 38 anatomical regions based on the automated anatomical labeling atlas (AAL-3) for quantitative parameter extraction. Brain regional volumes and CBF can be extracted by averaging the values from voxels in specific brain regions. The estimated global GMV and WMV were further normalized by correction of the intracranial volume (Figures 1, 2).