Abstract
241923
Introduction: Structural magnetic resonance imaging (sMRI) can directly measure the location and degree of brain atrophy, which is an important technique for the study and diagnosis of Frontotemporal dementia (FTD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET), which measures glucose metabolism, is one of the most commonly used imaging biomarkers for FTD. Cerebral blood flow (CBF) measured by arterial spin-labeling (ASL) MRI is considered to be tightly related to glucose metabolism. However, the diagnostic value of sMRI, ASL and 18F-FDG PET for FTD is still controversial. The objective of this study was to compare quantitative gray matter volume, glucose metabolism and CBF values in patients with FTD using integrated PET/MR.
Methods: Maps of gray matter volume (GMV), 18F-FDG PET standardized uptake value ratio (SUVR), and relative CBF (rCBF) were calculated from GE Signa PET/MR in 30 patients with FTD (mean age, 61.67 years ± 6.59) and 30 normal cognitive healthy control (NC) participants (mean age, 62.90 years ± 9.47). GMV was quantified using 3D T1WI imaging with CAT12. 18F-FDG PET SUVR was calculated relative to the pons. rCBFwas calculated relative to the putamen. The whole-brain voxel-wise based and region of interest (ROI) -based two-sample T-tests of the GMV, 18F-FDG PET SUVR, and rCBF between groups were performed using SPM12. The 12 priori regions of ROIs were created using the AAL atlas in SPM12. Also, the area under receiver operating characteristic (ROC) curves analysis was performed using SPSS 26.0 to evaluate the classification performance of GMV, 18F-FDG SUVR, and rCBF images of the 12 ROIs. Voxels level with P < 0.01 and cluster level with P < 0.05 was considered as of statistical significance with gaussian random field (GRF) corrected.
Results: Patients with FTD had significantly lower MMSE (P < 0.001), MOCA scores (P < 0.001), and higher CDR scores (P < 0.001) compared with NC participants. Analyses revealed overlaps between decreased GMV, 18F-FDG PET SUVR, and regional rCBF in patients with FTD compared with NC participants in the bilateral frontotemporal regions, insular cortex, cingulate cortex, and some regions in the parietal cortex (voxels level with P < 0.01, cluster level with P < 0.05, GRF corrected). The findings of the ROC analysis indicated that the area under the curve (AUC) for the left insular cortex GMV and 18F-FDG PET SUVR in diagnosing FTD were the greatest, with values of 0.988 and 0.991. For rCBF, the right insular cortex had the highest AUC of 0.893 for FTD diagnosis. The left insula cortex demonstrated the highest diagnostic accuracy for FTD with an AUC of 0.997 after combining GMV, 18F-FDG PET SUVR, and rCBF.
Conclusions: The distribution patterns of cerebral gray matter atrophy, hypometabolism, and hypoperfusion partial overlap in FTD patients. In comparison, 3D T1WI and 18F-FDG PET have better diagnostic accuracy than ASL-MRI for FTD patients.
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