A Multi-Atlas-Based [18F]9-Fluoropropyl-(+)-Dihydrotetrabenazine Positron Emission Tomography Image Segmentation Method for Parkinson’s Disease Quantification - Pan, Yiwei; Liu, Shuying; Zeng, Yao; Ye, Chenfei; Qiao, Hongwen; Song, Tianbing; Lv, Haiyan; Chan, Piu; Lu, Jie; Ma, Ting (Frontiers in Aging Neuroscience, 2022)
Objectives [18F]9-fluoropropyl-(+)-dihydrotetrabenzazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson’s disease. In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for Parkinson’s disease quantification assessment. Methods A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study and both brain PET and MR scans were conducted. Data from 20 subjects were used to generate atlases, based on which a multi-atlas-based [18F]-FP-DTBZ PET segmentation method was developed especially for striatum and its subregions. The proposed method was compared with the template-based method through striatal subregion parcellation performance and the standard uptake value ratios (SUVRs) quantification accuracy. Discriminant analysis between healthy controls and PD patients was further performed. Results Segmentation results of the multi-atlas-based method showed better consistency than the template-based method with the ground truth, yielding a dice coefficient of 0.81 over 0.73 on the full striatum. The SUVRs calculated by the multi-atlas-based method had an average ICC of 0.953 with the standardized result, while the template-based method only reached 0.815. The SUVRs of healthy controls were generally higher than that of PD patients and showed significant differences in all of the striatal subregions (all p<0.001). The median and posterior putamen performed best in discriminating PD patients from healthy controls. Conclusions The proposed multi-atlas-based [18F]-FP-DTBZ PET image segmentation method achieved better performance than the template-based method, indicating great potential in improving accuracy and efficiency for Parkinson’s disease diagnosis in clinical routine.