Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.

Abstract

Purpose: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.

Patients and methods: A retrospective dataset of PET/CT and T1-weighted MRI pairs from 226 individuals (157 with mild cognitive impairment or dementia and 69 healthy controls) was used. The dataset was split into training/validation (60%) and test (40%) sets. Utilizing auto-generated segmentation labels, 3 UNets were independently trained for multiplanar brain parcellation on CT and subsequently ensembled. Amyloid load was measured across 46 volumes of interest (VOIs), derived from the Desikan-Killiany-Tourville atlas. Dice similarity coefficient between the proposed CT-based DL model and MRI-based (FreeSurfer) method was calculated, with SUVR comparison using linear regression analysis and intraclass correlation coefficient. Global SUVRs were also compared within groups with clinical dementia ratings (CDRs) of 0, 0.5, and 1.

Results: The DL-based CT parcellation achieved mean Dice similarity coefficients of 0.80 for all 46 VOIs, 0.72 for 16 cortical and limbic VOIs, and 0.83 for 30 subcortical VOIs. For regional and global SUVR comparisons, the linear regression yielded a slope, y-intercept, and R2 of 1 ± 0.027, 0 ± 0.040, and ≧0.976, respectively ( P  < 0.001), and the intraclass correlation coefficient was ≧0.988 ( P  < 0.001). For global SUVRs in each CDR group, these values were 1 ± 0.020, 0 ± 0.026, ≧0.993, and ≧0.996, respectively ( P  < 0.001). Both MRI-based and CT-based global SUVR showed a consistent increase as the CDR score increased.

Conclusions: The DL-based CT parcellation agrees strongly with MRI-based methods for amyloid PET quantification.