Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study

Abstract

Purpose Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ considerably. To address these problems, this study proposes a deep learning framework for translating CT of PET/CT to synthetic MR images (MRSYN) and performing automated quantitative regional analysis using MRSYN-derived segmentation. Methods In this retrospective study, 139 subjects who underwent brain [F-18]FBB PET/CT and T1-weighted MRI were included. A U-Net-like model was trained to translate CT images to MRSYN; subsequently, a separate model was trained to segment MRSYN into 95 regions. Regional and composite standardised uptake value ratio (SUVr) was calculated in [F-18]FBB PET images using the acquired ROIs. For evaluation of MRSYN, quantitative measurements including structural similarity index measure (SSIM) were employed, while for MRSYN-based segmentation evaluation, Dice similarity coefficient (DSC) was calculated. Wilcoxon signed-rank test was performed for SUVrs computed using MRSYN and ground-truth MR (MRGT). Results Compared to MRGT, the mean SSIM of MRSYN was 0.974 +/- 0.005. The MRSYN-based segmentation achieved a mean DSC of 0.733 across 95 regions. No statistical significance (P > 0.05) was found for SUVr between the ROIs from MRSYN and those from MRGT, excluding the precuneus. Conclusion We demonstrated a deep learning framework for automated regional brain analysis in PET/CT with MRSYN. Our proposed framework can benefit patients who have difficulties in performing an MRI scan.