방사선의학 웹진

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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 [18F]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 [18F]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.

Affiliations

Daesung Kim # 1, Kyobin Choo # 2, Sangwon Lee 3, Seongjin Kang 3, Mijin Yun # 4, Jaewon Yang # 5
1Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea.
2Department of Computer Science, Yonsei University, Seoul, Republic of Korea.
3Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
4Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea. yunmijin@yuhs.ac.
5Department of Radiology, University of Texas Southwestern, Dallas, TX, USA.
#Contributed equally.