Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.
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...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 52; no. 8; pp. 2959 - 2968 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185941775&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185941775 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Jul2025 vid: 52 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185941775 183106510 10.1007/s00259-025-07132-2 185941775 ppf: 2959 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study. aug: au: Kim, Daesung Choo, Kyobin Lee, Sangwon Kang, Seongjin Yun, Mijin Yang, Jaewon affil: https://ror.org/01wjejq96 Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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