CT-Free Attenuation and Scatter Correction of [<sup>11</sup>C]CFT Brain PET Using a Bi-Directional Matching Network.
Authors
Affiliations (6)
Affiliations (6)
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai 200025, China.
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China; School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
- Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai 200240, China; School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China; MOE-LSC, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: [email protected].
- PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China. Electronic address: [email protected].
Abstract
Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation exposure-a significant concern for neurological studies involving repeated scans. Here we applied and extended a CT-free deep learning framework for [<sup>11</sup>C]CFT brain PET that achieves diagnostic-comparable dopamine transporter (DAT) quantification while avoiding CT-associated radiation. A Bi-directional Discrete Process Matching (Bi-DPM) network was adapted to establish reversible transformations between non-corrected (NASC-PET) and fully corrected (ASC-PET) images through discrete consistency constraints, eliminating the need for pseudo-CT generation or anatomical priors. Evaluated on 90 Parkinsonian syndrome patients, Bi-DPM demonstrated superior performance to Cycle-Consistent Generative Adversarial Networks (CycleGAN), Pix2Pix, and Rectified Flow (RF) across quantitative metrics (lower MAE, higher PSNR/SSIM). For standardized uptake value mean (SUVmean) measurements, Bi-DPM showed excellent agreement with CT-ASC reference (CCC > 0.98, PCC > 0.98). Voxel-wise analysis of DAT-positive/-negative (DAT+/DAT-) groups confirmed Bi-DPM's clinical validity, with statistical significance maps closely aligned to CT-ASC (Dice = 0.953 vs. 0.938 for RF, 0.948 for Pix2Pix and 0.618 for CycleGAN). This approach reduces unnecessary radiation exposure by omitting CT scans while maintaining PET quantification accuracy.