CTフリー脳PETにおける[11C]CFTの減衰および散乱補正のための双方向マッチングネットワーク

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 [¹¹C]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.