Clinical Feasibility of Deep Learning–Based Attenuation Correction Models for Tl-201 Myocardial Perfusion SPECT

Abstract

Purpose We aimed to develop deep learning (DL)–based attenuation correction models for Tl-201 myocardial perfusion SPECT (MPS) images and evaluate their clinical feasibility. Patients and Methods We conducted a retrospective study of patients with suspected or known coronary artery disease. We proposed a DL-based image-to-image translation technique to transform non–attenuation-corrected images into CT-based attenuation-corrected (CTAC) images. The model was trained using a modified U-Net with structural similarity index (SSIM) loss and mean squared error (MSE) loss and compared with other models. Segment-wise analysis using a polar map and visual assessment for the generated attenuation-corrected (GENAC) images were also performed to evaluate clinical feasibility. Results This study comprised 657 men and 328 women (age, 65 ± 11 years). Among the various models, the modified U-Net achieved the highest performance with an average mean absolute error of 0.003, an SSIM of 0.990, and a peak signal-to-noise ratio of 33.658. The performance of the model was not different between the stress and rest datasets. In the segment-wise analysis, the myocardial perfusion of the inferior wall was significantly higher in GENAC images than in the non–attenuation-corrected images in both the rest and stress test sets (P < 0.05). In the visual assessment of patients with diaphragmatic attenuation, scores of 4 (similar to CTAC images) or 5 (indistinguishable from CTAC images) were assigned to most GENAC images (65/68). Conclusions Our clinically feasible DL-based attenuation correction models can replace the CT-based method in Tl-201 MPS, and it would be useful in case SPECT/CT is unavailable for MPS.