Affiliation:
1. Department of Biomedical Systems Informatics, Yonsei University, Seoul
2. Department of Nuclear Medicine and Molecular Imaging, Ajou University School of Medicine, Suwon, South Korea.
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.
Publisher
Ovid Technologies (Wolters Kluwer Health)