Reconstructing degraded SPECT myocardial images via deep biophysical models: A modern computational approach
DOI:
https://doi.org/10.54939/1859-1043.j.mst.106.2025.55-62Keywords:
Single-photon emission computed tomography; Myocardial perfusion imaging; Computer-aided diagnosis; Generative adversarial network; Diffusion model.Abstract
Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a critical tool for diagnosing coronary artery disease, but it is often affected by signal degradation due to soft tissue attenuation. In this study, we utilize a publicly available SPECT MPI dataset to establish a benchmark for the task of attenuation correction (AC) by reconstructing AC images from non-attenuation corrected (NC) inputs in a 2D slice-to-slice manner. We implement and compare the performance of several advanced generative models, including generative adversarial networks (GANs) and diffusion models. These models are trained on both general-domain and medical-domain data to evaluate their reconstruction capabilities. The results show that modern deep learning approaches can effectively generate high-quality AC images, demonstrating promising potential for integration into computer-aided diagnosis (CAD) systems for SPECT MPI.
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