Abstract
Attenuation correction (AC) is an essential process in Single Photon Emission Computed Tomography (SPECT) myocardial perfusion imaging (MPI), an established imaging method for assessing coronary artery disease. Conventional AC approaches typically require CT scans, supplementary hardware, intricate reconstruction, or segmentation processes, which can hinder their clinical applicability. Recently, deep learning (DL) techniques have emerged as alternatives, allowing for the direct learning of attenuation patterns from non-AC (NAC) imaging data. This review explores the existing literature on DL-based AC methods for SPECT MPI. We highlight high-performing models, including attention-gated U-Net conditional Generative Adversarial Networks (GANs), and evaluate their validation methods. Although significant advancements have been achieved, numerous challenges persist, which are thoroughly discussed.
