Enhancing CCTA image quality: a review of deep learning approaches for advanced artifact correction and denoising

Abstract

Cardiac imaging is vital for diagnosing coronary artery disease (CAD), with coronary computed tomography angiography (CCTA) being commonly used to evaluate coronary vessels for stenosis, calcification, and atherosclerosis. However, CCTA images often suffer from artifacts like beam hardening, scatter, and noise, degrading image quality and obscuring anatomical details, leading to diagnostic uncertainty. Conventional post-processing techniques, such as filtered back projection and iterative reconstruction, have limited effectiveness in correcting these artifacts, posing challenges in CCTA, where precise visualization of coronary arteries is crucial. Artifacts can blur vessel boundaries, obscure calcified plaques, and misrepresent stenosis severity, potentially leading to misdiagnosis and suboptimal clinical decisions. Recent advancements in computational imaging, particularly deep learning algorithms, offer clinical benefits for artifact reduction in CCTA. Deep learning models, such as convolutional neural networks (CNNs), outperform traditional methods by effectively de-noising and correcting artifacts through learning complex patterns from large datasets. These models adapt to the non-linear, heterogeneous nature of artifacts, enhancing image clarity and diagnostic reliability. Improved image quality in CCTA enables better visualization of coronary arteries, aiding in accurate assessment of stenosis and calcification. This review highlights deep learning approaches for artifact correction in CCTA, emphasizing their potential to improve CAD diagnosis.