Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom

Background

Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA.

Methods

A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale.

Results

The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively).

Conclusions

2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

Advancements in non-invasive imaging of atherosclerosis: Future perspectives

Atherosclerosis is a chronic inflammatory disease characterized by the buildup of plaques in arterial walls, leading to cardiovascular diseases and high morbidity and mortality rates worldwide. Non-invasive imaging techniques play a crucial role in evaluating patients with suspected or established atherosclerosis. However, there is a growing body of evidence suggesting the need to visualize the underlying processes of plaque progression and rupture to enhance risk stratification. This review explores recent advancements in non-invasive assessment of atherosclerosis, focusing on computed tomography, magnetic resonance imaging, and nuclear imaging. These advancements provide valuable insights into the assessment and management of atherosclerosis, potentially leading to better risk stratification and improved patient outcomes.