# Super-Resolution Deep Learning Reconstruction Improves Image Quality of Dynamic Myocardial Computed Tomography Perfusion Imaging

## TL;DR

This study shows that a deep-learning technique improves image quality in heart CT scans without changing blood flow measurements.

## Contribution

The study is the first to investigate the impact of SR-DLR on dynamic myocardial CT perfusion imaging.

## Key findings

- SR-DLR significantly improved image quality in dynamic CTP with higher contrast and sharpness scores.
- SR-DLR reduced image noise and improved SNR, CNR, and ERS compared to HIR.
- SR-DLR maintained mean CT-MBF values while reducing intra-patient variability.

## Abstract

Super-resolution deep-learning reconstruction (SR-DLR) has been reported to improve image quality in computed tomography (CT) imaging, including the evaluation of coronary artery stenosis and the luminal assessment of coronary stents. However, its potential impact on dynamic myocardial CT perfusion (CTP) imaging has not been investigated. In our results, SR-DLR significantly improved the image quality of dynamic CTP images and reduced intra-patient CT-derived myocardial blood flow variability without altering the mean values. These findings suggest that SR-DLR has the potential to be applied to myocardial CTP imaging and may be useful for the assessment of CTP data.

Background/Objectives: Super-resolution deep-learning reconstruction (SR-DLR) is an advanced image reconstruction technique, but its effect on dynamic myocardial computed tomography perfusion (CTP) imaging has not been evaluated. This study aimed to examine the impact of SR-DLR on image quality and perfusion parameters in dynamic myocardial CTP. Methods: Thirty-five patients who underwent dynamic myocardial CTP for coronary artery disease assessment were retrospectively analyzed. Two CTP datasets were reconstructed using hybrid iterative reconstruction (HIR) and SR-DLR. Image quality was compared qualitatively and quantitatively, including image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge rise slope (ERS). Equivalence of CT-derived myocardial blood flow (CT-MBF) between two reconstructions was tested using a previously reported 15% equivalence margin. Intra-patient variability of CT-MBF was evaluated using the robust coefficient of variation (rCV). Results: In the qualitative assessment, SR-DLR had significantly higher scores in contrast (4.0 vs. 2.0) and sharpness (4.5 vs. 2.5) compared with HIR (p < 0.001), while contrast scores were similar. In the quantitative assessment, SR-DLR demonstrated significantly lower image noise (19.4 vs. 29.4 HU), and improved SNR (6.1 vs. 4.1), CNR (13.7 vs. 10.9), and ERS (171.0 vs. 135.1 HU/mm) (all p < 0.001). Mean global CT-MBF was comparable (3.15 ± 0.91 mL/g/min for HIR vs. 3.18 ± 0.97 mL/g/min for SR-DLR) and equivalence was confirmed (p = 0.022). SR-DLR significantly reduced rCV compared with HIR (36.0% vs. 41.0%, p < 0.001). Conclusions: SR-DLR enhances image quality in dynamic myocardial CTP while maintaining mean global CT-MBF and reducing intra-patient variability.