Rapid Whole-Heart CMR with Single Volume Super-resolution

TL;DR

This study introduces a deep learning-based super-resolution method using a residual U-Net to rapidly enhance low-resolution whole-heart MRI images, significantly improving image quality and measurement accuracy in clinical settings.

Contribution

The paper presents a novel application of a residual U-Net for super-resolution reconstruction of rapid, low-resolution whole-heart MRI data, enabling faster imaging without sacrificing quality.

Findings

Super-resolved images showed improved edge sharpness and fewer artifacts.

Quantitative analysis indicated no bias in super-resolution measurements.

Super-resolution achieved better image quality than low-resolution images, comparable to high-resolution data.

Abstract

Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed ups using a deep learning single volume super resolution reconstruction, to recover high resolution features from rapidly acquired low resolution WH-bSSFP images. Methods: A 3D residual U-Net was trained using synthetic data, created from a library of high-resolution WH-bSSFP images by simulating 0.5 slice resolution and 0.5 phase resolution. The trained network was validated with synthetic test data, as well as prospective low-resolution data. Results: Synthetic low-resolution data had significantly better image quality after super-resolution reconstruction. Qualitative image scores showed super-resolved images had better edge sharpness, fewer residual artefacts and less image distortion than low-resolution images, with similar scores to high-resolution data. Quantitative image scores showed super-resolved images had significantly better edge sharpness than low-resolution or high-resolution images, with significantly better signal-to-noise ratio than high-resolution data. Vessel diameters measurements showed over-estimation in the low-resolution measurements, compared to the high-resolution data. No significant differences and no bias was found in the super-resolution measurements. Conclusion: This paper demonstrates the potential of using a residual U-Net for super-resolution reconstruction of rapidly acquired low-resolution whole heart bSSFP data within a clinical setting. The resulting network can be applied very quickly, making these techniques particularly appealing within busy clinical workflow. Thus, we believe that this technique may help speed up whole heart CMR in clinical practice.