Rapid whole-heart CMR with single volume super-resolution

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 highresolution 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 500 high-resolution WH-bSSFP images by simulating 50% slice resolution and 50% phase resolution. The trained network was validated with 25 synthetic test data sets. Additionally, prospective low-resolution data and high-resolution data were acquired in 40 patients. In the prospective data, vessel diameters, quantitative and qualitative image quality, and diagnostic scoring was compared between the low-resolution, super-resolution and reference high-resolution WH-bSSFP data. Results: The synthetic test data showed a significant increase in image quality of the low-resolution images after super-resolution reconstruction. Prospectively acquired low-resolution data was acquired~× 3 faster than the prospective high-resolution data (173 s vs 488 s). Super-resolution reconstruction of the low-resolution data took < 1 s per volume. 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 lowresolution 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 highresolution data. No significant differences and no bias was found in the super-resolution measurements in any of the great vessels. However, a small but significant for the underestimation was found in the proximal left coronary artery diameter measurement from super-resolution data. Diagnostic scoring showed that although super-resolution did not improve accuracy of diagnosis, it did improve diagnostic confidence compared to low-resolution imaging. 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. We were able to train the network using synthetic training data from retrospective high-resolution whole heart data. 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.

Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning

“…In addition, the network is residual and only generates sparse feature differences, reducing the potential for hallucination 31 . Finally, we believe that the network is more likely to rely on general features than specific anatomies, as previously suggested in networks with related architectures 23 . Nevertheless, future work is required to expand the diagnoses of the patient population and evaluate different vessels such as coronary and renal arteries, as well as patients with implanted devices.…”

“…In this study, we chose to use an architecture based on the U‐Net, which was originally introduced for semantic segmentation 13 . U‐Net‐like architectures have become popular and have demonstrated good performance in a variety of problems including image reconstruction and processing 23–25 . Their power seems to lie in the ability to integrate high‐level semantic information with spatial information 26 .…”

“…13 U-Net-like architectures have become popular and have demonstrated good performance in a variety of problems including image reconstruction and processing. [23][24][25] Their power seems to lie in the ability to integrate high-level semantic information with spatial information. 26 We believe that enhancing contrast also requires these characteristics and that U-Nets are well suited to this problem.…”

“…31 Finally, we believe that the network is more likely to rely on general features than specific anatomies, as previously suggested in networks with related architectures. 23 Nevertheless, future work is required to expand the diagnoses of the patient population and evaluate different vessels such as coronary and renal arteries, as well as patients with implanted devices. Another possible limitation is the experimental design of the prospective data acquisition, where two angiograms were acquired during the same examination.…”

Machine learning in Magnetic Resonance Imaging: Image reconstruction

“…In another clinical validation paper, vessel diameters, diagnostic accuracy and diagnostic confidence were assessed from 3D whole-heart images with a single volume super-resolution ML reconstruction (see section 2.1) [27]. Prospective data was acquired in 40 patients with CHD, and compared to results from clinical gold-standard images.…”