Rapid whole-heart CMR with single volume super-resolution
|Author:||Steeden, Jennifer A.1; Quail, Michael1,2; Gotschy, Alexander2,3;|
1UCL Centre for Cardiovascular Imaging, Institute of Cardiovascular Science, University College London, 30 Guildford Street, London, WC1N 1EH, UK
2Great Ormond Street Hospital, London, WC1N 3JH, UK
3Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
4Department of Computer Science, University College London, London, WC1E 6BT, UK
5Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 5.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020082563143
|Publish Date:|| 2020-08-25
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 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 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 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.
Journal of cardiovascular magnetic resonance
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
217 Medical engineering
3121 General medicine, internal medicine and other clinical medicine
JAS receives Royal Society-EPSRC funding; Dorothy Hodgkin Fellowship (DH130079), and UKRI funding; Future Leaders Fellowship (MR/S032290/1). AH is partially supported by the Academy of Finland (Project 312123). This work was supported in part by British Heart Foundation grant NH/18/1/33511.
|Academy of Finland Grant Number:||
312123 (Academy of Finland Funding decision)
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