Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute

Abstract

Purpose To develop and evaluate a novel and generalizable super‐resolution (SR) deep‐learning framework for motion‐compensated isotropic 3D coronary MR angiography (CMRA), which allows free‐breathing acquisitions in less than a minute. Methods Undersampled motion‐corrected reconstructions have enabled free‐breathing isotropic 3D CMRA in ~5‐10 min acquisition times. In this work, we propose a deep‐learning–based SR framework, combined with non‐rigid respiratory motion compensation, to shorten the acquisition time to less than 1 min. A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. A 16‐fold increase in spatial resolution is achieved by reconstructing a high‐resolution (HR) isotropic CMRA (0.9 mm 3 or 1.2 mm 3 ) from a low‐resolution (LR) anisotropic CMRA (0.9 × 3.6 × 3.6 mm 3 or 1.2 × 4.8 × 4.8 mm 3 ). The impact and generalization of the proposed SRGAN approach to different input resolutions and operation on image and patch‐level is investigated. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR‐CMRA in ~50 s under free‐breathing. Vessel sharpness and length of the coronary arteries from the SR‐CMRA is compared against the HR‐CMRA. Results SR‐CMRA showed statistically significant ( P < .001) improved vessel sharpness 34.1% ± 12.3% and length 41.5% ± 8.1% compared with LR‐CMRA. Good generalization to input resolution and image/patch‐level processing was found. SR‐CMRA enabled recovery of coronary stenosis similar to HR‐CMRA with comparable qualitative performance. Conclusion The proposed SR‐CMRA provides a 16‐fold increase in spatial resolution with comparable image quality to HR‐CMRA while reducing the predictable scan time to <1 min.

Topics & Concepts