Abstract

--- - "Purpose: To accelerate non-rigid motion corrected coronary MR angiography (CMRA) reconstruction by developing a deep learning based non-rigid motion estimation network and combining this with an efficient implementation of the undersampled motion corrected reconstruction." - "Methods: Undersampled and respiratory motion corrected CMRA with overall short scans of 5 to 10 min have been recently proposed. However, image reconstruction with this approach remains lengthy, since it relies on several non-rigid image registrations to estimate the respiratory motion and on a subsequent iterative optimization to correct for motion during the undersampled reconstruction. Here we introduce a self-supervised diffeomorphic non-rigid respiratory motion estimation network, DiRespME-net, to speed up respiratory motion estimation. We couple this with an efficient GPU-based implementation of the subsequent motion-corrected iterative reconstruction. DiRespME-net is based on a U-Net architecture, and is trained in a self-supervised fashion, with a loss enforcing image similarity and spatial smoothness of the motion fields. Motion predicted by DiRespME-net was used for GPU-based motion-corrected CMRA in 12 test subjects and final images were compared to those produced by state-of-the-art reconstruction. Vessel sharpness and visible length of the right coronary artery (RCA) and the left anterior descending (LAD) coronary artery were used as metrics of image quality for comparison." - "Results: No statistically significant difference in image quality was found between images reconstructed with the proposed approach (MC:DiRespME-net) and a motion-corrected reconstruction using cubic B-splines (MC:Niftyreg). Visible vessel length was not significantly different between methods (RCA: MC:Nifty-reg 5.7 +/- 1.7 cm vs MC:DiRespME-net 5.8 +/- 1.7 cm, P = 0.32; LAD: MC:Nifty-reg 7.0 +/- 2.6 cm vs MC:DiRespME-net 6.9 +/- 2.7 cm, P = 0.81). Similarly, no statistically significant difference between methods was observed in terms of vessel sharpness (RCA: MC:Nifty-reg 60.3 +/- 7.2% vs MC:DiRespME-net 61.0 +/- 6.8%, P = 0.19; LAD: MC:Nifty-reg 57.4 +/- 7.9% vs MC:DiRespME-net 58.1 +/- 7.5%, P = 0.27). The proposed approach achieved a 50-fold reduction in computation time, resulting in a total reconstruction time of approximately 20 s." - "Conclusions: The proposed self-supervised learning-based motion corrected reconstruction enables fast motion corrected CMRA image reconstruction, holding promise for integration in clinical routine."