Self-supervised learning-based diffeomorphic non-rigid motion estimation for fast motion-compensated coronary MR angiography
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Date
2022
Journal Title
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ELSEVIER SCIENCE INC
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.
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.
Description
Keywords
Coronary MR angiography, deep learning, 3D whole-heart, motion estimation, motion correction, IMAGE REGISTRATION