TY - GEN
T1 - Motion-Guided Physics-Based Learning for Cardiac MRI Reconstruction
AU - Hammernik, Kerstin
AU - Pan, Jiazhen
AU - Rueckert, Daniel
AU - Kustner, Thomas
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this work, we propose a robust learning-based cardiac motion estimation framework, to estimate non-rigid cardiac motion fields from undersampled cardiac data. Our proposed frameworks leverages the advantages of a lightweight motion estimation network and a combination of photometric and smoothness losses. This framework enables the prediction of cardiac motion fields to further improve on the downstream task of motion-compensated image reconstruction. We evaluate our motion estimation framework qualitatively and quantitatively on 41 in-house acquired 2D cardiac CINE MRIs. Our proposed method provides quantitatively competitive results to state-of-the art methods in motion estimation, and superior results in image reconstruction in terms of structural similarity metric and peak-signal-to-noise ratio. Furthermore, our frameworks allows for ~3500x faster motion estimation compared to state-of-the-art approaches, opening up the practical application potential for motion-guided physics-based image reconstruction.
AB - In this work, we propose a robust learning-based cardiac motion estimation framework, to estimate non-rigid cardiac motion fields from undersampled cardiac data. Our proposed frameworks leverages the advantages of a lightweight motion estimation network and a combination of photometric and smoothness losses. This framework enables the prediction of cardiac motion fields to further improve on the downstream task of motion-compensated image reconstruction. We evaluate our motion estimation framework qualitatively and quantitatively on 41 in-house acquired 2D cardiac CINE MRIs. Our proposed method provides quantitatively competitive results to state-of-the art methods in motion estimation, and superior results in image reconstruction in terms of structural similarity metric and peak-signal-to-noise ratio. Furthermore, our frameworks allows for ~3500x faster motion estimation compared to state-of-the-art approaches, opening up the practical application potential for motion-guided physics-based image reconstruction.
KW - cardiac
KW - deep learning
KW - image reconstruction
KW - magnetic resonance imaging
KW - motion estimation
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85127036304&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF53345.2021.9723134
DO - 10.1109/IEEECONF53345.2021.9723134
M3 - Conference contribution
AN - SCOPUS:85127036304
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 900
EP - 907
BT - 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Y2 - 31 October 2021 through 3 November 2021
ER -