@inbook{37ecf23a2f2a49efad1f573ba0b500c2,
title = "Accelerated 4D Respiratory Motion-Resolved Cardiac MRI with a Model-Based Variational Network",
abstract = "Respiratory motion and long scan times remain major challenges in free-breathing 3D cardiac MRI. Respiratory motion-resolved approaches have been proposed by binning the acquired data to different respiratory motion states. After inter-bin motion estimation, motion-compensated reconstruction can be obtained. However, respiratory bins from accelerated acquisitions are highly undersampled and have different undersampling patterns depending on the subject-specific respiratory motion. Remaining undersampling artifacts in the bin images can influence the accuracy of the motion estimation. We propose a model-based variational network (VN) which reconstructs motion-resolved images jointly by exploiting shared information between respiratory bins. In each stage of VN, conjugate gradient is adopted to enforce data-consistency (CG-VN), achieving better enforcement of data consistency per stage than the classic VN with proximal gradient descent step (GD-VN), translating to faster convergence and better reconstruction performance. We compare the performance of CG-VN and GD-VN for reconstruction of respiratory motion-resolved images for two different cardiac MR sequences. Our results show that CG-VN with less stages outperforms GD-VN by achieving higher PSNR and better generalization on prospectively undersampled data. The proposed motion-resolved CG-VN provides consistently good reconstruction quality for all motion states with varying undersampling patterns by taking advantage of redundancies among motion bins.",
keywords = "Model-based Deep-learning, Motion-resolved MRI reconstruction, Variational network",
author = "Haikun Qi and Niccolo Fuin and Thomas Kuestner and Ren{\'e} Botnar and Claudia Prieto",
year = "2020",
doi = "10.1007/978-3-030-59725-2_41",
language = "English",
isbn = "9783030597245",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "427--435",
editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",
address = "Germany",
note = "23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",
}