TY - GEN
T1 - Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI
AU - Seegoolam, Gavin
AU - Schlemper, Jo
AU - Qin, Chen
AU - Price, Anthony
AU - Hajnal, Jo
AU - Rueckert, Daniel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - The problem of accelerated acquisition for dynamic MRI has been recently tackled with deep learning techniques. However, current state-of-the-art approaches do not incorporate a strategy to exploit the full temporal information of the k-space acquisition which would aid in producing higher quality reconstructions. In this paper, we propose a novel method for exploiting the full temporal dynamics for dynamic MRI reconstructions. Specifically, motion estimates are derived from undersampled MRI sequences. These are used to fuse data along the entire temporal axis to produce a novel data-consistent motion-augmented cine (DC-MAC). This is generated and utilised within an end-to-end trainable deep learning framework for MRI reconstruction. In particular, we find that for aggressive acceleration rates of ×51.2 on our cardiac dataset, our method with 3-fold cross-validation, ME-CNN, outperforms the current widely-accepted state-of-the-art, DC-CNN, with an improvement of 12% and 16% in PSNR and SSIM respectively. We report an average PSNR of 27.3±2.5and SSIM of 0.776±0.054. We also explore the robustness of using ME-CNN for unseen, out-of-domain examples.
AB - The problem of accelerated acquisition for dynamic MRI has been recently tackled with deep learning techniques. However, current state-of-the-art approaches do not incorporate a strategy to exploit the full temporal information of the k-space acquisition which would aid in producing higher quality reconstructions. In this paper, we propose a novel method for exploiting the full temporal dynamics for dynamic MRI reconstructions. Specifically, motion estimates are derived from undersampled MRI sequences. These are used to fuse data along the entire temporal axis to produce a novel data-consistent motion-augmented cine (DC-MAC). This is generated and utilised within an end-to-end trainable deep learning framework for MRI reconstruction. In particular, we find that for aggressive acceleration rates of ×51.2 on our cardiac dataset, our method with 3-fold cross-validation, ME-CNN, outperforms the current widely-accepted state-of-the-art, DC-CNN, with an improvement of 12% and 16% in PSNR and SSIM respectively. We report an average PSNR of 27.3±2.5and SSIM of 0.776±0.054. We also explore the robustness of using ME-CNN for unseen, out-of-domain examples.
UR - http://www.scopus.com/inward/record.url?scp=85075683276&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32251-9_77
DO - 10.1007/978-3-030-32251-9_77
M3 - Conference contribution
AN - SCOPUS:85075683276
SN - 9783030322502
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 704
EP - 712
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -