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Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI

Seegoolam G, Schlemper J, Qin C, Price A, Hajnal J, Rueckert D (2019)

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Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11767 LNCS

Pages Range: 704-712

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322502

DOI: 10.1007/978-3-030-32251-9_77

Abstract

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.

Involved external institutions

How to cite

APA:

Seegoolam, G., Schlemper, J., Qin, C., Price, A., Hajnal, J., & Rueckert, D. (2019). Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 704-712). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Seegoolam, Gavin, et al. "Exploiting motion for deep learning reconstruction of extremely-undersampled dynamic MRI." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 704-712.

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