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Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction

Schlemper J, Salehi SSM, Kundu P, Lazarus C, Dyvorne H, Rueckert D, Sofka M (2019)

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Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11766 LNCS

Pages Range: 57-64

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: 9783030322472

DOI: 10.1007/978-3-030-32248-9_7

Abstract

Deep learning for accelerated magnetic resonance (MR) image reconstruction is a fast growing field, which has so far shown promising results. However, most works are limited in the sense that they assume equidistant rectilinear (Cartesian) data acquisition in 2D or 3D. In practice, a reconstruction from nonuniform samplings such as radial and spiral is an attractive choice for more efficient acquisitions. Nevertheless, it has less been explored as the reconstruction process is complicated by the necessity to handle non-Cartesian samples. In this work, we present a novel approach for reconstructing from nonuniform undersampled MR data. The proposed approach, termed nonuniform variational network (NVN), is a convolutional neural network architecture based on the unrolling of a traditional iterative nonlinear reconstruction, where the knowledge of the nonuniform forward and adjoint sampling operators are efficiently incorporated. Our extensive evaluation shows that the proposed method outperforms existing state-of-the-art deep learning methods, hence offering a method that is widely applicable to different imaging protocols for both research and clinical deployments.

Involved external institutions

How to cite

APA:

Schlemper, J., Salehi, S.S.M., Kundu, P., Lazarus, C., Dyvorne, H., Rueckert, D., & Sofka, M. (2019). Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction. 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. 57-64). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Schlemper, Jo, et al. "Nonuniform Variational Network: Deep Learning for Accelerated Nonuniform MR Image Reconstruction." 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. 57-64.

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