University of Oulu

W. Peng, L. Feng, G. Zhao and F. Liu, "Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 20762-20771, doi: 10.1109/CVPR52688.2022.02013.

Learning optimal k-space acquisition and reconstruction using physics-informed neural networks

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Author: Peng, Wei1,2; Feng, Li3; Zhao, Guoying1;
Organizations: 1CMVS, University of Oulu
2Harvard Medical School
3Icahn School of Medicine at Mount Sinai
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.6 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-01-30


The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred the development of various acceleration methods, typically through heuristically undersampling the MRI measurement domain known as k-space. Recently, deep neural networks have been applied to reconstruct undersampled k-space data and have shown improved reconstruction performance. While most of these methods focus on designing novel reconstruction networks or new training strategies for a given undersampling pattern, e.g., Cartesian undersampling or Non-Cartesian sampling, to date, there is limited research aiming to learn and optimize k-space sampling strategies using deep neural networks. This work proposes a novel optimization framework to learn k-space sampling trajectories by considering it as an Ordinary Differential Equation (ODE) problem that can be solved using neural ODE. In particular, the sampling of k-space data is framed as a dynamic system, in which neural ODE is formulated to approximate the system with additional constraints on MRI physics. In addition, we have also demonstrated that trajectory optimization and image reconstruction can be learned collaboratively for improved imaging efficiency and reconstruction performance. Experiments were conducted on different in-vivo datasets (e.g., brain and knee images) acquired with different sequences. Initial results have shown that our proposed method can generate better image quality in accelerated MRI than conventional undersampling schemes in Cartesian and Non-Cartesian acquisitions.

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Series: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN: 1063-6919
ISSN-E: 2575-7075
ISSN-L: 1063-6919
ISBN: 978-1-6654-6946-3
ISBN Print: 978-1-6654-6947-0
Pages: 20762 - 20771
DOI: 10.1109/cvpr52688.2022.02013
Host publication: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference: IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: This work was supported by the Academy of Finland for ICT 2023 project (grant 328115), Academy Professor project EmotionAI (grants 336116, 345122), by Ministry of Education and Culture of Finland for AI forum project, and Infotech Oulu. As well, the authors wish to acknowledge CSC-IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 328115
Detailed Information: 328115 (Academy of Finland Funding decision)
336116 (Academy of Finland Funding decision)
345122 (Academy of Finland Funding decision)
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