SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI Without Ground Truth

Weijie Gan, Yuyang Hu, Cihat Eldeniz, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov

ICCVW 2021 pp. 4031-4039

doi:10.1109/ICCVW54120.2021.00450 /iccvw/2021/gan2021iccvw-ssjircs/

Abstract

Parallel magnetic resonance imaging (MRI) is a widely-used technique that accelerates data collection by making use of the spatial encoding provided by multiple receiver coils. A key issue in parallel MRI is the estimation of coil sensitivity maps (CSMs) that are used for reconstructing a single high-quality image. This paper addresses this issue by developing SS-JIRCS, a new self-supervised model-based deep-learning (DL) method for image reconstruction that is equipped with automated CSM calibration. Our deep network consists of three types of modules: data-consistency, regularization, and CSM calibration. Unlike traditional supervised DL methods, these modules are directly trained on undersampled and noisy k-space data rather than on fully sampled high-quality ground truth. We present empirical results on simulated data that show the potential of the proposed method for achieving better performance than several baseline methods.

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Cite

Text

Gan et al. "SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI Without Ground Truth." IEEE/CVF International Conference on Computer Vision Workshops, 2021. doi:10.1109/ICCVW54120.2021.00450

Markdown

[Gan et al. "SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI Without Ground Truth." IEEE/CVF International Conference on Computer Vision Workshops, 2021.](https://mlanthology.org/iccvw/2021/gan2021iccvw-ssjircs/) doi:10.1109/ICCVW54120.2021.00450

BibTeX

@inproceedings{gan2021iccvw-ssjircs,
  title     = {{SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI Without Ground Truth}},
  author    = {Gan, Weijie and Hu, Yuyang and Eldeniz, Cihat and Liu, Jiaming and Chen, Yasheng and An, Hongyu and Kamilov, Ulugbek S.},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2021},
  pages     = {4031-4039},
  doi       = {10.1109/ICCVW54120.2021.00450},
  url       = {https://mlanthology.org/iccvw/2021/gan2021iccvw-ssjircs/}
}