Improving reference-driven undersampled MRI reconstruction via iterative data correction

Guisong Wang; Xiaofeng Du; Yifan He

doi:10.1117/12.2680530

27 June 2023 Improving reference-driven undersampled MRI reconstruction via iterative data correction

Guisong Wang, Xiaofeng Du, Yifan He

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127052J (2023) https://doi.org/10.1117/12.2680530
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

When large training datasets are unavailable in real clinical scenarios, researchers turn to unsupervised learning for under-sampled magnetic resonance image reconstruction. However, unsupervised learning methods suffer from insufficient a priori knowledge. We introduce self-consistency constraint with the calibration and acquisition data to tackle these issues. Specifically, we propose an iterative data correction operator to ensure high fidelity of the reconstructed MR data. Experiments shows that the method is flexible and can reconstruct data from arbitrary k-space sampling patterns and easily incorporates additional image priors.

Citation Download Citation

Guisong Wang, Xiaofeng Du, and Yifan He "Improving reference-driven undersampled MRI reconstruction via iterative data correction", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127052J (27 June 2023); https://doi.org/10.1117/12.2680530

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