463-01-003 ISMRM Abstract

Comparative Evaluation of Retrospective Undersampling Strategies for Active Sampling in low-field 3D Cartesian MRI

Primary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling

Secondary: Acquisition & Reconstruction - Image Reconstruction: AI

463-01-003 · Data-Driven Reconstruction: From Networks to Clinical Practice · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row D

Keywords: Image Reconstruction Deep learning reconstruction Sampling pattern optimization Undersampling Optimization reconstruction

Accepted

Alexis Cogne¹, Guillaume Daval-Frérot¹, Zineb Belkacemi¹, Nina Gidel-Dissler¹, Romain Couvreur¹, Dimitri Labat ¹

¹Chipiron, Paris, France

Presenting Author: Dimitri Labat

Synopsis

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References

1. Knoll F, Clason C, Diwoky C, et al. Adapted random sampling patterns for accelerated MRI. Magnetic resonance materials in physics, biology and medicine, 2011, vol. 24, no 1, p. 43-50. PMID: 21213016 [pmid]

2. Liu D, Liang D, Liu X, et al. Under-sampling trajectory design for compressed sensing MRI. In : 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012. p. 73-76. https://doi.org/10.1109/EMBC.2012.6345874 [doi]

3. Sanchez T, Gözcü B, Van Heeswijk RB, et al. Scalable learning-based sampling optimization for compressive dynamic MRI. In : ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. p. 8584-8588. https://doi.org/10.1109/ICASSP40776.2020.9053345 [doi]

4. Zibetti M, Herman GT, Regatte RR. Fast data-driven learning of parallel MRI sampling patterns for large scale problems. Scientific Reports, 2021, vol. 11, no 1, p. 19312. https://doi.org/10.1038/s41598-021-97995-w [doi]

5. Lyu M, Mei L, Huang S, et al. M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research. Scientific Data, 2023, vol. 10, no 1, p. 264. https://doi.org/10.1038/s41597-023-02181-4 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/463-01-003