Coil sketching for fast and efficient 4D lung MRI reconstruction

Motivation: Non-Cartesian 4D lung MRI is especially challenging due to large matrix sizes and cardio-respiratory motion, leading to high memory allocation and long image reconstruction times. To be clinically feasible, there is a need for faster and more computationally-efficient respiratory-resolved reconstructions. Goal(s): Extend highly-efficient coil sketching reconstruction to 4D MRI, and solve large respiratory-resolved low rank reconstruction problems within clinically feasible timeframes and within <24GB GPU memory. Approach: We developed and applied 4D coil sketching to low-rank and motion-compensated low-rank reconstruction for free-breathing Stack-of-Spirals pulmonary imaging. Results: Coil sketching provided >2-fold faster and 3-fold more memory-efficient reconstructions compared to conventional methods. Impact: 4D coil sketching enables <5 minute reconstructions for high-resolution respiratory-resolved lung imaging, without the need for server-grade GPUs. This approach is ideal for free-breathing lung MRI applications, like phase-resolved functional-lung (PREFUL) MRI, that demand extensive oversampling and high temporal resolution.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/0031
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Coil sketching for fast and efficient 4D lung MRI reconstruction

Pierre Daudé, Rajiv Ramasawmy, Joel Moss, Ahsan Javed et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

Coil sketching for fast and efficient 4D lung MRI reconstruction

Pierre Daudé, Rajiv Ramasawmy, Joel Moss, Ahsan Javed, Adrienne E. Campbell‐Washburn, Joseph Plummer, Anastastia Tsakirellis
article en

Abstract

Motivation: Non-Cartesian 4D lung MRI is especially challenging due to large matrix sizes and cardio-respiratory motion, leading to high memory allocation and long image reconstruction times. To be clinically feasible, there is a need for faster and more computationally-efficient respiratory-resolved reconstructions. Goal(s): Extend highly-efficient coil sketching reconstruction to 4D MRI, and solve large respiratory-resolved low rank reconstruction problems within clinically feasible timeframes and within <24GB GPU memory. Approach: We developed and applied 4D coil sketching to low-rank and motion-compensated low-rank reconstruction for free-breathing Stack-of-Spirals pulmonary imaging. Results: Coil sketching provided >2-fold faster and 3-fold more memory-efficient reconstructions compared to conventional methods. Impact: 4D coil sketching enables <5 minute reconstructions for high-resolution respiratory-resolved lung imaging, without the need for server-grade GPUs. This approach is ideal for free-breathing lung MRI applications, like phase-resolved functional-lung (PREFUL) MRI, that demand extensive oversampling and high temporal resolution.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Radiomics and Machine Learning in Medical Imaging
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