Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911151863889920 |
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| author | Shimron, Efrat Shan, Shanshan Grover, James Koonjoo, Neha Shen, Sheng Boele, Thomas Sorby-Adams, Annabel J. Kirsch, John E. Rosen, Matthew S. Waddington, David E. J. |
| author_facet | Shimron, Efrat Shan, Shanshan Grover, James Koonjoo, Neha Shen, Sheng Boele, Thomas Sorby-Adams, Annabel J. Kirsch, John E. Rosen, Matthew S. Waddington, David E. J. |
| contents | Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan durations. Hence, methods for accelerating acquisition and boosting image quality are critically important to enable clinically actionable, high-quality imaging in these systems. Despite the role that compressed sensing (CS) and deep learning (DL)-based methods have played in improving image quality for high-field MRI, their adoption for low-field imaging is still in its infancy, and it remains unclear how robust these methods are in low-SNR regimes.
Here, we propose, investigate, and compare four reconstruction approaches: (i) L1-wavelet CS; (ii) a data-driven network; (iii) an unrolled network; and (iv) a Swin Transformer Cascade. We evaluate their performance across a range of SNR values using publicly available datasets and ultra-low field (6.5 mT) MRI data. Our results show that the unrolled network and Swin Transformer cascade outperform CS and data-driven models. While transformer-based models achieve the highest performance at high SNR, unrolled convolution-based networks are more robust in ultra-low SNR settings and often outperform transformers, indicating that simpler DL architectures may be better suited to low-field MRI.
This work highlights both the potential and limitations of advanced reconstruction techniques in low-field MRI and pinpoints effective DL strategies for addressing SNR challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06704 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers Shimron, Efrat Shan, Shanshan Grover, James Koonjoo, Neha Shen, Sheng Boele, Thomas Sorby-Adams, Annabel J. Kirsch, John E. Rosen, Matthew S. Waddington, David E. J. Medical Physics Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long scan durations. Hence, methods for accelerating acquisition and boosting image quality are critically important to enable clinically actionable, high-quality imaging in these systems. Despite the role that compressed sensing (CS) and deep learning (DL)-based methods have played in improving image quality for high-field MRI, their adoption for low-field imaging is still in its infancy, and it remains unclear how robust these methods are in low-SNR regimes. Here, we propose, investigate, and compare four reconstruction approaches: (i) L1-wavelet CS; (ii) a data-driven network; (iii) an unrolled network; and (iv) a Swin Transformer Cascade. We evaluate their performance across a range of SNR values using publicly available datasets and ultra-low field (6.5 mT) MRI data. Our results show that the unrolled network and Swin Transformer cascade outperform CS and data-driven models. While transformer-based models achieve the highest performance at high SNR, unrolled convolution-based networks are more robust in ultra-low SNR settings and often outperform transformers, indicating that simpler DL architectures may be better suited to low-field MRI. This work highlights both the potential and limitations of advanced reconstruction techniques in low-field MRI and pinpoints effective DL strategies for addressing SNR challenges. |
| title | Accelerating Low-field MRI: From Compressed Sensing to Deep Learning Reconstruction with CNNs and Transformers |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2411.06704 |