Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imaging
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910428550922240 |
|---|---|
| author | Zhu, Zheren Rehman, Azaan Cao, Xiaozhi Liao, Congyu Lee, Yoo Jin Ohliger, Michael Xue, Hui Yang, Yang |
| author_facet | Zhu, Zheren Rehman, Azaan Cao, Xiaozhi Liao, Congyu Lee, Yoo Jin Ohliger, Michael Xue, Hui Yang, Yang |
| contents | Recent developments in low-field (LF) magnetic resonance imaging (MRI) systems present remarkable opportunities for affordable and widespread MRI access. A robust denoising method to overcome the intrinsic low signal-noise-ratio (SNR) barrier is critical to the success of LF MRI. However, current data-driven MRI denoising methods predominantly handle magnitude images and rely on customized models with constrained data diversity and quantity, which exhibit limited generalizability in clinical applications across diverse MRI systems, pulse sequences, and organs. In this study, we present ImT-MRD: a complex-valued imaging transformer trained on a vast number of clinical MRI scans aiming at universal MR denoising at LF systems. Compared with averaging multiple-repeated scans for higher image SNR, the model obtains better image quality from fewer repetitions, demonstrating its capability for accelerating scans under various clinical settings. Moreover, with its complex-valued image input, the model can denoise intermediate results before advanced post-processing and prepare high-quality data for further MRI research. By delivering universal and accurate denoising across clinical and research tasks, our model holds great promise to expedite the evolution of LF MRI for accessible and equal biomedical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imaging Zhu, Zheren Rehman, Azaan Cao, Xiaozhi Liao, Congyu Lee, Yoo Jin Ohliger, Michael Xue, Hui Yang, Yang Image and Video Processing Medical Physics Recent developments in low-field (LF) magnetic resonance imaging (MRI) systems present remarkable opportunities for affordable and widespread MRI access. A robust denoising method to overcome the intrinsic low signal-noise-ratio (SNR) barrier is critical to the success of LF MRI. However, current data-driven MRI denoising methods predominantly handle magnitude images and rely on customized models with constrained data diversity and quantity, which exhibit limited generalizability in clinical applications across diverse MRI systems, pulse sequences, and organs. In this study, we present ImT-MRD: a complex-valued imaging transformer trained on a vast number of clinical MRI scans aiming at universal MR denoising at LF systems. Compared with averaging multiple-repeated scans for higher image SNR, the model obtains better image quality from fewer repetitions, demonstrating its capability for accelerating scans under various clinical settings. Moreover, with its complex-valued image input, the model can denoise intermediate results before advanced post-processing and prepare high-quality data for further MRI research. By delivering universal and accurate denoising across clinical and research tasks, our model holds great promise to expedite the evolution of LF MRI for accessible and equal biomedical applications. |
| title | Advancing low-field MRI with a universal denoising imaging transformer: Towards fast and high-quality imaging |
| topic | Image and Video Processing Medical Physics |
| url | https://arxiv.org/abs/2404.19167 |