LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909312723451904 |
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| author | Aggarwal, Hemant Kumar Chatterjee, Sudhanya Shanbhag, Dattesh Patil, Uday Hari, K. V. S. |
| author_facet | Aggarwal, Hemant Kumar Chatterjee, Sudhanya Shanbhag, Dattesh Patil, Uday Hari, K. V. S. |
| contents | Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (SSFSE) MR imaging poses a challenge, as it introduces T2-blur in the acquired images. This study aims to enhance the reconstruction quality of SSFSE MR images by (a) optimizing the trajectory for measuring the k-space, (b) acquiring fewer samples to speed up the acquisition process, and (c) reducing the impact of T2-blur. The proposed method adheres to physics constraints due to maximum gradient strength and slew-rate available while optimizing the trajectory within an end-to-end learning framework. Experiments were conducted on publicly available fastMRI multichannel dataset with 8-fold and 16-fold acceleration factors. An experienced radiologist's evaluation on a five-point Likert scale indicates improvements in the reconstruction quality as the ACL fibers are sharper than comparative methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07457 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging Aggarwal, Hemant Kumar Chatterjee, Sudhanya Shanbhag, Dattesh Patil, Uday Hari, K. V. S. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (SSFSE) MR imaging poses a challenge, as it introduces T2-blur in the acquired images. This study aims to enhance the reconstruction quality of SSFSE MR images by (a) optimizing the trajectory for measuring the k-space, (b) acquiring fewer samples to speed up the acquisition process, and (c) reducing the impact of T2-blur. The proposed method adheres to physics constraints due to maximum gradient strength and slew-rate available while optimizing the trajectory within an end-to-end learning framework. Experiments were conducted on publicly available fastMRI multichannel dataset with 8-fold and 16-fold acceleration factors. An experienced radiologist's evaluation on a five-point Likert scale indicates improvements in the reconstruction quality as the ACL fibers are sharper than comparative methods. |
| title | LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07457 |