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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08056 |
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| _version_ | 1866913729944223744 |
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| author | Mai, Zhongyu Zhan, Zewei Guo, Hanyu Huang, Yulang Su, Weifeng |
| author_facet | Mai, Zhongyu Zhan, Zewei Guo, Hanyu Huang, Yulang Su, Weifeng |
| contents | Magnetic resonance imaging (MRI) motion artifacts can seriously affect clinical diagnostics, making it challenging to interpret images accurately. Existing methods for eliminating motion artifacts struggle to retain fine structural details and simultaneously lack the necessary vividness and sharpness. In this study, we present a novel dual-domain optimization (DDO) approach that integrates information from the pixel and frequency domains guiding the recovery of clean magnetic resonance images through implicit neural representations(INRs). Specifically, our approach leverages the low-frequency components in the k-space as a reference to capture accurate tissue textures, while high-frequency and pixel information contribute to recover details. Furthermore, we design complementary masks and dynamic loss weighting transitioning from global to local attention that effectively suppress artifacts while retaining useful details for reconstruction. Experimental results on the NYU fastMRI dataset demonstrate that our method outperforms existing approaches in multiple evaluation metrics. Our code is available at https://anonymous.4open.science/r/DDO-IN-A73B. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08056 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DDO-IN: Dual Domains Optimization for Implicit Neural Network to Eliminate Motion Artifact in Magnetic Resonance Imaging Mai, Zhongyu Zhan, Zewei Guo, Hanyu Huang, Yulang Su, Weifeng Computer Vision and Pattern Recognition Magnetic resonance imaging (MRI) motion artifacts can seriously affect clinical diagnostics, making it challenging to interpret images accurately. Existing methods for eliminating motion artifacts struggle to retain fine structural details and simultaneously lack the necessary vividness and sharpness. In this study, we present a novel dual-domain optimization (DDO) approach that integrates information from the pixel and frequency domains guiding the recovery of clean magnetic resonance images through implicit neural representations(INRs). Specifically, our approach leverages the low-frequency components in the k-space as a reference to capture accurate tissue textures, while high-frequency and pixel information contribute to recover details. Furthermore, we design complementary masks and dynamic loss weighting transitioning from global to local attention that effectively suppress artifacts while retaining useful details for reconstruction. Experimental results on the NYU fastMRI dataset demonstrate that our method outperforms existing approaches in multiple evaluation metrics. Our code is available at https://anonymous.4open.science/r/DDO-IN-A73B. |
| title | DDO-IN: Dual Domains Optimization for Implicit Neural Network to Eliminate Motion Artifact in Magnetic Resonance Imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.08056 |