Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909464060231680 |
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| author | Huang, Shoujin Luo, Guanxiong Wang, Xi Chen, Ziran Wang, Yuwan Yang, Huaishui Heng, Pheng-Ann Zhang, Lingyan Lyu, Mengye |
| author_facet | Huang, Shoujin Luo, Guanxiong Wang, Xi Chen, Ziran Wang, Yuwan Yang, Huaishui Heng, Pheng-Ann Zhang, Lingyan Lyu, Mengye |
| contents | In general, diffusion model-based MRI reconstruction methods incrementally remove artificially added noise while imposing data consistency to reconstruct the underlying images. However, real-world MRI acquisitions already contain inherent noise due to thermal fluctuations. This phenomenon is particularly notable when using ultra-fast, high-resolution imaging sequences for advanced research, or using low-field systems favored by low- and middle-income countries. These common scenarios can lead to sub-optimal performance or complete failure of existing diffusion model-based reconstruction techniques. Specifically, as the artificially added noise is gradually removed, the inherent MRI noise becomes increasingly pronounced, making the actual noise level inconsistent with the predefined denoising schedule and consequently inaccurate image reconstruction. To tackle this problem, we propose a posterior sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC) operation. Extensive experiments are conducted on two public datasets and an in-house clinical dataset with field strength ranging from 0.3T to 3T, showing that our method surpasses the state-of-the-art MRI reconstruction methods, and is highly robust against various noise levels. The code for Nila is available at https://github.com/Solor-pikachu/Nila. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI Huang, Shoujin Luo, Guanxiong Wang, Xi Chen, Ziran Wang, Yuwan Yang, Huaishui Heng, Pheng-Ann Zhang, Lingyan Lyu, Mengye Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition In general, diffusion model-based MRI reconstruction methods incrementally remove artificially added noise while imposing data consistency to reconstruct the underlying images. However, real-world MRI acquisitions already contain inherent noise due to thermal fluctuations. This phenomenon is particularly notable when using ultra-fast, high-resolution imaging sequences for advanced research, or using low-field systems favored by low- and middle-income countries. These common scenarios can lead to sub-optimal performance or complete failure of existing diffusion model-based reconstruction techniques. Specifically, as the artificially added noise is gradually removed, the inherent MRI noise becomes increasingly pronounced, making the actual noise level inconsistent with the predefined denoising schedule and consequently inaccurate image reconstruction. To tackle this problem, we propose a posterior sampling strategy with a novel NoIse Level Adaptive Data Consistency (Nila-DC) operation. Extensive experiments are conducted on two public datasets and an in-house clinical dataset with field strength ranging from 0.3T to 3T, showing that our method surpasses the state-of-the-art MRI reconstruction methods, and is highly robust against various noise levels. The code for Nila is available at https://github.com/Solor-pikachu/Nila. |
| title | Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRI |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.05245 |