SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
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arXiv
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| Format: | Preprint |
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2023
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| author | Cui, Zhuo-Xu Cao, Chentao Wang, Yue Jia, Sen Cheng, Jing Liu, Xin Zheng, Hairong Liang, Dong Zhu, Yanjie |
| author_facet | Cui, Zhuo-Xu Cao, Chentao Wang, Yue Jia, Sen Cheng, Jing Liu, Xin Zheng, Hairong Liang, Dong Zhu, Yanjie |
| contents | Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivity maps (CSMs). k-space interpolation methods can effectively address this issue but conventional diffusion models are not readily applicable in k-space interpolation. To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method. Specifically, we utilize the iterative solver of the self-consistent term (i.e., k-space physical prior) in SPIRiT to formulate a novel stochastic differential equation (SDE) governing the diffusion process. Subsequently, k-space data can be interpolated by executing the diffusion process. This innovative approach highlights the optimization model's role in designing the SDE in diffusion models, enabling the diffusion process to align closely with the physics inherent in the optimization model, a concept referred to as model-driven diffusion. We evaluated the proposed SPIRiT-Diffusion method using a 3D joint intracranial and carotid vessel wall imaging dataset. The results convincingly demonstrate its superiority over image-domain reconstruction methods, achieving high reconstruction quality even at a substantial acceleration rate of 10. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_05060 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI Cui, Zhuo-Xu Cao, Chentao Wang, Yue Jia, Sen Cheng, Jing Liu, Xin Zheng, Hairong Liang, Dong Zhu, Yanjie Computer Vision and Pattern Recognition Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivity maps (CSMs). k-space interpolation methods can effectively address this issue but conventional diffusion models are not readily applicable in k-space interpolation. To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method. Specifically, we utilize the iterative solver of the self-consistent term (i.e., k-space physical prior) in SPIRiT to formulate a novel stochastic differential equation (SDE) governing the diffusion process. Subsequently, k-space data can be interpolated by executing the diffusion process. This innovative approach highlights the optimization model's role in designing the SDE in diffusion models, enabling the diffusion process to align closely with the physics inherent in the optimization model, a concept referred to as model-driven diffusion. We evaluated the proposed SPIRiT-Diffusion method using a 3D joint intracranial and carotid vessel wall imaging dataset. The results convincingly demonstrate its superiority over image-domain reconstruction methods, achieving high reconstruction quality even at a substantial acceleration rate of 10. |
| title | SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2304.05060 |