CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging
As a medical imaging method, magnetic resonance imaging (MRI) can provide good soft tissue contrast, but it is necessary to accelerate MRI scanning due to the long reconstruction time which results in serious artefacts of undersampled k-space data reconstruction. Existing methods still have various...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2025.2507923 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849325922811904000 |
|---|---|
| author | Jingyu Wang Sen Dong |
| author_facet | Jingyu Wang Sen Dong |
| author_sort | Jingyu Wang |
| collection | DOAJ |
| description | As a medical imaging method, magnetic resonance imaging (MRI) can provide good soft tissue contrast, but it is necessary to accelerate MRI scanning due to the long reconstruction time which results in serious artefacts of undersampled k-space data reconstruction. Existing methods still have various limitations in image fidelity and dependence on full sampled acquisition. This article proposes a reconstruction method based on cross-conditional diffusion model CobDiffMRI to address the issue of undersampled reconstruction artefacts, using undersampled k-space signals and artefact images as conditions, applying the conditional diffusion model to the training and reconstruction process of MRI imaging in which learn the diffusion process and image features through training. At the same time, a cross-regularisation loss function is designed to predict noise more reasonably and effectively. With a diffusion step of 2000, the artefacts caused by insufficient features under undersampled conditions is well optimised, which makes the goal clear and the features distinguishable, providing a new idea for the application of neural networks in MRI. |
| format | Article |
| id | doaj-art-8ca03bf9fca141abb96b782353690687 |
| institution | Kabale University |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-8ca03bf9fca141abb96b7823536906872025-08-20T03:48:18ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712025-12-0113110.1080/21681163.2025.2507923CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imagingJingyu Wang0Sen Dong1Astronaut center of China (ACC), Beijing, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi, ChinaAs a medical imaging method, magnetic resonance imaging (MRI) can provide good soft tissue contrast, but it is necessary to accelerate MRI scanning due to the long reconstruction time which results in serious artefacts of undersampled k-space data reconstruction. Existing methods still have various limitations in image fidelity and dependence on full sampled acquisition. This article proposes a reconstruction method based on cross-conditional diffusion model CobDiffMRI to address the issue of undersampled reconstruction artefacts, using undersampled k-space signals and artefact images as conditions, applying the conditional diffusion model to the training and reconstruction process of MRI imaging in which learn the diffusion process and image features through training. At the same time, a cross-regularisation loss function is designed to predict noise more reasonably and effectively. With a diffusion step of 2000, the artefacts caused by insufficient features under undersampled conditions is well optimised, which makes the goal clear and the features distinguishable, providing a new idea for the application of neural networks in MRI.https://www.tandfonline.com/doi/10.1080/21681163.2025.2507923MRIdiffusion modelk-spaceaccelerated MRI |
| spellingShingle | Jingyu Wang Sen Dong CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization MRI diffusion model k-space accelerated MRI |
| title | CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging |
| title_full | CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging |
| title_fullStr | CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging |
| title_full_unstemmed | CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging |
| title_short | CobDiffMRI: a cross-conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging |
| title_sort | cobdiffmri a cross conditional diffusion model for removing artefacts in undersampled magnetic resonance imaging |
| topic | MRI diffusion model k-space accelerated MRI |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2025.2507923 |
| work_keys_str_mv | AT jingyuwang cobdiffmriacrossconditionaldiffusionmodelforremovingartefactsinundersampledmagneticresonanceimaging AT sendong cobdiffmriacrossconditionaldiffusionmodelforremovingartefactsinundersampledmagneticresonanceimaging |