A survey of emerging applications of diffusion probabilistic models in MRI
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest. Despite their huge computational burdens due to the large number of steps involved during sampling, DPMs are widely app...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2024-06-01
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| Series: | Meta-Radiology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2950162824000353 |
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| author | Yuheng Fan Hanxi Liao Shiqi Huang Yimin Luo Huazhu Fu Haikun Qi |
| author_facet | Yuheng Fan Hanxi Liao Shiqi Huang Yimin Luo Huazhu Fu Haikun Qi |
| author_sort | Yuheng Fan |
| collection | DOAJ |
| description | Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest. Despite their huge computational burdens due to the large number of steps involved during sampling, DPMs are widely appreciated in various medical imaging tasks for their high-quality and diversity of generation. Magnetic resonance imaging (MRI) is an important medical imaging modality with excellent soft tissue contrast and superb spatial resolution, which possesses unique opportunities for DPMs. Although there is a recent surge of studies exploring DPMs in MRI, a survey paper of DPMs specifically designed for MRI applications is still lacking. This review article aims to help researchers in the MRI community to grasp the advances of DPMs in different applications. We first introduce the theory of two dominant kinds of DPMs, categorized according to whether the diffusion time step is discrete or continuous, and then provide a comprehensive review of emerging DPMs in MRI, including reconstruction, image generation, image translation, segmentation, anomaly detection, and further research topics. Finally, we discuss the general limitations as well as limitations specific to the MRI tasks of DPMs and point out potential areas that are worth further exploration. |
| format | Article |
| id | doaj-art-eccced6f2e544386b77ec5c3f8c3b42b |
| institution | Kabale University |
| issn | 2950-1628 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Meta-Radiology |
| spelling | doaj-art-eccced6f2e544386b77ec5c3f8c3b42b2024-11-12T05:22:43ZengKeAi Communications Co., Ltd.Meta-Radiology2950-16282024-06-0122100082A survey of emerging applications of diffusion probabilistic models in MRIYuheng Fan0Hanxi Liao1Shiqi Huang2Yimin Luo3Huazhu Fu4Haikun Qi5School of Biomedical Engineering, ShanghaiTech University, Pudong, Shanghai, 201210, ChinaSchool of Biomedical Engineering, ShanghaiTech University, Pudong, Shanghai, 201210, ChinaSchool of Biomedical Engineering, ShanghaiTech University, Pudong, Shanghai, 201210, ChinaSchool of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, London, SE1 7EH, UKInstitute of High Performance Computing, Agency for Science, Technology, and Research (A∗STAR), 201210, SingaporeSchool of Biomedical Engineering, ShanghaiTech University, Pudong, Shanghai, 201210, China; Corresponding author.Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and a gradual sampling process to synthesize data, have gained increasing research interest. Despite their huge computational burdens due to the large number of steps involved during sampling, DPMs are widely appreciated in various medical imaging tasks for their high-quality and diversity of generation. Magnetic resonance imaging (MRI) is an important medical imaging modality with excellent soft tissue contrast and superb spatial resolution, which possesses unique opportunities for DPMs. Although there is a recent surge of studies exploring DPMs in MRI, a survey paper of DPMs specifically designed for MRI applications is still lacking. This review article aims to help researchers in the MRI community to grasp the advances of DPMs in different applications. We first introduce the theory of two dominant kinds of DPMs, categorized according to whether the diffusion time step is discrete or continuous, and then provide a comprehensive review of emerging DPMs in MRI, including reconstruction, image generation, image translation, segmentation, anomaly detection, and further research topics. Finally, we discuss the general limitations as well as limitations specific to the MRI tasks of DPMs and point out potential areas that are worth further exploration.http://www.sciencedirect.com/science/article/pii/S2950162824000353Diffusion probabilistic modelsScore based generative modelingMRI |
| spellingShingle | Yuheng Fan Hanxi Liao Shiqi Huang Yimin Luo Huazhu Fu Haikun Qi A survey of emerging applications of diffusion probabilistic models in MRI Meta-Radiology Diffusion probabilistic models Score based generative modeling MRI |
| title | A survey of emerging applications of diffusion probabilistic models in MRI |
| title_full | A survey of emerging applications of diffusion probabilistic models in MRI |
| title_fullStr | A survey of emerging applications of diffusion probabilistic models in MRI |
| title_full_unstemmed | A survey of emerging applications of diffusion probabilistic models in MRI |
| title_short | A survey of emerging applications of diffusion probabilistic models in MRI |
| title_sort | survey of emerging applications of diffusion probabilistic models in mri |
| topic | Diffusion probabilistic models Score based generative modeling MRI |
| url | http://www.sciencedirect.com/science/article/pii/S2950162824000353 |
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