Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models
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arXiv
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| Format: | Preprint |
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2024
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| author | Osuala, Richard Lang, Daniel M. Verma, Preeti Joshi, Smriti Tsirikoglou, Apostolia Skorupko, Grzegorz Kushibar, Kaisar Garrucho, Lidia Pinaya, Walter H. L. Diaz, Oliver Schnabel, Julia A. Lekadir, Karim |
| author_facet | Osuala, Richard Lang, Daniel M. Verma, Preeti Joshi, Smriti Tsirikoglou, Apostolia Skorupko, Grzegorz Kushibar, Kaisar Garrucho, Lidia Pinaya, Walter H. L. Diaz, Oliver Schnabel, Julia A. Lekadir, Karim |
| contents | Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fréchet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet and provide a user-friendly library for Fréchet radiomics distance calculation at https://pypi.org/project/frd-score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13890 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models Osuala, Richard Lang, Daniel M. Verma, Preeti Joshi, Smriti Tsirikoglou, Apostolia Skorupko, Grzegorz Kushibar, Kaisar Garrucho, Lidia Pinaya, Walter H. L. Diaz, Oliver Schnabel, Julia A. Lekadir, Karim Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Contrast agents in dynamic contrast enhanced magnetic resonance imaging allow to localize tumors and observe their contrast kinetics, which is essential for cancer characterization and respective treatment decision-making. However, contrast agent administration is not only associated with adverse health risks, but also restricted for patients during pregnancy, and for those with kidney malfunction, or other adverse reactions. With contrast uptake as key biomarker for lesion malignancy, cancer recurrence risk, and treatment response, it becomes pivotal to reduce the dependency on intravenous contrast agent administration. To this end, we propose a multi-conditional latent diffusion model capable of acquisition time-conditioned image synthesis of DCE-MRI temporal sequences. To evaluate medical image synthesis, we additionally propose and validate the Fréchet radiomics distance as an image quality measure based on biomarker variability between synthetic and real imaging data. Our results demonstrate our method's ability to generate realistic multi-sequence fat-saturated breast DCE-MRI and uncover the emerging potential of deep learning based contrast kinetics simulation. We publicly share our accessible codebase at https://github.com/RichardObi/ccnet and provide a user-friendly library for Fréchet radiomics distance calculation at https://pypi.org/project/frd-score. |
| title | Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.13890 |