Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-013 ISMRM Abstract

Enable Quantitative Analysis of Clinical Breast DCE-MRI Using Gen-AI Enhancement of Temporal Resolution: A Preliminary Study

Primary: Analysis Methods - Image Synthesis and Translation
Secondary: Body - Breast
364-04-013 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: Breast DCE-MRI Generative AI Temporal resolution
Accepted
Qisheng He1, Fahad Q Khawar1, Wei Huang 2,3, Ming Dong1
1Computer Science, Wayne State University, Detroit, United States of America
2Radiation Oncology, Corewell Health, Detroit, United States of America
3Research Institute, Corewell Health, Detroit, United States of America
Presenting Author: Wei Huang

Synopsis

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References

1. 1. Huang W. Quantitative DCE-MRI of the Breast. Chapter 22, pp 425-458. In: Pinker K, Mann R, Partridge S eds. Breast MRI – State of the Art and Future Directions. Elsevier Academic Press, London UK, 2022. 2. Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative Imaging Biomarkers Alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2019: 49:e101-e121 doi: 10.1002/jmri.26518. 3. Moloney B, Li X, Hirano M, Saad Eddin A, Lim JY, Biswas D, et al. Initial experience in implementing quantitative DCE-MRI to predict breast cancer therapy response in a multi-center and multi-vendor platform setting. Front Oncol 2024;14:1395502. Doi: 10.3389/fonc.2024.1395502 4. Li G, Rao C, Mo J, Zhang Z, Xing W, Zhao L. Rethinking diffusion model for multi-contrast MRI super-resolution. in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2024, pp. 11365–11374. 5. Goodfellow, et al. Generative adversarial nets. in Advances in Neural Information Processing Systems (NeurIPS), vol. 27, 2014. 6. Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, pp. 6840–6851, 2020. 7. Tudorica A, Oh KY, Chui SYC, Roy N, Troxell ML, Naik A, et al. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI. Transl Oncol 2016;9:8-17. 8. Li B, Xue K, Liu B, Lai Y-K. BBDM: Image-to-image translation with Brownian bridge diffusion models. in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1952–1961, 2023. 9. Wang P, Xiao B, He Q, Glide-Hurst C, Dong M. Score-Based Image-to-Image Brownian Bridge. In Proceedings of the 32nd ACM International Conference on Multimedia (MM '24). Association for Computing Machinery, New York, NY, USA, 10765–10773. 10. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004. 11. Zhang R, Isola P, Efros AA, Shechtman E, Wang O. The unreasonable effectiveness of deep features as a perceptual metric. in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 586–595, 2018. [doi]

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http://echo.ismrm.org/p/ISMRM2026/364-04-013