Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-03-186 ISMRM Abstract

Deep learning-accelerated fat suppressed T2-weighted imaging of the breast: Faster acquisition with comparable image quality

Primary: Body - Breast
Secondary: Acquisition & Reconstruction - AI methods
570-03-186 · AI Methods · Wednesday, 13 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: Breast MRI T2-weighted imaging Accelerated Acquisition Deep Resolve Boost
Accepted
Chenxi Zhang 1, Yuanfeng Wei1, Hao dong Qin2, Marcel Dominik Nickel3, Mingjue Jian1, Li Ding1, Xiaomei Li1, Yingrui Huangyingrui1, Chenggong Yan1
1Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
2MR Research Collaboration, Siemens Healthineers, Guangzhou, Chin, Guangzhou, China
3Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
Presenting Author: Chenxi Zhang

Synopsis

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References

1. Mann RM, Cho N, Moy L. Breast MRI: State of the Art. Radiology. 2019;292(3):520-536. doi:10.1148/radiol.2019182947 [doi]
2. Heacock L, Lewin AA, Toth HK, Moy L, Reig B. Abbreviated MR Imaging for Breast Cancer. Radiol Clin North Am. 2021;59(1):99-111. doi:10.1016/j.rcl.2020.09.001 [doi]
3. Wang S, Wu R, Jia S, et al. Knowledge-driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un-supervised learning. Magn Reson Med. 2024;92(2):496-518. doi:10.1002/mrm.30105 [doi]
4. Xie Y, Tao H, Li X, et al. Prospective Comparison of Standard and Deep Learning–reconstructed Turbo Spin-Echo MRI of the Shoulder. Radiology. 2024;310(1):e231405. doi:10.1148/radiol.231405 [doi]
5. Ichinohe F, Oyama K, Yamada A, et al. Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning–Based Reconstruction of the Liver: Comparison to Conventional Free-Breathing Turbo Spin Echo. Invest Radiol. 2023;58(6):373-379. doi:10.1097/RLI.0000000000000943 [doi]
6. Bischoff LM, Peeters JM, Weinhold L, et al. Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI. Radiology. 2023;308(3):e230427. doi:10.1148/radiol.230427 [doi]
7. Kiryu S, Akai H, Yasaka K, et al. Clinical Impact of Deep Learning Reconstruction in MRI. RadioGraphics. 2023;43(6):e220133. doi:10.1148/rg.220133 [doi]
8. Herrmann J, Koerzdoerfer G, Nickel D, et al. Feasibility and Implementation of a Deep Learning MR Reconstruction for TSE Sequences in Musculoskeletal Imaging. Diagnostics (Basel). 2021;11(8):1484. doi:10.3390/diagnostics11081484 [doi]

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http://echo.ismrm.org/p/ISMRM2026/570-03-186