Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
668-04-003 ISMRM Abstract

Neural Network Modeling with Synthetic b-Values Enables Accurate and Efficient Cervical Cancer Assessment

Primary: Body - Pelvis
Secondary: Diffusion - Diffusion Modeling
668-04-003 · Pulse Sequences: Design and Optimization · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row I
Keywords: Cancer Diffusion Modeling Body Imaging Diffusion-weighted MRI Apparent diffusion coefficient
Accepted
Albert Yen1,2,3, Guangyu Dan1, Cui Feng4, Qingfei Luo 1,5, Kezhou Wang1, Muge Karaman1,3, Daoyu Hu4, Zhen Li4, Xiaohong Joe Zhou1,3,6
1Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, United States of America
2Medical Scientist Training Program, University of Illinois Chicago, Chicago, United States of America
3Department of Biomedical Engineering, University of Illinois Chicago, Chicago, United States of America
4Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue #1095, Wuhan, China
5Department of Radiology, University of Illinois Chicago, Chicago, United States of America
6Department of Radiology and Neurosurgery, University of Illinois Chicago, Chicago, United States of America
Presenting Author: Qingfei Luo

Synopsis

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References

1. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71, 209–249 (2021).
2. Lakhman, Y., Aherne, E. A., Jayaprakasam, V. S., Nougaret, S. & Reinhold, C. Staging of Cervical Cancer: A Practical Approach Using MRI and FDG PET. AJR Am J Roentgenol 221, 633–648 (2023).
3. Liu, B. et al. Potentialities of multi-b-values diffusion-weighted imaging for predicting efficacy of concurrent chemoradiotherapy in cervical cancer patients. BMC Med Imaging 20, 97 (2020).
4. Karaman, M. M. et al. Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values. Magn Reson Med 76, 1149–1157 (2016).
5. Panagiotaki, E. et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol 50, 218–227 (2015).
6. Dan, G. et al. Tissue classification from raw diffusion-weighted images using machine learning. Med Phys (2025) doi:10.1002/mp.17810. [doi]
7. Castro, J. L., Mantas, C. J. & Benítez, J. M. Neural networks with a continuous squashing function in the output are universal approximators. Neural Netw 13, 561–563 (2000).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/668-04-003