365-03-009 ISMRM Abstract

Comparison of Conventional and Deep Neural Network IVIM Fitting Strategies for Soft-Tissue Tumor Characterization

Primary: Diffusion - IVIM

Secondary: Analysis Methods - Classification and Prediction

365-03-009 · Body Diffusion MRI Methods · Monday, 11 May, 1:50 PM–2:45 PM · Digital Posters Row F

Keywords: IVIM Tumor classification Deep-learning based IVIM fitting Segmented IVIM fitting

Accepted

Julia Augusta de Souza Buratti ^1,2, Huseyin Ekin Ergi³, Ahmet Peker³, Guangyu Dan^1,2, Albert Yen^1,2,4, Yusuf Oner³, Xiaohong Joe Zhou^1,2,5, Muge Karaman^1,2

¹Department of Biomedical Engineering, University of Illinois Chicago, Chicago, United States of America

²Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, United States of America

³Department of Radiology, Koç University Hospital, Istanbul, Turkey

⁴College of Medicine, Medical Scientist Training Program, University of Illinois Chicago, Chicago, United States of America

⁵College of Medicine, Department of Radiology and Neurosurgery, University of Illinois Chicago, Chicago, United States of America

Presenting Author: Julia Augusta de Souza Buratti

Synopsis

Motivation:

Goals:

Approach:

Results:

Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

References

1. Kransdorf MJ, Murphey MD. Soft-Tissue Tumors and Tumor-Like Masses: A Systematic Approach to Diagnosis. In: Hodler J et al., editors. Musculoskeletal Diseases. Milano: Springer Milan; 2005 Oct 24;p.54–61.

2. Goyal S, Rangankar V, Deshmukh S, et al. MRI Evaluation of Soft Tissue Tumors and Tumor-Like Lesions of Extremities. Cureus. 2023 Apr 2;15(4):e37047.

3. Kaandorp MPT, Barbieri S, Klaassen R, et al. Improved unsupervised physics-informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magnetic Resonance in Medicine. 2021 May 3;86(4):2250-2265.

4. Barbieri S, Gurney-Champion OJ, Klaassen R, et al. Deep learning how to fit an intravoxel incoherent motion model to diffusion-weighted MRI. Magnetic Resonance in Medicine. 2020 Jan 1;83(1):312-321.

5. Cho GY, Moy L, Zhang JL, et al. Comparison of fitting methods and b-value sampling strategies for intravoxel incoherent motion in breast cancer. Magn Reson Med. 2015 Oct 9;74(4):1077-1085.

6. Gurney-Champion OJ, Klaassen R, Froeling M, et al. Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients. Rahman MS, editor. PLoS ONE. 2018 Apr 4;13(4):e0194590.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-03-009