Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
561-02-002 ISMRM Abstract

Influence of Site Effects on Radiomics-Based Knee Injury Diagnosis

Primary: Musculoskeletal - Tendon/Ligament
Secondary: Analysis Methods - Radiomics
561-02-002 · Classification and Analysis in the Body · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row B
Keywords: Radiomics Magnetic Field Strength Gaussian Mixture Model Site Effects Knee Injury
Accepted
Jiqing Huang1, Yi Chen2,3, Antoine Jacquemin1, Evgenios N Kornaropoulos 1, Mohamed Ali Bahri1, Christophe Phillips1
1CRC-Human Imaging, GIGA-Institute, University of Liège, Liège, Belgium
2Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Guizhou University, Guizhou, China
3D-Lab, Maastricht University, Maastricht, Netherlands
Presenting Author: Evgenios N Kornaropoulos

Synopsis

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References

1. Bien, Nicholas, et al. "Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet." PLoS medicine 15.11 (2018): e1002699. doi: 10.1371/journal.pmed.1002699 [doi]
2. Wong S, Steinbach L, Zhao J, Stehling C, Ma CB, Link TM. Comparative study of imaging at 3.0 T versus 1.5 T of the knee. Skeletal Radiol. 2009 Aug;38(8):761-9. doi: 10.1007/s00256-009-0683-0. [doi]
3. Tran, Alexia, et al. "Deep learning to detect anterior cruciate ligament tear on knee MRI: multi-continental external validation." European Radiology 32.12 (2022): 8394-8403. doi: 10.1007/s00330-022-08923-z [doi]
4. Nikolaou, Vassilios S., et al. "MRI efficacy in diagnosing internal lesions of the knee: a retrospective analysis." Journal of trauma management & outcomes 2.1 (2008): 4. doi: 10.1186/1752-2897-2-4 [doi]
5. Hajianfar G, Hosseini SA, Bagherieh S, Oveisi M, Shiri I, Zaidi H. Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study. Med Biol Eng Comput. 2024 Aug;62(8):2319-2332. doi: 10.1007/s11517-024-03071-6. [doi]
6. Štajduhar I, Mamula M, Miletić D, Ünal G. Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Programs Biomed. 2017;140:151–64. doi: 10.1016/j.cmpb.2016.12.006 [doi]
7. Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." Nature methods 18.2 (2021): 203-211.doi: 10.1038/s41592-020-01008-z [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/561-02-002