Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-02-002 ISMRM Abstract

Turning Routine MRI into Pseudo Training Data: Scalable Physics-Informed Learning for Quantitative Susceptibility Mapping

Primary: Contrast Mechanisms - Susceptibility/QSM
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
560-02-002 · New Developments in QSM II · Wednesday, 13 May, 9:15 AM–10:10 AM · Digital Posters Row A
Keywords: Domain adaptation Quantitative Susceptibility Mapping (QSM) Data Scarcity Physics-informed Deep Learning Pseudo data
Accepted
Simon Graf 1,2, Walter A Wohlgemuth1,2, Andreas Deistung1,2
1Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Germany
2Medical Physics Group, University Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), Germany
Presenting Author: Simon Graf

Synopsis

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References

1. Stüber C, Morawski M, Schäfer A, et al. Myelin and iron concentration in the human brain: a quantitative study of MRI contrast. NeuroImage. 2014;93 Pt 1:95-106. doi: 10.1016/j.neuroimage.2014.02.026. [doi]
2. LaMontagne PJ, Benzinger TL, Morris JC, et al. OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease. medRxiv. 2019. https://www.medrxiv.org/content/early/2019/12/15/2019.12.13.19014902doi: 10.1101/2019.12.13.19014902. [doi]
3. Baid U, Ghodasara S, Mohan S, et al. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification. 2021.
4. Bakas S, Akbari H, Sotiras A, et al. Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. The cancer imaging archive. 2017;286. doi:
5. Bakas S, Akbari H, Sotiras A, et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific data. 2017;4(1):170117. doi: 10.1038/sdata.2017.117. [doi]
6. Menze BH, Jakab A, Bauer S, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging. 2015;34(10):1993-2024. doi: 10.1109/TMI.2014.2377694. [doi]
7. Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging. 2010;29(6):1310-1320. doi: 10.1109/TMI.2010.2046908. [doi]
8. Manjón JV, Coupé P, Martí-Bonmatí L, Collins DL, Robles M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging. 2010;31(1):192-203. doi: 10.1002/jmri.22003. [doi]
9. Billot B, Greve DN, Puonti O, et al. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Medical image analysis. 2023;86:102789. doi: 10.1016/j.media.2023.102789. [doi]
10. Feng X, Deistung A, Reichenbach JR. Quantitative susceptibility mapping (QSM) and R2* in the human brain at 3T: Evaluation of intra-scanner repeatability. Z Med Phys. 2018;28(1):36-48. doi: 10.1016/j.zemedi.2017.05.003. [doi]
11. Deistung A, Schäfer A, Schweser F, Biedermann U, Turner R, Reichenbach JR. Toward in vivo histology: a comparison of quantitative susceptibility mapping (QSM) with magnitude-, phase-, and R2*-imaging at ultra-high magnetic field strength. NeuroImage. 2013;65:299-314. doi: 10.1016/j.neuroimage.2012.09.055. [doi]
12. Graf S, Wohlgemuth WA, Deistung A. Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution. Front Neurosci. 2024;18:1366165. doi: 10.3389/fnins.2024.1366165. [doi]
13. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation; 2016. Available at: http://arxiv.org/pdf/1606.06650v11606.06650v11606.06650v1.
14. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research. 2014(15):1929 -1958. http://jmlr.org/papers/v15/srivastava14a.htmldoi:
15. Loshchilov I, Hutter F. Decoupled Weight Decay Regularization; 2017. Available at: http://arxiv.org/pdf/1711.05101v31711.05101v31711.05101v3.
16. Liu T, Spincemaille P, Rochefort L de, Kressler B, Wang Y. Calculation of susceptibility through multiple orientation sampling (COSMOS): a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med. 2009;61(1):196-204. doi: 10.1002/mrm.21828. [doi]
17. Shi Y, Feng R, Li Z, Zhuang J, Zhang Y, Wei H. Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset. NeuroImage. 2022;261:119522. doi: 10.1016/j.neuroimage.2022.119522. [doi]
18. Schweser F, Sommer K, Deistung A, Reichenbach JR. Quantitative susceptibility mapping for investigating subtle susceptibility variations in the human brain. NeuroImage. 2012;62(3):2083-2100. doi: 10.1016/j.neuroimage.2012.05.067. [doi]
19. Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR Biomed. 2017;30(4). doi: 10.1002/nbm.3569. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-02-002