Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
660-04-012 ISMRM Abstract

VHU-Net: Variational Hadamard U-Net for Body MRI Bias Field Correction

Primary: Acquisition & Reconstruction - Artifacts and Correction Strategies
Secondary: Acquisition & Reconstruction - AI methods
660-04-012 · AI: Anything Synthetic or Correcting Artifacts · Thursday, 14 May, 2:35 PM–3:30 PM · Digital Posters Row A
Keywords: MRI Segmentation U-Net Bias field correction Hadamard transform Variational inference
Accepted
Xin Zhu1, Halil Ertugrul Aktas 2, Gorkem Durak2, Ahmet Enis Cetin1, Batuhan Gundogdu3, Ziliang Hong2, Hongyi Pan2, Elif Keles2, Hatice Savas2, Aytekin Oto3, Hiten Patel4, Adam B Murphy4, Ashley Ross4, Frank Miller2, Baris Turkbey5, Ulas Bagci2
1Electrical and Computer Engineering Department, University of Illinois Chicago, Chicago, United States of America
2Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, United States of America
3Department of Radiology, University of Chicago, Chicago IL, Chicago, United States of America
4Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, United States of America
5Molecular Imaging Branch, National Institutes of Health, Bethesda, United States of America
Presenting Author: Halil Ertugrul Aktas

Synopsis

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References

1. Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: improved N3 bias correction. IEEE transactions on medical imaging, 29(6), 1310–1320. https://doi.org/10.1109/TMI.2010.2046908 [doi]
2. S. N. Sridhara, H. Akrami, V. Krishnamurthy, A. A. Joshi, Bias field correction in 3d-mris using convolutional autoencoders., in: Medical Imaging 2021: Image Processing, Vol. 11596, SPIE, 2021, pp. 671–676.
3. X. He, A. Q. Wang, M. R. Sabuncu, Neural pre-processing: A learning framework for end-to-end brain mri pre-processing, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2023, pp. 258–267.
4. X. Zhu, H. Pan, B. Gundogdu, D. Jha, Y. Velichko, A. B. Murphy, A. Ross, B. Turkbey, A. E. Cetin, U. Bagci, A probabilistic hadamard u-net for mri bias field correction, in: International Workshop on Machine Learning in Medical Imaging, Springer, 2024, pp. 208–217.
5. Chen, L., Wu, Z., Hu, D., Wang, F., Smith, J. K., Lin, W., Wang, L., Shen, D., Li, G., & Consortium, F. U. B. C. P. (2021). ABCnet: Adversarial bias correction network for infant brain MR images. Medical image analysis, 72, 102133. https://doi.org/10.1016/j.media.2021.102133 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/660-04-012