567-03-010 ISMRM Abstract

Self-Supervised Hierarchy Flow-Based Approach for Creating Digital MRI Phantoms

Primary: Acquisition & Reconstruction - AI methods

Secondary: Analysis Methods - Generative Models

567-03-010 · Standardizing MRI Through Data Formats and Phantoms · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row H

Keywords: Signal Modeling Self-supervised learning Neural Network Digital phantom MRI physics - synthetic data generation

Accepted

Kseniya Belousova¹, Zilya Badrieva¹, Nikita Babich¹, Iuliia Pisareva¹, Ekaterina A Brui ¹, Walid Al-Haidri¹

¹Department of Physics, ITMO University, Saint Petersburg, Russian Federation

Presenting Author: Ekaterina A Brui

Synopsis

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References

1. B. Aubert-Broche, M. Griffin, G. B. Pike, A. C. Evans, and D. L. Collins, “Twenty new digital brain phantoms for creation of validation image data bases, ” IEEE Trans. Med. Imaging, vol. 25, no. 11, pp. 1410–1416, 2006. doi: 10.1109/TMI.2006.883453. [doi]

2. Castillo-Passi C, Coronado R, Varela-Mattatall G, Alberola-López C, Botnar R, Irarrazaval P. KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration. Magn Reson Med. 2023; 90: 329-342. doi: 10.1002/mrm.29635 [doi]

3. K. S. Ravi, S. Geethanath, and J. T. Vaughan, “PyPulseq: A python package for MRI pulse sequence design,” J. Open Source Softw., vol. 4, no. 42, p. 1725, 2019. doi: 10.21105/joss.01725. [doi]

4. Fan W., Chen J., Liu Z. Hierarchy Flow For High-Fidelity Image-to-Image Translation //arXiv preprint arXiv:2308.06909. – 2023. doi: 10.48550/arXiv.2308.06909 [doi]

5. Z. Wang, E. P. Simoncelli and A. C. Bovik, "Multiscale structural similarity for image quality assessment," The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, CA, USA, 2003, pp. 1398-1402 Vol.2, doi: 10.1109/ACSSC.2003.1292216. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/567-03-010