569-01-008 ISMRM Abstract

Modeling Tissue Signals from a Single Voxel Simulation

Primary: Acquisition & Reconstruction - Signal Modeling

Secondary: Acquisition & Reconstruction - Non-AI methods

569-01-008 · Signal Modeling and Signal Preparation: Novel Approaches · Wednesday, 13 May, 8:20 AM–9:15 AM · Digital Posters Row J

Keywords: MRI simulation Computation model Magnetic Resonance Imaging (MRI)

Accepted

Guillermo Sahonero-Alvarez^1,2,3, Ronal M Coronado^1,3,4, Carlos A Castillo-Passi^5,6,7, Pablo Irarrazaval ^2,3,8

¹Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile

²Institute for Biological and Biomedical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

³Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile

⁴Núcleo de Investigación en Data Science (NIDS), Facultad de Ingeniería y Negocios, Universidad de Las Americas, Santiago, Chile

⁵Department of Radiology, Stanford University, Stanford, United States of America

⁶Division of Radiology, Veterans Administration Health Care System, Palo Alto, United States of America

⁷Stanford Cardiovascular Institute, Stanford Medicine, Stanford, United States of America

⁸Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Presenting Author: Pablo Irarrazaval

Synopsis

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References

1. Huang H, Yang Q, Wang J, Zhang P, Cai S, Cai C. High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning. Phys Med Biol. 2023;68(8):085002. doi:10.1088/1361-6560/acc4a6 [doi]

2. van der Heide O, van den Berg CAT, Sbrizzi A. GPU‐accelerated Bloch simulations and MR‐STAT reconstructions using the Julia programming language. Magnetic Resonance in Med. 2024;92(2):618-630. doi:10.1002/mrm.30074 [doi]

3. 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. Magnetic Resonance in Med. 2023;90(1):329-342. doi:10.1002/mrm.29635 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-01-008