ISMRM25 - Electro-Magnetic Properties of Tissues

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

Electro-Magnetic Properties of Tissues

Oral

Contrast Mechanisms

Thursday, 15 May 2025

320

13:15 - 15:15

Moderators: Beata Bachrata & Ferdinand Schweser

Session Number: O-35

CME Credit

13:15		1263. Predicting Mesoscopic Larmor Frequency Shifts with Diffusion MRI in Ex Vivo Pig Optic Nerve View Presentation Video A. Sandgaard, A. Pampel, R. Müller, N. Wallstein, T. Mildner, A. Alstrup, C. Jäger, H. Möller, S. Jespersen Aarhus University, Aarhus, Denmark Impact: This study elucidates the nature of Larmor frequency shifts in coherent white matter, enhancing our understanding of its microstructural origin. These insights have the potential to improve QSM techniques by achieving better estimation of tissue magnetic susceptibility.
13:27		1264. A robust deep learning method for quantitative susceptibility mapping using diffusion model with a time-travel and resampling refinement module View Presentation Video M. Zhang, H. Wei Shanghai Jiao Tong University, Shanghai, China Impact: We introduce a diffusion model-based method for QSM reconstruction by enforcing hard data consistency during inference. We also present a time-travel and resampling refinement module in the latter steps to enhance performance. Our approach enables robust and high-quality QSM reconstruction.
13:39		1265. Boundary value estimation for electrical properties tomography based on Helmholtz decomposition for the electric and magnetic fields View Presentation Video T. Nara, N. Eda The University of Tokyo, Tokyo, Japan Impact: The estimated boundary EP values can be used in the conventional partial-differential-equation-based and integral-equation based methods for reconstructing non-homogeneous EPs in the ROI, which eliminates the necessity of assuming the boundary EPs.
13:51		1266. DeepSepSTI-R2: R2-Free Anisotropic Susceptibility Source Separation in Susceptibility Tensor Imaging with Deep Learning View Presentation Video Z. Fang, H-G Shin, B. Dewey, P. Calabresi, P. van Zijl, J. Sulam, X. Li The Johns Hopkins University, Baltimore, United States Impact:* DeepSepSTI-R2* enhances anisotropic susceptibility source separation by eliminating the need for extra R2 measurement, i.e., using only R2* and phase maps derived solely from gradient echo data, thereby significantly reducing scan time.
14:03		1267. Vessel Segmentation in χ-separation using Deep Learning View Presentation Video H. Park, T. Kim, R. Hong, J. Kim, J. Lee Seoul National University, Seoul, Korea, Republic of Impact: The proposed deep neural network can produce high-quality vessel masks, eliminating the need for hyperparameter tuning in the original region-growing method. The result may improve the accuracy and efficiency of χ-separation map analysis, supporting precise susceptibility studies.
14:15		1268. Preliminary Study on Functional MRI-Related Conductivity and Permittivity Changes using Electrical Properties Tomography View Presentation Video K-J Jung, C. Cui, C. Park, S. Lee, S. Jung, D. Han, D-H Kim Yonsei University, Seoul, Korea, Republic of Impact: Functional MRI-related changes in both electrical conductivity and permittivity are observed during brain activation, potentially enhancing our understanding of the relationships between fMRI effect, vascular factors, and tissue electrical properties.
14:27		1269. Multi-compartment susceptibility source separation (mc-chi-separation) using GRE data only: Integrated model of QSM and myelin water imaging View Presentation Video H-G Shin, S. Saidha, J. Lee, P. Calabresi, P. van Zijl, X. Li Johns Hopkins University , Baltimore, United States Impact: Mc-chi-separation enhances susceptibility source separation by enabling chi-separation using GRE data only, expanding its applications to clinical settings and pre-acquired data lacking R2 measurements. This may also facilitate better monitoring of iron and myelin-related neurodegenerative disease with reduced inter-site variability.
14:39		1270. Quantitative Susceptibility Mapping and R2* of Ischemia-reperfusion Injury and its Relationship with Reactive Oxygen Species Activity View Presentation Video M. Awad, S. Swago, C. Camillo, E. Thompson, A. Bhattaru, B. Moon, G. Ferrari, E. Castillero, E. Gallagher, M. McManus, V. Ferrari, R. Gorman, C. Tschabrunn, R. Mach, J. Karp, P. Bravo, W. Witschey University of Pennsylvania, Philadelphia, United States Impact: The observed associations between magnetic susceptibility and R2* with ROS activity allows for better understanding of IRI and potential to develop new targeted interventions. This suggests that iron could be a catalyst for ROS production in ischemia-reperfusion injury.
14:51		1271. Development of χ-separation pipeline for UK Biobank dataset View Presentation Video J. Koo, H. Jeong, J. Kim, R. Hong, H-G Shin, X. Li, Y. S. Hong, Y. Qiao, D. Arking, J. Lee Seoul National University, Seoul, Korea, Republic of Impact: This study proposes a processing pipeline for high-quality χ-separation in the UKB dataset. For high-quality χ-separation, B₀-field inhomogeneity artifact in $$$R_2 ^*$$$ was removed using a neural network. Our pipeline enables us to investigate the large cohort UKB data.
15:03		1272. Uncertainty-Aware Conductivity Reconstruction via a 2.5D Patch-to-Patch Data-driven Model View Presentation Video C-H Park, T. Meerbothe, K-J Jung, C. Cui, C. van den Berg, S. Mandija, D-H Kim Yonsei University, Seoul, Korea, Republic of Impact: A 2.5D uncertainty-aware data-driven framework is developed for conductivity reconstructions. This approach enhances estimation accuracy and quantifies variability, providing insights into model reliability and improving performance compared to conventional physics-based and end-to-end deep-learning methods.

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