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Abstract #1580

Correction of Resting-State fMRI Data Contaminated by B0 Inhomogeneity Artifacts Using a Single-Subject Fitting Neural Network

DongWook Kim1, SooHyoung Lee1, KyuJin Jung1, SeungYeon Seo1, Hyun-Joo Song2, and Dong-Hyun Kim1
1Department of Electrical & Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Department of Psychology, Yonsei University, Seoul, Korea, Republic of

Synopsis

Keywords: Functional Connectivity, fMRI (resting state)

Motivation: A primary limitation of Gradient Echo-EPI(GRE-EPI) is its sensitivity to B0 inhomogeneity, resulting in signal dropout at air/tissue interfaces. Current artifact correction methods, however, continue to face substantial limitation.

Goal(s): We aim to develop a frequency domain based single subject fitting neural network to effectively reduce B0 field inhomogeneity artifacts.

Approach: Spin Echo EPI is resilient to B0 inhomogeneity but less T2*-sensitive. GRE EPI is T2*-sensitive but prone to B0 inhomogeneity. Leveraging these properties, we proposed a single-subject fitting neural network.

Results: The proposed method mitigated the B0 filed inhomogeneity artifacts and restored brain signals.

Impact: B0 field inhomogeneity induced artifacts were mitigated by proposed frequency-domain deconvolutional neural network. The proposed method is expected to broaden the detectable range of brain regions, particularly in areas heavily affected by B0 inhomogeneity, enabling more comprehensive whole-brain fMRI research.

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