Enhancing 3T BOLD fMRI SNR using Unpaired 7T Data with Schrödinger Bridge Diffusion

• URL: http://arxiv.org/abs/2504.01004v1
• Date: Tue, 01 Apr 2025 17:41:24 GMT
• Title: Enhancing 3T BOLD fMRI SNR using Unpaired 7T Data with Schrödinger Bridge Diffusion
• Authors: Yujian Xiong, Xuanzhao Dong, Sebastian Waz, Wenhui Zhu, Negar Mallak, Zhong-lin Lu, Yalin Wang,
• Abstract summary: Most research relies on 3T MRI systems which offer lower spatial resolution and temporal and SNR.<n>We propose a novel framework that aligns 7T and 3T fMRI data from different subjects datasets.<n>We then apply an unpaired Brain Disk Schr"odinger Bridge diffusion model to enhance the SNR of the 3T data.
• Score: 1.8091533096543726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High spatial and temporal resolution, coupled with a strong signal-to-noise ratio (SNR), has made BOLD 7 Tesla fMRI an invaluable tool for understanding how the brain processes visual stimuli. However, the limited availability of 7T MRI systems means that most research relies on 3T MRI systems, which offer lower spatial and temporal resolution and SNR. This naturally raises the question: Can we enhance the spatiotemporal resolution and SNR of 3T BOLD fMRI data to approximate 7T quality? In this study, we propose a novel framework that aligns 7T and 3T fMRI data from different subjects and datasets in a shared parametric domain. We then apply an unpaired Brain Disk Schr\"odinger Bridge diffusion model to enhance the spatiotemporal resolution and SNR of the 3T data. Our approach addresses the challenge of limited 7T data by improving the 3T scan quality. We demonstrate its effectiveness by testing it on two distinct fMRI retinotopy datasets (one 7T and one 3T), as well as synthetic data. The results show that our method significantly improves the SNR and goodness-of-fit of the population receptive field (pRF) model in the enhanced 3T data, making it comparable to 7T quality. The codes will be available at Github.

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