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Abstract #1447

Optimizing Image Reconstruction in fMRI using a Reduced Field-of-View and (k, t)-Space undersampling

Qingfei Luo1,2, Joseph Hutter1,3, and Xiaohong Joe Zhou1,2,4
1Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, IL, United States, 2Department of Radiology, University of Illinois Chicago, Chicago, IL, United States, 3College of Medicine, University of Illinois Chicago, Chicago, IL, United States, 4Departments of Neurosurgery and Biomedical Engineering, University of Illinois Chicago, Chicago, IL, United States

Synopsis

Keywords: fMRI Acquisition, fMRI (task based)

Motivation: By employing the PS-Sparse reconstruction, the 3D reduced field-of-view imaging with (k, t)-space undersampling (k-t 3D-rFOVI) can acquire fMRI data with high temporal and spatial resolutions.

Goal(s): This study aims to improve the data quality of k-t 3D-rFOVI-based fMRI by optimizing two key parameters in PS-Sparse reconstruction: the model order L and the sparsity regularization parameter λ.

Approach: k-t 3D-rFOVI images were reconstructed with various L and λ values and compared with the images with full k-space sampling (ground truth).

Results: PS-Sparse reconstruction with L/λ = 16/0 provided the best accuracy in recovering brain activation from the fMRI data.

Impact: The optimal parameters determined in this study improved the performance of PS-Sparse reconstruction on image quality, allowing accurate detection of brain activations in fMRI studies using k-t 3D-rFOVI.

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