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Abstract #2739

Advancing Ultralow-field Brain MRI through K-space Undersampling and Deep Learning Image Reconstruction

Xiang Li1,2, Christopher Man1,2, Vick Lau1,2, Alex T. L. Leong1,2, Yujiao Zhao1,2, and Ed X. Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

Synopsis

Keywords: Image Reconstruction, Low-Field MRI

Motivation: While the emerging ULF MRI shows potential of low-cost and point-of-care imaging applications, its image quality is poor and the scan time is long.

Goal(s): To reduce the ULF brain MRI scan time through deep learning image reconstruction from partial Fourier and uniformly undersampled data.

Approach: We proposed a DL reconstruction method for fast 3D brain MRI at 0.055T by applying the 3D DL image reconstruction to undersampled 3D k-space data, achieving speed up of 2x over our newly developed partial Fourier reconstruction and superresolution (PF-SR) method.

Results: Our preliminary results show the proposed method could reduce noise, artifacts, and enhance spatial resolution.

Impact: Our model can work with uniformly undersampled data, leading to acceleration factor of 2, and a PF sampling of at a fraction of 0.7. Our development enables fast and quality whole-brain MRI at 0.055T, indicating potential for widespread biomedical applications.

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Keywords