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Abstract #2763

Representation learning for ultra-low-field brain MRI super-resolution

Xiang Li1,2, Vick Lau1,2, Christopher Man1,2, 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: AI/ML Image Reconstruction, Brain, Representation learning, ULF, INR, KAN

Motivation: High, isotropic resolution is desirable for lesion detection and biomarkers extraction for cognitive disorders. However, ULF MRI severely suffers from low spatial resolution and SNR.

Goal(s): To introduce a new deep learning (DL) architecture for improving image quality of noisy, low-resolution 3D ULF data.

Approach: Implemented new DL super-resolution method with UNet-based encoder and a novel decoder based on KAN to learn and decode concise representation of noisy, low-resolution 3D ULF isotropic images.

Results: Proposed method improves resolution, suppresses noise and artifacts, and produces images similar to those obtained from high-field MRI scanners in terms of overall appearance.

Impact: Enhancing image resolution and fidelity for ULF brain imaging at 0.055T using data-driven 3D deep learning approach. Potentially enable portable and point-of-care diagnosis.

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Keywords