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Abstract #3849

Enhanced Diffusion MRI of Infant Brain at 0.35 Tesla Using a Self-Training Two-Stage Framework

Zhibo Chen1, Yushuang Ding2, Haoan Xu1, Li Zhao1, Hongxi Zhang2, and Dan Wu1
1Department of Biomedical Engineering, Zhejiang University, Hangzhou, China, 2Department of Radiology, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China

Synopsis

Keywords: AI/ML Image Reconstruction, Low-Field MRI, DWI, Diffusion Probabilistic Models

Motivation: Low-field MRI produces less acoustic noise, making it more suitable for newborns. However, low-field MRI, especially diffusion-weighted imaging (DWI), suffers from low SNR and reduced contrast, which limits its clinical effectiveness in diagnosis.

Goal(s): To enhance the image quality of DWI at 0.35 T.

Approach: A self-training two-stage framework was proposed, which extracted the structural and contrast representations of infant DWI from 0.35T and 1.5T, and aligned the above features using a proposed contrastive loss function.

Results: Experiments on clinical infant brains achieved a PSNR of 23.88 and a SSIM of 0.8944, outperforming other networks and demonstrating the superior performance of our method.

Impact: The proposed method improved the image quality of DWI at low-field without collecting paired LF-HF data. It may promote accurate diagnosis using low-field MRI for DWI.

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Keywords