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Abstract #3916

A Dual-Domain Denoising Method Based on Physical Intelligent Neural Network for Diffusion Tensor Imaging

Haoyu Zhang1, Feiqiang Guan2, Chengsong Zeng2, Tao Gong3, Chen Qian2, Liangjie Lin4, Jiazheng Wang4, and Xiaobo Qu2
1Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen, China, 2Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Institute of Artificial Intelligence, Xiamen University, Xiamen, China, 3Departments of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China, 4Clinical and Technical Support, Philips Healthcare, Beijing, China, Beijing, China

Synopsis

Keywords: DWI/DTI/DKI, DWI/DTI/DKI, Denoising, Deep Learning

Motivation: Diffusion Tensor Imaging (DTI) is limited by the long acquisition time and low signal-to-noise ratio. Noisy images hinder the observation of the anisotropy of water molecule diffusion, which further impacts clinical assessment.

Goal(s): To design a deep learning-based method for DTI signal-to-noise ratio improvement.

Approach: The proposed Dual-domain Tensor Denoising (DuTD) network leverages the structural, diffusion, and tensor information of DTI for denoising and fractional anisotropy (FA) generation.

Results: Extensive results show that DuTD can improve the signal-to-noise ratio and remain more diffusion information efficiently compared with other advanced methods.

Impact: The proposed DuTD network effectively enhances the SNR of images and describes diffusion information more accurately in both public datasets and private Parkinson's datasets.

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