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Abstract #4054

SdADC: Semi-supervised Diffusion MRI Artefact Detection and Classification

Xinyi Wang1,2, Zhenlin Liu2, Sheng Chen1,2, Hao Li3, He Wang3, Michael Barnett4, Weidong Cai2, Chenyu Wang1,4,5, and Zihao Tang1,2,5
1Brain and Mind Centre, The University of Sydney, Sydney, Australia, 2School of Computer Science, The University of Sydney, Sydney, Australia, 3Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 4Sydney Neuroimaging Analysis Centre, Sydney, Australia, 5Central Clinical School, The University of Sydney, Sydney, Australia

Synopsis

Keywords: Diffusion Analysis & Visualization, Artifacts

Motivation: Diffusion magnetic resonance imaging (dMRI) is prone to artefacts, which can significantly impact the preprocessing and downstream analysis.

Goal(s): Develop automatic methods to detect and classify the dMRI artefacts to exclude problematic cases for further analysis.

Approach: A two-stage deep learning-based framework is proposed to detect the artefacts using angular resolution enhanced fractional anisotropy (FA) and then classify the specific type of artefacts.

Results: The proposed method shows consistently good performance in dMRI artefact detection and classification across HCP and PPMI datasets.

Impact: Our method improves dMRI data reliability by automating artifact detection and classification using a two-stage deep learning approach with angular resolution-enhanced FA. The propsoed framework consistently identifies and categorizes artifacts, enhancing preprocessing and analysis across large-scale diffusion MRI datasets.

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Keywords