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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