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Abstract #5156

Deep Learning-based Fast Calculation of Diffusion Tensor Distribution Parameters

Jiayin Zhou1, Zaimin Zhu1, Fangrong Zong1, Xiaofeng Deng2, Pak Shing Kenneth Or3, and Daniel Topgaard3
1School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, China, 2Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, 3Physical Chemistry, Lund University, Sölvegatan, Sweden

Synopsis

Keywords: AI/ML Image Reconstruction, Brain

Motivation: Current DTD methods rely on Monte Carlo inversion, which is computationally expensive and time-consuming, and treat each voxel independently, neglecting spatial relationships between neighboring voxels.

Goal(s): To accelerate the reconstruction of DTD parameter maps using deep learning, while maintaining accuracy.

Approach: The model estimates voxel-specific diffusion tensor parameters using spatial correlations in diffusion MRI data. It combines a transformer’s self-attention mechanism for encoding spatial dependencies and sparse dictionary learning for decoding.

Results: DTDMapper is a deep learning model that accelerates DTD parameter map reconstruction by over 100 times without sacrificing accuracy, showing high performance across various pathologies for broader disease applicability.

Impact: This study introduces deep learning for DTD parameter computation, overcoming computational complexity and spatial continuity issues of Monte Carlo methods, with potential for clinical translation across various diseases.

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