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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