ODF based deep learning network for unsupervised deformable diffusion resonance image registration (ODDRnet).

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Abstract

In recent years, deep-learning algorithms have been widely adopted for medical-image registration owing to their high speed and accuracy, yet most works focus on scalar images. Diffusion MRI (dMRI) is primarily used to depict white-matter architecture in the brain. Because each voxel contains measurements acquired under multiple diffusion directions and b-values, dMRI constitutes a high-dimensional imaging modality. Existing techniques often neglect the directional information, particularly in regions with complex fiber crossings, compromising the alignment of white matter structures between individuals and potentially affecting subsequent analyses like tractography. Aligning Fiber Orientation Distribution Functions (fODF), which represent the distribution of fiber orientations per voxel, is a promising strategy to leverage directional information. This approach facilitates more anatomically precise correspondence of white matter pathways compared to methods relying solely on scalar information. This study proposes ODDRnet, an end-to-end, unsupervised deep-learning framework for nonlinear registration of dMRI data. ODDRnet directly aligns high-dimensional fODF derived from raw dMRI signals. It predicts a dense deformation field which is used to spatially warp the fODFs while appropriately reorienting the directional information, ensuring accurate alignment. ODDRnet was evaluated on multiple public and private datasets and compared with several state-of-the-art methods. Experimental results demonstrated that ODDRnet achieved superior macroscopic accuracy, with a mean increase of 0.02 in the tract dice and a reduction of 0.31 mm in tract distance. Furthermore, ODDRnet showed robust generalization across diverse ethnicities, age groups, health conditions, and imaging protocols, confirming its adaptability for various dMRI registration tasks.

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