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Related Experiment Video

Updated: Feb 19, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Asymmetric fiber orientation distribution estimation via unsupervised deep learning.

Di Zhang1, Ziyu Li2, Xiaofeng Deng3

  • 1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

Medical Image Analysis
|February 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Recursive-a-fODF, a novel deep learning method for estimating asymmetric fiber orientations from diffusion MRI data. This approach enhances brain connectivity mapping and reveals disease-specific microstructural changes.

Keywords:
Diffusion magnetic resonance imagingfiber orientation distribution functionunsupervised deep learning

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Diffusion magnetic resonance imaging (dMRI) tractography reconstructs brain structural connectivity but is limited by the assumption of symmetric fiber orientation distribution functions (fODFs).
  • This enforced symmetry can hinder accurate modeling in brain regions with complex, asymmetric white matter microstructures.
  • Existing methods to address this often require external anatomical priors or labeled data, limiting their broad applicability.

Purpose of the Study:

  • To develop and validate an unsupervised deep learning framework, Recursive-a-fODF, for directly estimating asymmetric fODFs (a-fODFs) from dMRI data.
  • To eliminate the need for anatomical priors by incorporating a data-driven recursive calibration process for estimating the white matter response function.
  • To demonstrate the utility of a-fODF modeling for resolving complex fiber configurations and identifying disease-specific microstructural alterations.

Main Methods:

  • Implementation of Recursive-a-fODF, an unsupervised deep learning model for estimating a-fODFs directly from dMRI.
  • A recursive calibration process within the model dynamically estimates the white matter response function from the data itself.
  • Validation using ex vivo marmoset and in vivo human brain datasets, including clinical cohorts with neurodegenerative and psychiatric conditions.

Main Results:

  • Recursive-a-fODF demonstrated superior performance in resolving complex fiber configurations compared to conventional methods.
  • Application to clinical cohorts revealed disease-specific alterations in fiber orientation asymmetry, highlighting the method's sensitivity.
  • The data-driven estimation of a-fODFs successfully captured microstructural signatures relevant to disease pathology.

Conclusions:

  • Recursive-a-fODF provides a powerful, anatomically unbiased method for estimating asymmetric fiber orientations, overcoming limitations of traditional tractography.
  • This approach offers a complementary dimension to conventional diffusion MRI metrics and establishes a-fODF modeling as a valuable tool.
  • The developed framework advances tractography accuracy and opens new avenues for sensitive neuroimaging biomarkers in neurological and psychiatric disorders.