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A review on learning-based algorithms for tractography and human brain white matter tracts recognition
Amin Barati Shoorche1,2, Parastoo Farnia1,3, Bahador Makkiabadi4,5
1Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Science (TUMS), Tehran, Iran.
Neuroradiology
|June 4, 2025
Summary
This study reviews learning-based algorithms for human brain fiber tractography using diffusion MRI. These advanced methods enhance the accuracy of mapping white matter tracts and aid in surgical planning.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Medical Image Analysis
Background:
- Diffusion magnetic resonance imaging (dMRI) is essential for mapping brain white matter.
- Accurate tractography aids in surgical planning and understanding neural connectivity.
- Traditional methods face limitations, driving the need for advanced algorithms.
Purpose of the Study:
- To review learning-based algorithms for dMRI tractography.
- To discuss tractography and tract recognition methods.
- To assess the efficiency of learning-based approaches.
Main Methods:
- Review of conventional machine learning, deep learning, reinforcement learning, and dictionary learning.
- Analysis of methods for white matter tract, nerve, and pathway recognition.
- Examination of techniques for whole brain streamline and tractogram creation.
Main Results:
- Learning-based algorithms show promising results in tractography.
- Recent methods leverage advanced machine learning techniques.
- These algorithms improve recognition of neural pathways.
Conclusions:
- Learning-based methods play a significant role in advancing tractography.
- This review provides a comprehensive comparison of these methods.
- The study highlights the importance of these techniques for neuroimaging applications.
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