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Kernel regression estimation of fiber orientation mixtures in diffusion MRI
Ryan P Cabeen1, Mark E Bastin2, David H Laidlaw1
1Department of Computer Science, Brown University, Providence, RI, USA.
Neuroimage
|December 23, 2015
Summary
This study introduces a kernel regression method for estimating brain white matter fiber orientations and volume fractions in diffusion MRI. The new approach improves tractography and creates more detailed multi-fiber atlases from clinical data.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Medical Image Analysis
Background:
- Diffusion MRI tractography is crucial for mapping brain white matter.
- Accurate estimation of fiber orientations and volume fractions is essential for clinical studies.
- Existing multi-compartment models face challenges in handling complex fiber crossings and data variability.
Purpose of the Study:
- To develop and evaluate a kernel regression method for estimating fiber orientations and volume fractions in diffusion MRI.
- To enhance tractography and enable the construction of population-based multi-fiber atlases for clinical imaging.
- To provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures.
Main Methods:
- A model-based image processing technique using kernel regression to estimate representative fiber models from diffusion MRI data.
- Incorporation of directional measures of divergence and data-adaptive extensions for model selection and bilateral filtering.
- Evaluation using synthetic data from computational phantoms and in vivo clinical data from human subjects.
Main Results:
- Experimental evaluation on synthetic data showed accurate estimation of fiber orientation, volume fraction, and compartment count.
- In vivo experiments demonstrated improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics.
- A multi-fiber tractography atlas created from 80 subjects revealed more complete white matter features compared to single-tensor atlasing.
Conclusions:
- The proposed kernel regression method effectively estimates fiber orientations and volume fractions for diffusion MRI tractography.
- This framework offers improved reconstruction of complex anatomical features and enhances the creation of detailed multi-fiber atlases.
- The data-adaptive extensions show potential for general multi-compartment image processing in clinical neuroscience research.
