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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
A fractional order-based mixture of central Wishart (FMoCW) model for reconstructing white matter fibers from
Ashishi Puri1, Snehlata Shakya2, Sanjeev Kumar3
1Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, Uttarakhand, India.
Abstract:
This paper introduces an algorithm for reconstructing the brain's white matter fibers (WMFs). In particular, a fractional order mixture of central Wishart (FMoCW) model is proposed to reconstruct the WMFs from diffusion MRI data. The pseudo super diffusive modality of anomalous diffusion is coupled with the mixture of central Wishart (MoCW) model to derive the proposed model. We have shown results on multiple synthetic simulations, including fibers orientations in 2 and 3 directions per voxel and experiments on real datasets of rat optic chiasm and a healthy human brain. In synthetic simulations, a varying Rician distributed noise levels, σ=0.01-0.09 is also considered. The proposed model can efficiently distinguish multiple fibers even when the angle of separation between fibers is very small. This model outperformed, giving the least angular error when compared to fractional mixture of Gaussian (MoG), MoCW and mixture of non-central Wishart (MoNCW) models.
Insights
This study presents a new fractional order mixture of central Wishart (FMoCW) model for reconstructing white matter fibers (WMFs) from diffusion MRI data. The FMoCW model accurately distinguishes complex fiber orientations, outperforming existing methods with reduced angular error.
Area of Science:
- Neuroimaging
- Biophysics
- Computational Neuroscience
Background:
- Diffusion MRI is crucial for mapping brain white matter fibers (WMFs).
- Accurate reconstruction of WMFs, especially in complex regions, remains challenging.
- Existing models struggle with small fiber angles and noise.
Purpose of the Study:
- To introduce a novel algorithm for reconstructing WMFs.
- To propose a new fractional order mixture of central Wishart (FMoCW) model.
- To evaluate the performance of the FMoCW model against established methods.
Main Methods:
- Developed a fractional order mixture of central Wishart (FMoCW) model.
- Coupled pseudo super diffusive modality of anomalous diffusion with the mixture of central Wishart (MoCW) model.
- Tested the model on synthetic data with varying noise levels and real datasets (rat optic chiasm, human brain).
Main Results:
- The FMoCW model successfully reconstructed WMFs from diffusion MRI data.
- It efficiently distinguished multiple fiber orientations, even with small angular separation.
- Achieved the least angular error compared to fractional mixture of Gaussian (MoG), MoCW, and mixture of non-central Wishart (MoNCW) models.
Conclusions:
- The proposed FMoCW model offers superior performance in WMF reconstruction.
- It demonstrates robustness in the presence of Rician noise.
- This advancement has significant implications for neuroimaging analysis and understanding brain connectivity.
