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Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter.

Reddy CP, Rathi Y - Front Neurosci (2016)

Bottom Line: We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography.The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF).Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity.

View Article: PubMed Central - PubMed

Affiliation: Data Analytics, Walmart ISD Bangalore, India.

ABSTRACT
Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

No MeSH data available.


Comparision of results between 1-fiber NODDI model tractography(red) and 2-fiber NODDI model tractography(green) for the IOFF fibers.
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Figure 6: Comparision of results between 1-fiber NODDI model tractography(red) and 2-fiber NODDI model tractography(green) for the IOFF fibers.

Mentions: Figure 6 shows a comparison of the traced inferior occipito-frontal fibers (IOFF) as traced by the 1-fiber (red) and 2-fiber (green) NODDI model. The 2-fiber NODDI manages to trace and connect a different part of gray matter region that is missed by the 1-fiber NODDI model. In fact the 1-fiber tracts only trace the medial portion of the lateral occipital cortex, whereas the 2-fiber NODDI tracts cover most of the lateral-occipital cortex as labeled by Freesurfer. Thus, the 2-fiber NODDI potentially provides a better estimate of fiber connectivity.


Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter.

Reddy CP, Rathi Y - Front Neurosci (2016)

Comparision of results between 1-fiber NODDI model tractography(red) and 2-fiber NODDI model tractography(green) for the IOFF fibers.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4837399&req=5

Figure 6: Comparision of results between 1-fiber NODDI model tractography(red) and 2-fiber NODDI model tractography(green) for the IOFF fibers.
Mentions: Figure 6 shows a comparison of the traced inferior occipito-frontal fibers (IOFF) as traced by the 1-fiber (red) and 2-fiber (green) NODDI model. The 2-fiber NODDI manages to trace and connect a different part of gray matter region that is missed by the 1-fiber NODDI model. In fact the 1-fiber tracts only trace the medial portion of the lateral occipital cortex, whereas the 2-fiber NODDI tracts cover most of the lateral-occipital cortex as labeled by Freesurfer. Thus, the 2-fiber NODDI potentially provides a better estimate of fiber connectivity.

Bottom Line: We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography.The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF).Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity.

View Article: PubMed Central - PubMed

Affiliation: Data Analytics, Walmart ISD Bangalore, India.

ABSTRACT
Tracing white matter fiber bundles is an integral part of analyzing brain connectivity. An accurate estimate of the underlying tissue parameters is also paramount in several neuroscience applications. In this work, we propose to use a joint fiber model estimation and tractography algorithm that uses the NODDI (neurite orientation dispersion diffusion imaging) model to estimate fiber orientation dispersion consistently and smoothly along the fiber tracts along with estimating the intracellular and extracellular volume fractions from the diffusion signal. While the NODDI model has been used in earlier works to estimate the microstructural parameters at each voxel independently, for the first time, we propose to integrate it into a tractography framework. We extend this framework to estimate the NODDI parameters for two crossing fibers, which is imperative to trace fiber bundles through crossings as well as to estimate the microstructural parameters for each fiber bundle separately. We propose to use the unscented information filter (UIF) to accurately estimate the model parameters and perform tractography. The proposed approach has significant computational performance improvements as well as numerical robustness over the unscented Kalman filter (UKF). Our method not only estimates the confidence in the estimated parameters via the covariance matrix, but also provides the Fisher-information matrix of the state variables (model parameters), which can be quite useful to measure model complexity. Results from in-vivo human brain data sets demonstrate the ability of our algorithm to trace through crossing fiber regions, while estimating orientation dispersion and other biophysical model parameters in a consistent manner along the tracts.

No MeSH data available.