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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.


Corticospinal tract (CST) and SLF-II traced using the 2-fiber NODDI model. Intersecting fibers are seen in the centrum-semiovale region.
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Figure 3: Corticospinal tract (CST) and SLF-II traced using the 2-fiber NODDI model. Intersecting fibers are seen in the centrum-semiovale region.

Mentions: Figure 3 shows results for the 2-fiber NODDI model obtained using the UIF filter. The cortico-spinal tract (shown in color) and the superior-longitudinal fasiculus II (SLF-II) intersect in the centrum-semiovale region. As can be seen, the proposed algorithm is able to trace fibers through crossing regions, which is not possible using the 1-fiber NODDI model. Also, the lateral fibers of the corticospinal tract (CST) that go to the hand and face area are missing in the 1-fiber NODDI model, but can be nicely traced using the 2-fiber NODDI model.


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

Reddy CP, Rathi Y - Front Neurosci (2016)

Corticospinal tract (CST) and SLF-II traced using the 2-fiber NODDI model. Intersecting fibers are seen in the centrum-semiovale region.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 3: Corticospinal tract (CST) and SLF-II traced using the 2-fiber NODDI model. Intersecting fibers are seen in the centrum-semiovale region.
Mentions: Figure 3 shows results for the 2-fiber NODDI model obtained using the UIF filter. The cortico-spinal tract (shown in color) and the superior-longitudinal fasiculus II (SLF-II) intersect in the centrum-semiovale region. As can be seen, the proposed algorithm is able to trace fibers through crossing regions, which is not possible using the 1-fiber NODDI model. Also, the lateral fibers of the corticospinal tract (CST) that go to the hand and face area are missing in the 1-fiber NODDI model, but can be nicely traced using the 2-fiber NODDI model.

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.