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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.


Arcuate Fasciculus traced using 1-fiber NODDI model. Background is the b = 0 image, where very bright regions indicate CSF areas. As seen, higher error in data fitting occurs only in the CSF areas, which is extremely noisy with free isotropic diffusion.
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Figure 7: Arcuate Fasciculus traced using 1-fiber NODDI model. Background is the b = 0 image, where very bright regions indicate CSF areas. As seen, higher error in data fitting occurs only in the CSF areas, which is extremely noisy with free isotropic diffusion.

Mentions: Nevertheless, the proposed method has a few limitations. First, we assume equal volume fraction for each of the crossing fibers in our 2-fiber NODDI model, which may not be accurate. Second, the model fit to the data in the CSF areas is poor due to high noise in the data itself, as seen in Figure 7. However, the error in most white and gray matter areas is quite low, i.e., less than 2%. Another limitation of the current implementation of the proposed method is its inability to trace more than 2 fiber crossings. While an extension to trace 3 fiber model is straightforward, it can be done in areas which are a-priori known to have 3 fiber crossings. However, we believe that using a 3-fiber model for tracing all fibers would result in over-fitting of the data. Yet, we should note that the a majority of the white matter voxels have two crossings, and a very small region has more than 2 fiber crossings. Thus, the proposed method can be applied in most parts of the brain to trace fibers and estimate the dispersion index of each fiber separately, which is a significant improvement over the existing single-fiber based model of Zhang et al. (2012).


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

Reddy CP, Rathi Y - Front Neurosci (2016)

Arcuate Fasciculus traced using 1-fiber NODDI model. Background is the b = 0 image, where very bright regions indicate CSF areas. As seen, higher error in data fitting occurs only in the CSF areas, which is extremely noisy with free isotropic diffusion.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 7: Arcuate Fasciculus traced using 1-fiber NODDI model. Background is the b = 0 image, where very bright regions indicate CSF areas. As seen, higher error in data fitting occurs only in the CSF areas, which is extremely noisy with free isotropic diffusion.
Mentions: Nevertheless, the proposed method has a few limitations. First, we assume equal volume fraction for each of the crossing fibers in our 2-fiber NODDI model, which may not be accurate. Second, the model fit to the data in the CSF areas is poor due to high noise in the data itself, as seen in Figure 7. However, the error in most white and gray matter areas is quite low, i.e., less than 2%. Another limitation of the current implementation of the proposed method is its inability to trace more than 2 fiber crossings. While an extension to trace 3 fiber model is straightforward, it can be done in areas which are a-priori known to have 3 fiber crossings. However, we believe that using a 3-fiber model for tracing all fibers would result in over-fitting of the data. Yet, we should note that the a majority of the white matter voxels have two crossings, and a very small region has more than 2 fiber crossings. Thus, the proposed method can be applied in most parts of the brain to trace fibers and estimate the dispersion index of each fiber separately, which is a significant improvement over the existing single-fiber based model of Zhang et al. (2012).

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.