Abstract
Automated probabilistic reconstruction of white matter pathways facilitates tractography in large studies. TRACULA (TRActs Constrained by UnderLying Anatomy) follows a Markov-chain Monte Carlo (MCMC) approach that is compute-intensive. TRACULA is available on our Neuroscience Gateway (NSG), a user-friendly environment for fully automated data processing on grid computing resources. Despite the robustness of TRACULA, our users and others have reported incidents of partially reconstructed tracts. Investigation revealed that in these situations the MCMC algorithm is caught in local minima. We developed a method that detects unsuccessful tract reconstructions and iteratively repeats the sampling procedure while maintaining the anatomical priors to reduce computation time. The anatomical priors are recomputed only after several unsuccessful iterations. Our method detects affected tract reconstructions by analyzing the dependency between samples produced by the MCMC algorithm. We extensively validated the original and the modified methods by performing five repeated reconstructions on a dataset of 74 HIV-positive patients and 47 healthy controls. Our method increased the rate of successful reconstruction in the two most prominently affected tracts (forceps major and minor) on average from 74% to 99%. In these tracts, no group difference in FA and MD was found, while a significant association with age could be confirmed.
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Keywords
- Fractional Anisotropy
- Markov Chain Monte Carlo
- Markov Chain Monte Carlo Algorithm
- Uncinate Fasciculus
- Successful Reconstruction
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Fischl, B.: FreeSurfer. NeuroImage 62(2), 774–781 (2012)
Hatton, S.N., Lagopoulos, J., Hermens, D.F., Hickie, I.B., Scott, E., Bennett, M.R.: White matter tractography in early psychosis: clinical and neurocognitive associations. Journal of Psychiatry & Neuroscience: JPN 39(6), 417–427 (2014)
Jbabdi, S., Woolrich, M.W., Andersson, J.L.R., Behrens, T.E.J.: A Bayesian framework for global tractography. NeuroImage 37(1), 116–129 (2007)
Lazar, M.: Mapping brain anatomical connectivity using white matter tractography. NMR in Biomedicine 23(7), 821–835 (2010)
Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., Beaulieu, C.: Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage 60(1), 340–352 (2012)
Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A.V., Mahmood, A., Woods, R., Toga, A., Pike, G., Neto, P., Evans, A., Zhang, J., Huang, H., Miller, M., van Zijl, P., Mazziotta, J.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40(2), 570–582 (2008)
Shahand, S., Benabdelkader, A., Jaghoori, M.M., al Mourabit, M., Huguet, J., Caan, M.W.A., van Kampen, A.H.C., Olabarriaga, S.D.: A Data-Centric Neuroscience Gateway: Design, Implementation, and Experiences. Concurrency and Computation: Practice and Experience 27(2), 489–506 (2015)
Woolrich, M.W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., Smith, S.M.: Bayesian analysis of neuroimaging data in FSL. NeuroImage 45(1), S173–S186 (2009)
Yendiki, A., Panneck, P., Srinivasan, P., Stevens, A., Zöllei, L., Augustinack, J., Wang, R., Salat, D., Ehrlich, S., Behrens, T., Jbabdi, S., Gollub, R., Fischl, B.: Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Frontiers in Neuroinformatics 5(23), 1–12 (2011)
Zhang, W., Olivi, A., Hertig, S., Zijl, P.V., Mori, S.: Automated fiber tracking of human brain white matter using diffusion tensor imaging. Neuroimage 42(2), 771–777 (2008)
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© 2015 Springer International Publishing Switzerland
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Teeuw, J., Caan, M.W.A., Olabarriaga, S.D. (2015). Robust Automated White Matter Pathway Reconstruction for Large Studies. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_13
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DOI: https://doi.org/10.1007/978-3-319-24553-9_13
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