Diffusion simulation-based fiber tracking using time-of-arrival maps: a comparison with standard methods
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We propose a new tracking method based on time-of-arrival (TOA) maps derived from simulated diffusion processes.
Materials and methods
The proposed diffusion simulation-based tracking consists of three steps that are successively evaluated on small overlapping sub-regions in a diffusion tensor field. First, the diffusion process is simulated for several time steps. Second, a TOA map is created to store simulation results for the individual time steps that are required for the tract reconstruction. Third, the fiber pathway is reconstructed on the TOA map and concatenated between neighboring sub-regions. This new approach is compared with probabilistic and streamline tracking. All methods are applied to synthetic phantom data for an easier evaluation of their fiber reconstruction quality.
The comparison of the tracking results did show severe problems for the streamline approach in the reconstruction of crossing fibers, for example. The probabilistic method was able to resolve the crossing, but could not handle strong curvature. The new diffusion simulation-based tracking could reconstruct all problematic fiber constellations.
The proposed diffusion simulation-based tracking method used the whole tensor information of a neighborhood of voxels and is, therefore, able to handle problematic tracking situations better than established methods.
KeywordsFiber tracking DSBT DTI
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- 2.Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Imaging 44: 625–632Google Scholar
- 6.Parker GJM, Haroon HA, Wheeler-Kingshott CA (2003) A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. Magn Reson Med 18: 242–254Google Scholar
- 9.Batchelor PG, Hill DLG, Calamante F, Atkinson D (2001) Study of connectivity in the brain using the full diffusion tensor from MRI. In: Proceedings of the 17th international conference on information processing in medical imaging, pp 121–133Google Scholar
- 10.Gembris D (2001) Doctorate Thesis: Rekonstruktion Neuronaler Konnektivität Mittels Kernmagnetischer Resonanz. University of Dortmund, GermanyGoogle Scholar
- 11.Mang S, Gembris D, Männer R (2005) Tracking white matter fibers using time of arrival maps. In: 22nd Annual meeting of ESMRMBGoogle Scholar
- 13.Deoni SCL, Jones DK (2005) Generation of a common diffusion tensor imaging dataset. In: Proceedings of the ISMRM workshop on methods for quantitative diffusion MRI of human brain, http://cubric.psych.cf.ac.uk/commondti
- 15.Morton K, Mayers D (1994) Numerical solution of partial differential equations. Cambridge University Press, CambridgeGoogle Scholar
- 16.Hackbusch W (1994) Iterative solution of large sparse systems of equations. Springer, New YorkGoogle Scholar
- 17.Mang S (2008) Doctorate Thesis: Diffusion simulation based tracking and optimized gradient encoding schemes for diffusion magnetic resonance imaging, University of Mannheim, GermanyGoogle Scholar
- 18.Kang N, Balls GT, Frank LR (2009) Diffusion simulation tractography for higher angular resolution diffusion imaging. In: Proceedings of the 17th annual metting of the ISMRM, Honolulu, p 1437Google Scholar