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Neuroimage. 2015 Apr 1;109:341-56. doi: 10.1016/j.neuroimage.2014.12.060. Epub 2014 Dec 30.

Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values.

Author information

1
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Children's Hospital of Los Angeles, Los Angeles, CA, USA; Department of Radiology, Keck School of Medicine of USC, Los Angeles, CA, USA.
2
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; Center of Magnetic Resonance Research, Korea Basic Science Institute, Ochang, South Korea.
3
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Keck School of Medicine of USC, Los Angeles, CA, USA.
4
Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA; Department of Radiology, Children's Hospital of Los Angeles, Los Angeles, CA, USA. Electronic address: nlepore@chla.usc.edu.

Abstract

Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, tractography and the ability to recover complex white-matter pathways, differences between results due to choice of analysis method, and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work, we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment "ball and stick" model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm(2)) common to clinical studies. We found that the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for complex three-fiber crossing regions. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project "Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations" at http://www.nitrc.org/projects/sim_dwi_brain.

KEYWORDS:

Diffusion-weighted MRI; Multiple-fiber estimation; Quantitative metrics; Simulated data; Tractography

PMID:
25555998
PMCID:
PMC4600612
DOI:
10.1016/j.neuroimage.2014.12.060
[Indexed for MEDLINE]
Free PMC Article

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