Cluster Confidence Index: A Streamline‐Wise Pathway Reproducibility Metric for Diffusion‐Weighted MRI Tractography

Journal of Neuroimaging - Tập 28 Số 1 - Trang 64-69 - 2018
Kesshi Jordan1,2, Bagrat Amirbekian1,2, Anisha Keshavan1,2, Roland G. Henry1,3,2
1Departments of Neurology University of California San Francisco CA
2UCSF-UC Berkeley Graduate Group in Bioengineering San Francisco and Berkeley CA
3Radiology and Biomedical Imaging University of California San Francisco CA

Tóm tắt

ABSTRACTBACKGROUNDDiffusion‐weighted magnetic resonance imaging tractography can be used to create models of white matter fascicles. Anatomical and pathological variability between subjects can drastically alter the tractography output, so standardizing results across a cohort is nontrivial. Furthermore, tractography methods have inherently low reproducibility due to stochasticity (for probabilistic methods) and subjective decisions, since the final fascicle model often requires a manual intervention step performed by an expert human operator to control both outliers and systematic false‐positive pathways, as defined by prior knowledge of anatomy.METHODSWe present an approach that computationally assigns a cluster confidence index (CCI) reflecting the reproducibility of that pathway in the context of a streamline dataset. This metric is a tractography algorithm‐agnostic tool that can be applied to any dataset of streamlines.RESULTSApplications of this metric include systematic elimination of outlier streamlines using a CCI threshold and interactive filtering by CCI to facilitate manual segmentation of fascicle models.CONCLUSIONSThis method is intended to replace the application of a streamline density threshold so that outliers are eliminated based on low pathway density instead of voxel‐wise density.

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Tài liệu tham khảo

10.2463/mrms.8.165

10.1016/S1474-4422(08)70163-7

10.3171/jns.2004.101.1.0066

10.1016/j.cortex.2013.08.005

10.1016/j.neuroimage.2011.03.022

10.1006/jmrb.1994.1037

10.1002/mrm.1910360612

Beaulieu C., 2006, The biological basis of diffusion tractography, Proc IEEE Int Symp Biomed Imaging, 3, 347

10.1016/j.neuroimage.2012.06.081

Aganj I, 2009, ODF reconstruction in Q‐ball imaging with constant solid angle consideration, Proc IEEE Int Symp Biomed Imaging, 2009, 1398

10.1002/mrm.21277

10.1016/j.neuroimage.2007.02.016

10.1002/mrm.20279

10.1002/mrm.10609

10.1016/j.neuroimage.2007.08.021

10.1016/j.neuroimage.2006.09.018

10.1002/ima.22005

10.1016/j.nicl.2013.08.008

10.3171/2014.4.JNS131160

10.1109/TMI.2008.2004424

10.1016/j.cortex.2008.05.004

10.3171/2015.6.JNS142203

10.1016/j.wneu.2013.01.004

10.1002/nbm.3266

10.1016/j.neuroimage.2007.02.049

10.3389/fnins.2012.00175

10.3389/fninf.2014.00008

Wang R, 2007, Diffusion toolkit: a software package for diffusion imaging data processing and tractography, Proc Int Soc Mag Reson Med, 15, 3720

10.1016/j.neuroimage.2011.09.015

10.1002/mrm.21890

10.1016/j.neuroimage.2009.04.049

10.1016/j.media.2009.11.001

10.1007/978-3-319-41501-7_71

Côté MA, 2015, Cleaning up the mess: tractography outlier removal using hierarchical quickbundles clustering, Proc Int Soc Mag Reson Med, 23, 2844

MeestersS SanguinettiG GaryfallidisG et al.Cleaning output of tractography via fiber to bundle coherence a new open source implementation (poster). Presented at the Organization for Human Brain Mapping Annual Meeting June 30 2016 Geneva. Available at:http://bmia.bmt.tue.nl/people/RDuits/ERCprog_2.pdf. Accessed August 8 2017.