Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics

Christopher Schwarz, Robert I. Reid, Jeffrey L. Gunter, Matthew L. Senjem, Scott A. Przybelski, Samantha M. Zuk, Jennifer Lynn Whitwell, Prashanthi D Vemuri, Keith Anthony Josephs, Kejal M Kantarci, Paul M. Thompson, Ronald Carl Petersen, Clifford R Jr. Jack

Research output: Contribution to journalArticle

87 Citations (Scopus)

Abstract

Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter "skeleton" is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSS's skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projection's compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimer's disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each disease's pathophysiology.

Original languageEnglish (US)
Pages (from-to)65-78
Number of pages14
JournalNeuroImage
Volume94
DOIs
StatePublished - Jul 1 2014

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Skeleton
Anisotropy
Progressive Supranuclear Palsy
Alzheimer Disease
Software
Population

Keywords

  • DTI
  • Fractional Anisotropy
  • Registration
  • TBSS
  • VBM
  • Voxel-based analysis

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics. / Schwarz, Christopher; Reid, Robert I.; Gunter, Jeffrey L.; Senjem, Matthew L.; Przybelski, Scott A.; Zuk, Samantha M.; Whitwell, Jennifer Lynn; Vemuri, Prashanthi D; Josephs, Keith Anthony; Kantarci, Kejal M; Thompson, Paul M.; Petersen, Ronald Carl; Jack, Clifford R Jr.

In: NeuroImage, Vol. 94, 01.07.2014, p. 65-78.

Research output: Contribution to journalArticle

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