Tractography Processing with the Sparse Closest Point Transform
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ORIGINAL ARTICLE
Tractography Processing with the Sparse Closest Point Transform Ryan P. Cabeen1
· Arthur W. Toga1 · David H. Laidlaw2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scanrescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit. Keywords Diffusion MRI tractography · Clustering · Simplification · Segmentation · Fiber bundles · Sparse closest point transform · Neuroimaging
Introduction Diffusion MR imaging provides a unique in-vivo probe of tissue microstructure through the sensing of water molecule diffusion patterns (Pierpaoli and Basser 1996). This technique is particularly valuable for characterizing the local features of white matter and for reconstructing the large scale structure of fiber bundles through tractography (Basser and Pierpaoli 1996). The size and complexity of tractography datasets can pose a challenge to the practical application of tractography in neuroimaging studies, as delineating Ryan P. Cabeen
[email protected] 1
Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
2
Department of Computer Science, Brown University, Providence, RI, USA
fiber bundle pathways from whole brain tractography often involves expert anatomical knowledge and time consuming manual interaction (O’Donnell et al. 2013) (Lenglet et al.
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