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Proceedings Paper

Fiber feature map based landmark initialization for highly deformable DTI registration
Author(s): Aditya Gupta; Matthew Toews; Ravikiran Janardhana; Yogesh Rathi; John Gilmore; Maria Escolar; Martin Styner
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Paper Abstract

This paper presents a novel pipeline for the registration of diffusion tensor images (DTI) with large pathological variations to normal controls based on the use of a novel feature map derived from white matter (WM) fiber tracts. The research presented aims towards an atlas based DTI analysis of subjects with considerable brain pathologies such as tumors or hydrocephalus. In this paper, we propose a novel feature map that is robust against variations in WM fiber tract integrity and use these feature maps to determine a landmark correspondence using a 3D point correspondence algorithm. This correspondence drives a deformation field computed using Gaussian radial basis functions(RBF). This field is employed as an initialization to a standard deformable registration method like demons. We present early preliminary results on the registration of a normal control dataset to a dataset with abnormally enlarged lateral ventricles affected by fatal demyelinating Krabbe disease. The results are analyzed based on a regional tensor matching criterion and a visual assessment of overlap of major WM fiber tracts. While further evaluation and improvements are necessary, the results presented in this paper highlight the potential of our method in handling registration of subjects with severe WM pathology.

Paper Details

Date Published: 13 March 2013
PDF: 7 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866907 (13 March 2013); doi: 10.1117/12.2006977
Show Author Affiliations
Aditya Gupta, Univ. of Pittsburgh (United States)
Univ. of North Carolina at Chapel Hill (United States)
Matthew Toews, Harvard Medical School (United States)
Ravikiran Janardhana, Univ. of North Carolina at Chapel Hill (United States)
Yogesh Rathi, Harvard Medical School (United States)
John Gilmore, Univ. of North Carolina at Chapel Hill (United States)
Maria Escolar, Univ. of Pittsburgh (United States)
Martin Styner, Univ. of North Carolina at Chapel Hill (United States)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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