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Fiber Feature Map Based Landmark Initialization for Highly Deformable DTI Registration

Institution:
1Department of Pediatrics, University of Pittsburgh, PA, USA.
2Surgical Planning Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
3Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
4Department of Pediatrics, University of Pittsburgh, PA, USA.
5Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Publication Date:
Mar-2013
Journal:
Proc Soc Photo Opt Instrum Eng
Volume Number:
8669
Citation:
Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669.
PubMed ID:
24353392
PMCID:
PMC3864965
Keywords:
3D Point Correspondence, DTI, fiber feature map, large deformation fields
Appears in Collections:
NA-MIC, PNL, SPL
Sponsors:
P30 HD003110/HD/NICHD NIH HHS/United States
R01 NS061965/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Gupta A., Toews M., Janardhana R., Rathi Y., Gilmore J., Escolar M., Styner M. Fiber Feature Map Based Landmark Initialization for Highly Deformable DTI Registration. Proc Soc Photo Opt Instrum Eng. 2013 Mar 13;8669. PMID: 24353392. PMCID: PMC3864965.
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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.

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