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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Alonso Ramirez-Manzanares , Penn Image Computing and Science Laboratory, Dept. of Radiology, Univ. of Pennsylvania, 3600 Market Street, Suite. 370, Philadelphia, 19104, USA
Hui Zhang , Penn Image Computing and Science Laboratory, Dept. of Radiology, Univ. of Pennsylvania, 3600 Market Street, Suite. 370, Philadelphia, 19104, USA
Mariano Rivera , Centro de Investigacion en Matematicas A.C., Callejon Jalisco S/N, Valenciana, Guanajuato, Gto. Mexico. C.P. 36240
James C. Gee , Penn Image Computing and Science Laboratory, Dept. of Radiology, Univ. of Pennsylvania, 3600 Market Street, Suite. 370, Philadelphia, 19104, USA
ABSTRACT
Diffusion weighted magnetic resonance images allows one to infer white matter axon fiber orientations. Nowadays it is possible to estimate intra-voxel orientations at voxels where fibers cross or split. Though, the recovered orientations could be prone to error because of low signal to noise ratio, the complex fiber structure and/or reduced number of measurements. Spatial regularization can improve the estimations but it must be done carefully such that real information is not removed and false orientations are not introduced. In this work we propose a robust method for regularizing local multi-fiber estimations given by the Diffusion Basis Function method. Our method is based on a robust outlier rejection framework which integrates data while preserves important local information. Additionally, our method constrains the solution orientation-space within a voxel by using the diffusion tensor diffusivity profile as prior information. Our experiments using both in vivo and realistic synthetic data demonstrate the advantage of using the proposed approach.
CITATION
Alonso Ramirez-Manzanares, Hui Zhang, Mariano Rivera, James C. Gee, "Robust regularization for the estimation of intra-voxel axon fiber orientations", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4562993
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