Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47460
Title: Nonrigid registration of 3D tensor medical data
Authors: Ruiz-Alzola, J. 
Westin, C. F.
Warfield, S. K.
Alberola, C.
Maier, S.
Kikinis, R.
UNESCO Clasification: 3314 Tecnología médica
Keywords: Diffusion Tensor
Image Registration
Similarity Measures
Point Landmarks
Human Brain, et al
Issue Date: 2002
Publisher: 1361-8415
Journal: Medical Image Analysis 
Abstract: New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MM, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data. (C) 2002 Elsevier Science B.V. All rights reserved.
URI: http://hdl.handle.net/10553/47460
ISSN: 1361-8415
DOI: 10.1016/S1361-8415(02)00055-5
Source: Medical Image Analysis[ISSN 1361-8415],v. 6, p. 143-161
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