Nonrigid Registration of 3D Tensor Medical Data

J. Ruiz-Alzola, C.-F. Westin, S. K. Warfield, C. Alberola, S. E. Maier, R. Kikinis
Medical Image Analysis
Volume 6, Number 2, Pages 143-161
2002

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Abstract

New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, 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.

DT-MRI interpatient warping. (a), (b) Simplified views of axial DTMRI from different individuals. The curvilinear grid illustrates the deformation.

Reference

Ruiz-Alzola J, Westin CF, Warfield SK, Alberola C, Maier SE, Kikinis R. Nonrigid registration of 3d tensor medical data. Medical Image Analysis 2002;6(2):143-161.

Bibtex entry

@Article{ruizMEDIA02,
  author         = {J. Ruiz-Alzola and C.-F. Westin and S. K. Warfield and  C. 
                   Alberola and S. E. Maier and R. Kikinis},                   
  title          = {Nonrigid Registration of 3D Tensor Medical Data},          
  journal        = {Medical Image Analysis},                                   
  year           = 2002,                                                       
  volume         = 6,                                                          
  number         = 2,                                                          
  pages          = {143--161}
}

Grants

NIH P41-RR13218 (NAC), FPU PRI-19990175, NIH P41-RR13218 (NAC), NIH P01-CA67165, NIH R01-RR11747, 1FD97-0881-C02

Research areas

DTMRI, Tensor

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