Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data

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 automatized algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data. The paper extends the usual concept of similarity in intensity (scalar) data to vector and tensor cases. A discussion on appropriate template selection and on the limitations of the template matching approach to incorporate the vector and tensor reorientation is also offered. Our approach to registration is based on a multiresolution scheme based on local matching of areas with a high degree of local structure and subsequent interpolation. 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 that outperforms conventional polynomial methods for the interpolation of sparse vector fields. The feasibility of the approach is illustrated by results on synthetic and clinical data.