Fully automatic stitching of diffusion tensor images in spinal cord

Abstract

Diffusion tensor imaging (DTI) has become an important tool for studying the spinal cord pathologies. To enable high resolution imaging for modern studies, the DTI technique utilizes a small field of view (FOV) to capture partial human spinal cords. However, normal aging and many other diseases which affect the entire spinal cord increase the desire of acquiring the continuous full-view of the spinal cord. To overcome this problem, this paper presents a novel pipeline for automatic stitching of three-dimensional (3D) DTI of different portions of the spinal cord. The proposed technique consists of two operations, e.g. feature-based registration and adaptive composition to stitch every source image together to create a panoramic image. In the feature-based registration process, feature points are detected from the apparent diffusion coefficient map, and then a novel feature descriptor is designed to characterize feature points directly from a tensor neighborhood. 3D affine transforms are achieved by determining the correspondence matching. In the adaptive composition process, an effective feathering approach is presented to compute the tensors in the overlap region by the Log-Euclidean metrics. We evaluate the algorithm on real datasets from one healthy subject and one adolescent idiopathic scoliosis (AIS) patient. The colored FA maps and fiber tracking results show the effectiveness and accuracy of the proposed stitching framework.