Experimental validation of an intrasubject elastic registration algorithm for dynamic‐3D ultrasound images

Purpose: In image‐guided therapy, real‐time visualization of the anatomy and adjustments in the therapy plan due to anatomical motions during the procedure is of outmost importance. 3D ultrasound has the potential to enable this real‐time monitoring; however, nonrigid registration of a sequence of 3D ultrasound volumes remains to be a challenging problem. The authors present our recent results on the development of a computationally inexpensive feature‐based registration algorithm for elastic alignment of dynamic‐3D ultrasound images. Methods: Our algorithm uses attribute vectors, based on the image intensity and gradient information, to perform feature‐based matching in a sequence of 3D ultrasound images. Prior information from both the fixed and previous moving images is utilized to track features throughout the 3D image series. The algorithm has been compared to various publicly available registration techniques, i.e., the B‐splines deformable registration, the symmetric forces Demons, and the fast free‐form deformable registration method. Results: Using a series of validation experiments on datasets collected from carotid artery, liver, and kidney of 20 subjects, the authors demonstrate that the feature‐based, B‐splines, Demons, and fast free‐form deformable registration techniques can all recover volume deformations in a 3D ultrasound image series with reasonable accuracy; however, the proposed feature‐based registration technique has substantial computational advantage over the other approaches. Conclusions: The proposed feature‐based registration technique has the potential for real‐time implementation on a computationally inexpensive platform and has the capability of recovering nonrigid deformations in tissue with reasonable accuracy.