Non-rigid registration of medical images based on estimation of deformation states

Abstract

A unified framework for automatic non-rigid 3D-3D and 3D-2D registration of medical images with static and dynamic deformations is proposed in this paper. The problem of non-rigid image registration is approached as a classical state estimation problem using a generic deformation model for the soft tissue. The registration technique employs a dynamic linear elastic continuum mechanics model of the tissue deformation, which is discretized using the finite element method. In the proposed method, the registration is achieved through a Kalman-like filtering process, which incorporates information from the deformation model and a vector of observation prediction errors computed from an intensity-based similarity/distance metric between images. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework. The performance of the proposed registration technique was evaluated in a number of different registration scenarios. First, 3D magnetic resonance (MR) images of uncompressed and compressed breast tissue were co-registered. 3D MR images of the uncompressed breast tissue were also registered to a sequence of simulated 2D interventional MR images of the compressed breast. Finally, the registration algorithm was employed to dynamically track a target sub-volume inside the breast tissue during the process of the biopsy needle insertion based on registering pre-insertion 3D MR images to a sequence of real-time simulated 2D interventional MR images. Registration results indicate that the proposed method can be effectively employed for the registration of medical images in image-guided procedures, such as breast biopsy in which the tissue undergoes static and dynamic deformations.

Robotic Insertion of Flexible Needle in Deformable Structures Using Inverse Finite-Element Simulation

“…As opposed to image-to-image registration, biomechanical models provide a physics-based extrapolation, not just geometric, in areas where few or no intraoperative data are available (see [38] for a detailed discussion). Marami et al [39] presents a method for non-rigid 3D to 3D and 3D to 2D registration of medical images. The registration technique employs a dynamic linear elastic model of the tissue to dynamically track a target inside breast tissue during a biopsy.…”

A review of computer-aided oral and maxillofacial surgery: planning, simulation and navigation

“…Currently, some non-rigid registration algorithms have been proposed aiming at compensating for tissue deformation, or aligning images from different subjects. For example, Marami et al (2014) presented a non-rigid image registration algorithm employing a dynamic FE-based linear elastic model of tissue deformation, which was applicable to 3D-3D and 3D-2D, single and multi-modality image registration problems [54]. However, the further development requires a higher accuracy for the actual clinical applications.…”