Integrating Retraction Modeling Into an Atlas-Based Framework for Brain Shift Prediction.

In recent work, an atlas-based statistical model for brain shift prediction, which accounts for uncertainty in the intraoperative environment, has been proposed. Previous work reported in the literature using this technique did not account for local deformation caused by surgical retraction. It is challenging to precisely localize the retractor location prior to surgery and the retractor is often moved in the course of the procedure. This paper proposes a technique that involves computing the retractor-induced brain deformation in the operating room through an active model solve and linearly superposing the solution with the precomputed deformation atlas. As a result, the new method takes advantage of the atlas-based framework's accounting for uncertainties while also incorporating the effects of retraction with minimal intraoperative computing. This new approach was tested using simulation and phantom experiments. The results showed an improvement in average shift correction from 50% (ranging from 14 to 81%) for gravity atlas alone to 80% using the active solve retraction component (ranging from 73 to 85%). This paper presents a novel yet simple way to integrate retraction into the atlas-based brain shift computation framework.

Automatic generation of boundary conditions using demons nonrigid image registration for use in 3-D modality-independent elastography.

Modality-independent elastography (MIE) is a method of elastography that reconstructs the elastic properties of tissue using images acquired under different loading conditions and a biomechanical model. Boundary conditions are a critical input to the algorithm and are often determined by time-consuming point correspondence methods requiring manual user input. This study presents a novel method of automatically generating boundary conditions by nonrigidly registering two image sets with a demons diffusion-based registration algorithm. The use of this method was successfully performed in silico using magnetic resonance and X-ray-computed tomography image data with known boundary conditions. These preliminary results produced boundary conditions with an accuracy of up to 80% compared to the known conditions. Demons-based boundary conditions were utilized within a 3-D MIE reconstruction to determine an elasticity contrast ratio between tumor and normal tissue. Two phantom experiments were then conducted to further test the accuracy of the demons boundary conditions and the MIE reconstruction arising from the use of these conditions. Preliminary results show a reasonable characterization of the material properties on this first attempt and a significant improvement in the automation level and viability of the method.

Kidney deformation and intraprocedural registration: a study of elements of image-guided kidney surgery.

INTRODUCTION: Central to any image-guided surgical procedure is the alignment of image and physical coordinate spaces, or registration. We explored the task of registration in the kidney through in vivo and ex vivo porcine animal models and a human study of minimally invasive kidney surgery. METHODS: A set of (n = 6) ex vivo porcine kidney models was utilized to study the effect of perfusion and loss of turgor caused by incision. Computed tomography (CT) and laser range scanner localizations of the porcine kidneys were performed before and after renal vessel clamping and after capsular incision. The da Vinci robotic surgery system was used for kidney surface acquisition and registration during robot-assisted laparoscopic partial nephrectomy. The surgeon acquired the physical surface data points with a tracked robotic instrument. These data points were aligned to preoperative CT for surface-based registrations. In addition, two biomechanical elastic computer models (isotropic and anisotropic) were constructed to simulate deformations in one of the kidneys to assess predictive capabilities. RESULTS: The mean displacement at the surface fiducials (glass beads) in six porcine kidneys was 4.4 ± 2.1 mm (range 3.4-6.7 mm), with a maximum displacement range of 6.1 to 11.2 mm. Surface-based registrations using the da Vinci robotic instrument in robot-assisted laparoscopic partial nephrectomy yielded mean and standard deviation closest point distances of 1.4 and 1.1 mm. With respect to computer model predictive capability, the target registration error was on average 6.7 mm without using the model and 3.2 mm with using the model. The maximum target error reduced from 11.4 to 6.2 mm. The anisotropic biomechanical model yielded better performance but was not statistically better. CONCLUSIONS: An initial point-based alignment followed by an iterative closest point registration is a feasible method of registering preoperative image (CT) space to intraoperative physical (robot) space. Although rigid registration provides utility for image-guidance, local deformations in regions of resection may be more significant. Computer models may be useful for prediction of such deformations, but more investigation is needed to establish the necessity of such compensation.

Non-rigid registration of breast surfaces using the laplace and diffusion equations.

A semi-automated, non-rigid breast surface registration method is presented that involves solving the Laplace or diffusion equations over undeformed and deformed breast surfaces. The resulting potential energy fields and isocontours are used to establish surface correspondence. This novel surface-based method, which does not require intensity images, anatomical landmarks, or fiducials, is compared to a gold standard of thin-plate spline (TPS) interpolation. Realistic finite element simulations of breast compression and further testing against a tissue-mimicking phantom demonstrate that this method is capable of registering surfaces experiencing 6 - 36 mm compression to within a mean error of 0.5 - 5.7 mm.