In open abdominal image-guided liver surgery, sparse measurements of the organ surface can be taken intraoperatively via a laser-range scanning device or a tracked stylus with relatively little impact on surgical workflow. We propose a novel nonrigid registration method which uses sparse surface data to reconstruct a mapping between the preoperative CT volume and the intraoperative patient space. The mapping is generated using a tissue mechanics model subject to boundary conditions consistent with surgical supportive packing during liver resection therapy. Our approach iteratively chooses parameters which define these boundary conditions such that the deformed tissue model best fits the intraoperative surface data. Using two liver phantoms, we gathered a total of five deformation datasets with conditions comparable to open surgery. The proposed nonrigid method achieved a mean target registration error (TRE) of 3.3 mm for targets dispersed throughout the phantom volume, using a limited region of surface data to drive the nonrigid registration algorithm, while rigid registration resulted in a mean TRE of 9.5 mm. In addition, we studied the effect of surface data extent, the inclusion of subsurface data, the trade-offs of using a nonlinear tissue model, robustness to rigid misalignments, and the feasibility in five clinical datasets.

The accurate identification of tumor margins during neurosurgery is a primary concern for the surgeon in order to maximize resection of malignant tissue while preserving normal function. The use of preoperative imaging for guidance is standard of care, but tumor margins are not always clear even when contrast agents are used, and so margins are often determined intraoperatively by visual and tactile feedback. Ultrasound strain imaging creates a quantitative representation of tissue stiffness which can be used in real-time. The information offered by strain imaging can be placed within a conventional image-guidance workflow by tracking the ultrasound probe and calibrating the image plane, which facilitates interpretation of the data by placing it within a common coordinate space with preoperative imaging. Tumor geometry in strain imaging is then directly comparable to the geometry in preoperative imaging. This paper presents a tracked ultrasound strain imaging system capable of co-registering with preoperative tomograms and also of reconstructing a 3D surface using the border of the strain lesion. In a preliminary study using four phantoms with subsurface tumors, tracked strain imaging was registered to preoperative image volumes and then tumor surfaces were reconstructed using contours extracted from strain image slices. The volumes of the phantom tumors reconstructed from tracked strain imaging were approximately between 1.5 to 2.4 cm3, which was similar to the CT volumes of 1.0 to 2.3 cm3. Future work will be done to robustly characterize the reconstruction accuracy of the system.

In the context of open abdominal image-guided liver surgery, the efficacy of an image-guidance system relies on its ability to (1) accurately depict tool locations with respect to the anatomy, and (2) maintain the workflow of the surgical team. Laser-range scanned (LRS) partial surface measurements can be taken intraoperatively with relatively little impact on the surgical workflow, as opposed to other intraoperative imaging modalities. Previous research has demonstrated that this kind of partial surface data may be (1) used to drive a rigid registration of the preoperative CT image volume to intraoperative patient space, and (2) extrapolated and combined with a tissue-mechanics-based organ model to drive a non-rigid registration, thus compensating for organ deformations. In this paper we present a novel approach for intraoperative nonrigid liver registration which iteratively reconstructs a displacement field on the posterior side of the organ in order to minimize the error between the deformed model and the intraopreative surface data. Experimental results with a phantom liver undergoing large deformations demonstrate that this method achieves target registration errors (TRE) with a mean of 4.0 mm in the prediction of a set of 58 locations inside the phantom, which represents a 50% improvement over rigid registration alone, and a 44% improvement over the prior non-iterative single-solve method of extrapolating boundary conditions via a surface Laplacian.

BACKGROUND:Commercial image-guided surgery systems rely on the fundamental assumption that preoperative medical images represent the physical state of the patient in the operating room. The guidance display typically consists of a three-dimensional (3D) model derived from medical images and three orthogonal views of the imaging data. A challenging question in image-guided surgery is: what happens when the images used in the guidance display no longer correspond to the current geometric state of the anatomy and guidance information is still desirable?METHODS:We modify the conventional display with two techniques for incorporating a displacement field from a finite-element model into the guidance display and present a preliminary study of the effect of our method on performance with a simple surgical task. The topic of this paper is methods for conveying the computational model solution, not the model itself. To address the integration of the computational model solution into the display, a novel method of applying the deformation to the tool tip was developed, which quickly corrects for deformation but also maintains the pristine nature of the preoperative images. We compare the proposed technique to an existing method that applies the deformation field to the image volume.RESULTS:A pilot study compared mean performance with our method of applying the deformation to the tool tip and the conventional technique. These methods were statistically similar with respect to accuracy of localization (p < 0.05) and amount of time (p < 0.05) required for localization of the target.CONCLUSIONS:These results suggest that our new technique can be used in place of the computationally expensive task of deforming the image volume, without affecting the time or accuracy of the surgical task. Most notably, our work addresses the problem of incorporating deformation correction into the guidance display and offers a first step toward understanding its effect on surgical performance.

The objective of our work is a system that enables both mechanically and electronically shapable thermal energy deposition in soft tissue ablation. The overall goal is a system that can percutaneously (and through a single organ surface puncture) treat tumors that are large, multiple, geometrically complex, or located too close to vital structures for traditional resection. This paper focuses on mechanical steering and image guidance aspects of the project. Mechanical steering is accomplished using an active cannula that enables repositioning of the ablator tip without complete retraction. We describe experiments designed to evaluate targeting accuracy of the active cannula (also known as a concentric tube robot) in soft tissues under tracked 3D ultrasound guidance.

Intraoperative ultrasound imaging is a commonly used modality for image guided surgery and can be used to monitor changes from pre-operative data in real time. Often a mapping of the liver surface is required to achieve image-tophysical alignment for image guided liver surgery. Laser range scans and tracked optical stylus instruments have both been utilized in the past to create an intraoperative representation of the organ surface. This paper proposes a method to digitize the organ surface utilizing tracked ultrasound and to evaluate a relatively simple correction technique. Surfaces are generated from point clouds obtained from the US transducer face itself during tracked movement. In addition, a surface generated from a laser range scan (LRS) was used as the gold standard for evaluating the accuracy of the US transducer swab surfaces. Two liver phantoms with varying stiffness were tested. The results reflected that the average deformation observed for a 60 second swab of the liver phantom was 3.7 ± 0.9 mm for the more rigid phantom and 4.6 ± 1.2 mm for the less rigid phantom. With respect to tissue targets below the surface, the average error in position due to ultrasound surface digitization was 3.5 ± 0.5 mm and 5.9 ± 0.9 mm for the stiffer and softer phantoms respectively. With the simple correction scheme, the surface error was reduced to 1.1 ± 0.8 mm and 1.7 ± 1.0 mm, respectively; and the subsurface target error was reduced to 2.0 ± 0.9 mm and 4.5 ± 1.8 mm, respectively. These results are encouraging and suggest that the ultrasound probe itself and the acquired images could serve as a comprehensive digitization approach for image guided liver surgery.

Image-guided surgery may reduce the re-excision rate in breast-conserving tumor-resection surgery, but image guidance is difficult since the breast undergoes significant deformation during the procedure. In addition, any imaging performed preoperatively is usually conducted in a very different presentation to that in surgery. Biomechanical models combined with low-cost ultrasound imaging and laser range scanning may provide an inexpensive way to provide intraoperative guidance information while also compensating for soft tissue deformations that occur during breast-conserving surgery. One major cause of deformation occurs after an incision into the tissue is made and the skin flap is pulled back with the use of retractors. Since the next step in the surgery would be to start building a surgical plane around the tumor to remove cancerous tissue, in an image-guidance environment, it would be necessary to have a model that corrects for the deformation caused by the surgeon to properly guide the application of resection tools. In this preliminary study, two anthropomorphic breast phantoms were made, and retractions were performed on both with improvised retractors. One phantom underwent a deeper retraction that the other. A laser range scanner (LRS) was used to monitor phantom tissue change before and after retraction. The surface data acquired with the LRS and retractors were then used to drive the solution of a finite element model. The results indicate an encouraging level of agreement between model predictions and data. The surface target error for the phantom with the deep retraction was 2.2 +/- 1.2 mm (n=47 targets) with the average deformation of the surface targets at 4.2 +/- 1.6mm. For the phantom with the shallow retraction, the surface target error was 2.1 +/- 1.0 mm (n=70 targets) with the average deformation of the surface targets at 4.0 +/- 2.0 mm.