A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point (ICP) algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.

Image-guided surgery provides navigational assistance to the surgeon by displaying the surgical probe position on a set of preoperative tomograms in real time. In this study, the feasibility of implementing image-guided surgery concepts into liver surgery was examined during eight hepatic resection procedures. Preoperative tomographic image data were acquired and processed. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptAccompanying intraoperative data on liver shape and position were obtained through optically tracked probes and laser range scanning technology. The preoperative and intraoperative representations of the liver surface were aligned using the iterative closest point surface matching algorithm. Surface registrations resulted in mean residual errors from 2 to 6 mm, with errors of target surface regions being below a stated goal of 1 cm. Issues affecting registration accuracy include liver motion due to respiration, the quality of the intraoperative surface data, and intraoperative organ deformation. Respiratory motion was quantified during the procedures as cyclical, primarily along the cranial-caudal direction. The resulting registrations were more robust and accurate when using laser range scanning to rapidly acquire thousands of points on the liver surface and when capturing unique geometric regions on the liver surface, such as the inferior edge. Finally, finite element models recovered much of the observed intraoperative deformation, further decreasing errors in the registration. Image-guided liver surgery has shown the potential to provide surgeons with important navigation aids that could increase the accuracy of targeting lesions and the number of patients eligible for surgical resection. KeywordsImage-guided surgery; Liver resection; Surface registration; Laser range scanning; Finite elementOf the 147,000 projected new cases of colorectal cancer for 2004, 1 it is estimated that 50% of all colorectal primary tumors will develop a liver metastasis at some point in the disease, and 20% of cases will develop a metastasis solely in the liver. 2 Metastatic liver cancer takes a rapid course. When untreated, the median survival rate is between 5 and 12 months with a 5-year survival rate approaching zero. [3][4][5][6] The most common form of treatment is surgical resection. For metastases, studies have reported a 5-year survival rates of 20-50%, with much of the variance attributed to bias in patient selection. For primary liver tumors, the 5-year survival rates varied from 24 to 76% due to variables such as age, size of tumor, and presence of cirrhosis. 2,7-10 With 70-90% of all patients ineligible for resection, ablative techniques provide a promising alternative. [11][12][13][14][15] In the cases of resection and ablation, if the surgeon can direct therapy to the target with an ever higher degree of accuracy, it could lead to smaller resection margins, improved outcomes, and more patients eligible for treatment. To that end, image-guided surgical techniques could...

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 current protocol for image guidance in open abdominal liver tumor removal surgeries involves a rigid registration between the patient's operating room space and the pre-operative diagnostic image-space. Systematic studies have shown that the liver can deform up to 2 cm during surgeries in a non-rigid fashion thereby compromising the accuracy of these surgical navigation systems. Compensating for intra-operative deformations using mathematical models has shown promising results. In this work, we follow up the initial rigid registration with a computational approach that is geared towards minimizing the residual closest point distances between the un-deformed pre-operative surface and the rigidly registered intra-operative surface. We also use a surface Laplacian equation based filter that generates a realistic deformation field. Preliminary validation of the proposed computational framework was performed using phantom experiments and clinical trials. The proposed framework improved the rigid registration errors for the phantom experiments on average by 43%, and 74% using partial and full surface data, respectively. With respect to clinical data, it improved the closest point residual error associated with rigid registration by 54% on average for the clinical cases. These results are highly encouraging and suggest that computational models can be used to increase the accuracy of image-guided open abdominal liver tumor removal surgeries.

Indications for liver surgery to treat primary and secondary hepatic malignancies are broadening. Utilizing data from B-mode or 2-dimensional intraoperative ultrasound, it is often challenging to replicate the findings from preoperative CT or MRI scans. Additional data from more recently developed image-guidance technology, which registers preoperative axial imaging to a 3-dimensional real-time model, may be used to improve operative planning, locate difficult to find hepatic tumors, and guide ablations. Laparoscopic liver procedures are often more challenging than their open counterparts. Image-guidance technology can assist in overcoming some of the obstacles to minimally invasive liver procedures by enhancing ultrasound findings and ablation guidance. This manuscript describes a protocol that evaluated an open image-guidance system, and a subsequent protocol that directly compared, for validation, a laparoscopic with an open image-guidance system. Both protocols were limited to ablations within the liver. The laparoscopic image-guidance system successfully creates a 3-D model at both 7 and 14 mm Hg that is similar to the open 3-D model. Ultimately, improving intraoperative image guidance can help expand the ability to perform both laparoscopic and open liver surgeries.