Concepts and preliminary data toward the realization of image-guided liver surgery

Abstract

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. Accompanying 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.

Figure 1

Comparison of manual and level-set segmentations of the liver.

Figure 2

Surface model generation from the segmented contours. The initial surface mesh (left) is generated using the marching cubes method. It is refined (right) with a surface fitting technique that employs radial basis functions, providing a smoother surface with less vertices, potentially increasing the speed and accuracy of the registration.

Figure 3

Surface data acquisition in the operating room. In the left image, the surgeon is digitizing points on the liver surface with the optically tracked probe. The right image shows the range scanner in position to acquire surface data of the liver intraoperatively.

Figure 4

Data acquisition with the range scanner. The video snapshot on the top left and the three-dimensional data on the top right are combined to form a texture mapped point cloud, which is shown in the bottom image.

Figure 5

Screen shot of the ORION surgical navigation software. ORION is displaying, from the top-left panel clock-wise, the native tomogram, two different perspectives of the three-dimensional liver and the vasculature as segmented by MeVis, and a tomographic slice of the segmented liver.

Figure 6

Time plot of respiratory data. The data are aligned according to the axes provided by the primary component analysis. The origin is the mean of the original respiration data.

Figure 7

Iterative closest point registration results. For each case, the registered range scan data is overlaid on top of the three tomographic slices from the volume.

Figure 8

Iterative closest point registration results. For each case, the registered probe data are overlaid on top of the three tomographic slices from the volume.

Figure 9

Comparison of surface registrations using tracked probe (left column) and range scan (right column). Both data-sets are overlaid on the identical slice from the image volume.

Figure 10

Left column—Original rigid registration of range scan data overlaid on tomograms. Right column—The deformed liver volume from the finite element model is overlaid in red. In the areas where the point cloud was used for the boundary conditions, there is improved agreement between the range scan surface and the deformed image surface.

Figure 11

In case 7, the relatively planar range scan data result in misalignments during the surface-based registration. The qualitatively identified landmark, where the falciform ligament resided before surgery, is rotated clockwise as indicated by the white arrows.