A successful surface based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information 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 algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intra-operatively acquired point cloud of the liver surface provided by a laser range scanner. Using the aforementioned method, the registration accuracy in IGLS can be compromised by poor initial pose estimation as well as tissue deformation due to the liver mobilization and packing procedure 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 intra-operative 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. The proposed surface registration method will be compared with the traditional technique using both phantom and clinically acquired data. Additionally, robustness studies will be performed to demonstrate the ability of the proposed method to converge to reasonable solutions even under conditions of large deformation and poor initial alignment.
Laser range scanners produce high resolution surface data of anatomic structures, which facilitates the determination of intraoperative soft tissue deformation and the performance of surface based image-to-physical space registration. Segmentation of the range scans is required for the data to be effectively incorporated into current image-guided procedures. Due to time constraints in the operating room, manual segmentation methods are not feasible. We propose a novel segmentation algorithm based on the level set method that uses information from the texture map and curvature of the acquired point cloud to provide an accurate edge map for computation of the speed image. Specifically, the edge image is created by combining the curvature values, computed from a surface fitted to the acquired point cloud using radial basis functions, and gradients of the RGB intensities in the texture map. Preliminary results, obtained from comparing the semiautomatic segmentations of intraoperatively acquire liver LRS data with manual gold standard segmentations, shows the method to be a significant first step towards the implementation of semiautomatic LRS segmentation routine during image-guided surgery.
To compensate for soft-tissue deformation during image-guided
surgical procedures, non-rigid methods are often used as
compensation. However, most of these algorithms first implement a
rigid registration to provide an initial alignment. In liver tumor
resections, the organ is deformed on a large scale, causing visual
shape change on the organ. Unlike neurosurgery, there is no rigid
reference available, so the initial rigid alignment is based on
the organ surface. Any deformation present might lead to
misalignment of non-deformed areas. This study attempts to
develop a technique for the identification of organ deformation
and its separation from the problem of rigid alignment. The basic
premise is to identify areas of the surface that are minimally
deformed and use only these regions for a rigid registration. To
that end, two methods were developed. First, the observation is
made that deformations of this scale cause noticeable changes in
measurements based on differential geometry, such as surface
normals and curvature. Since these values are sensitive to noise,
smooth surfaces were tesselated from point cloud representations.
The second approach was to develop a cost function which rewarded
large regions with low closest point distances. Experiments were
performed using analytic and phantom data, acquiring surface data
both before and after deformation. Multiple registration trials
were performed by randomly perturbing the post-deformed surface
from a ground truth position. After registration, subsurface
target positions were compared with those of the ground truth.
While the curvature-based algorithm was successful with analytic
data, it could not identify enough significant changes in the
surface to be useful for phantom data. The minimal distance
algorithm proved much more effective in separating the
registration, providing significantly improved error measurements
for subsurface targets throughout the whole surface.
Laser range scanners provide rapid and accurate non-contact methods for acquiring 3D surface data, offering many advntages over other techniques currently available during surgery. The range scanner was incorporated into our image-guided surgery system to augment registration and deformation compensation. A rigid body, embedded with IR diodes, was attached to the scanner for tracking in physical space with an optical localization system. The relationship between the scanner's coordinate system and the tracked rigid body was determined using a calibration phantom. Tracking of the scanner using the calibration phantom resulted in an error of 1.4±0.8 mm. Once tracked, data acquired intraoperatively from the range scanner data is registered with preoperative tomographic volumes using the Iterative Closest Point algorithm. Sensitivity studies were performed to ensure that this algorithm effectively reached a global minimum. In cases where tissue deformation is significant, rigid registrations can lead to inaccuracy during surgical navigation. Methods of non-rigid compensation may be necessary, and an initial study using a linearly elastic finite element model is presented. Differences between intraoperative and preoperative surfaces after rigid registration are used to formulate boundary conditions, and the resulting displacement field deforms the preoperative image volume. To test this protocol, a phantom was built, consisting of fiducial points and a silicone liver model. Range scan and CT data were captured both before and after deforming the organ. The pre-deformed images, after registration and modeling, were compared to post-deformation, although there is a noticeable improvement by implementing the finite element model. To improve accuracy, more elaborate surface registration and deformation compensation strategies will be investigated. To improve accuracy, more elaborate surface registration and deformation compensation strategies will be investigated. The ragne scanner is an innovative, uncumbersome, and relatively inexpensive method of collecting intraoperative data. It has been integrated into our image-guided surgical system and software with virtually no overhead.
The development of image-guided surgical systems (IGS) has had a significant impact on clinical neurosurgery and the desire to extend these principles to other surgical endeavors is the next step in IGS evolution. An impediment to its widespread adoption is the realization that the organ of interest often deforms due to common surgical loading conditions. As a result, alignment degradation between patient and the MR/CT image volume can occur which can compromise guidance fidelity. Recently, computational approaches to correct alignment have been proposed within neurosurgery. In this work, these approaches are extended for use within image-guided liver surgery and demonstrate this framework's adaptability. Results from the registration of the preoperative segmented liver surface and the intraoperative liver, as acquired by a laser range scanner, demonstrate accurate visual alignment in regions that deform minimally while in other regions misalignment due to deformations on the order of 1 cm are apparent. A model-updating strategy is employed which uses the closest point operator to compensate for deformations within the patient-specific image volume. The framework presented is an approach whereby laser range scanning coupled to a computational model of soft tissue deformation provide the necessary information to extend IGS principles to intra-abdominal explorative surgery applications.
In this work, preliminary quantitative results are presented that characterize the cortical surface during neurosurgery using a laser range scanner. Intra-operative cortical surface data is collected from patients undergoing cortical resection procedures and is registered to patient-specific pre-operative data. After the skull bone-flap has been removed and the dura retracted, a laser range scanner (LRS) is used to capture range data of the brain's surface. An RGB bitmap is also captured at the time of scanning, which permits texturing of the range data. The textured range data is then registered to textured surfaces of the brain generated from pre-operative images. Registration is provided by a rigid-body transform that is based on iterative-closest point transforms and mutual information. Preliminary results using the LRS during surgery demonstrate a good visual alignment between intra-operative and pre-operative data. The registration algorithm is able to register surfaces using both sulcal and vessel patterns. Target registration errors on the order of 2mm have been achieved using the registration algorithm in a clinical setting. Results from the analysis of laser range scan data suggest that the unique feature-rich cortical surface may provide a robust method for intra-operative registration and deformation measurement. Using laser range scan data as a non-contact method of acquiring spatially relevant data in a clinical setting is a novel application of this technology. Furthermore, the work presented demonstrates a viable framework for current IGS systems to computationally account for brain shift.
Many image guided surgical (IGS) procedures use surface representations of anatomical features to provide a registration between physical space and image space. While anatomical fiducial registration techniques can often be compromised by soft tissue deformation, laser-scanning systems offer a non-contact method for accurately resolving geometric surfaces. This digitization approach is particularly well suited for liver resection surgery, where registration is accomplished by aligning the segmented preoperative liver image volume with the intraoperative presentation of the organ. Further, this three-dimensional technology affords a method of rapid surface data acquisition that can be used for the measurement and compensation of tissue deformation. A phantom was constructed to assess the laser scanner's capability of acquiring accurate shape information and robust surface registration. The phantom contained a wealth of point and surface geometry that could be identified in both a CT image volume and a range scan image. Point based registrations were calculated and served as a reference transformation for comparison of the results from surface registration using the iterative closest point (ICP) algorithm. The laser range scanner used the optical principle of triangulation to obtain a dense point set of surface data with a grid spacing of 0.6 mm. The scanner was able to localize and track fiducials on the order of 0.1mm. Phantom experiments demonstrated the ability to perform point-based registrations to CT with a Fiducial Registration Error of 0.5 mm. The scanner accurately resolves geometric surface information and achieved surface-based registrations within a mean distance residual of 0.4 mm. The principal sources of error were systemic misrepresentations of the position by the range scanner, which can be accounted for through calibration or other procedures. The laser scanner system was successfully used to register images to sub-millimetric accuracy. Future work involves acquisition of liver surfaces during surgical procedures for use in registration and deformation measurements.
Integration of tomographic angiograms into neurosurgical navigation should decrease the probability of vascular injury and allow localization of vascular lesions. Information from angiograms is often presented using maximum intensity projections (MIPs), which provide a more intuitive presentation of 3D vascular structures. Conventional MIPs involve the whole image volume during ray casting. Our goal was to construct surgically appropriate MIPs that excluded information contralateral to the operation site and to quantify the accuracy of vessel depiction using this new method. For each angiogram slice, the center of mass (COM) was calculated. Together, the COM coordinates formed a boundary plane that clipped the contralateral information from ray casting. A separate depth buffer was created to preserve 3D information. MIPs were examined quantitatively using a mathematical model of the head containing vascular structures of known diameter. The vessel widths of the resulting MIPs were then measured and compared. To examine the spatial accuracy of MIP images, a vascular phantom was created, which had rigid vessels of known diameter and extrinsic fiducial markers to perform a physical to image space registration. Studies with the mathematical model showed that the vessels appeared smaller in MIPs than their actual diameters. This decrease is attributed to the statistical properties of the ray casting process that are affected by the pathlength. Studies with the vascular phantom show correct localization of the probe in tomographic and projective image space. From these studies, we concluded that additional methods for providing information concerning vessel proximity during surgical guidance should be investigated. Surgically appropriate MIPs provide comparable images to conventional MIPs; however, they allow more focus on the vascular structures in proximity to the target site.