Logan W. Clements

Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation.

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.

Model-updated image-guided liver surgery: preliminary results using surface characterization.

The current protocol for image guidance in open abdominal liver tumor removal surgeries involves a rigid registration between the patients 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.

Liver Planning Software Accurately Predicts Postoperative Liver Volume and Measures Early Regeneration

BACKGROUND Postoperative or remnant liver volume (RLV) after hepatic resection is a critical predictor of perioperative outcomes. This study investigates whether the accuracy of liver surgical planning software for predicting postoperative RLV and assessing early regeneration. STUDY DESIGN Patients eligible for hepatic resection were approached for participation in the study from June 2008 to 2010. All patients underwent cross-sectional imaging (CT or MRI) before and early after resection. Planned remnant liver volume (pRLV) (based on the planned resection on the preoperative scan) and postoperative actual remnant liver volume (aRLV) (determined from early postoperative scan) were measured using Scout Liver software (Pathfinder Therapeutics Inc.). Differences between pRLV and aRLV were analyzed, controlling for timing of postoperative imaging. Measured total liver volume (TLV) was compared with standard equations for calculating volume. RESULTS Sixty-six patients were enrolled in the study from June 2008 to June 2010 at 3 treatment centers. Correlation was found between pRLV and aRLV (r = 0.941; p < 0.001), which improved when timing of postoperative imaging was considered (r = 0.953; p < 0.001). Relative volume deviation from pRLV to aRLV stratified cases according to timing of postoperative imaging showed evidence of measurable regeneration beginning 5 days after surgery, with stabilization at 8 days (p < 0.01). For patients at the upper and lower extremes of liver volumes, TLV was poorly estimated using standard equations (up to 50% in some cases). CONCLUSIONS Preoperative virtual planning of future liver remnant accurately predicts postoperative volume after hepatic resection. Early postoperative liver regeneration is measureable on imaging beginning at 5 days after surgery. Measuring TLV directly from CT scans rather than calculating based on equations accounts for extremes in TLV.

A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data

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.

Image-guided liver surgery: intraoperative projection of computed tomography images utilizing tracked ultrasound

BACKGROUND Ultrasound (US) is the most commonly used form of image guidance during liver surgery. However, the use of navigation systems that incorporate instrument tracking and three-dimensional visualization of preoperative tomography is increasing. This report describes an initial experience using an image-guidance system with navigated US. METHODS An image-guidance system was used in a total of 50 open liver procedures to aid in localization and targeting of liver lesions. An optical tracking system was employed to localize surgical instruments. Customized hardware and calibration of the US transducer were required. The results of three procedures are highlighted in order to illustrate specific navigation techniques that proved useful in the broader patient cohort. RESULTS Over a 7-month span, the navigation system assisted in completing 21 (42%) of the procedures, and tracked US alone provided additional information required to perform resection or ablation in six procedures (12%). Average registration time during the three illustrative procedures was <1 min. Average set-up time was approximately 5 min per procedure. CONCLUSIONS The Explorer™ Liver guidance system represents novel technology that continues to evolve. This initial experience indicates that image guidance is valuable in certain procedures, specifically in cases in which difficult anatomy or tumour location or echogenicity limit the usefulness of traditional guidance methods.

Feasibility study for image-guided kidney surgery: Assessment of required intraoperative surface for accurate physical to image space registrations

A notable complication of applying current image-guided surgery techniques of soft tissue to kidney resections (nephrectomies) is the limited field of view of the intraoperative kidney surface. This limited view constrains the ability to obtain a sufficiently geometrically descriptive surface for accurate surface-based registrations. The authors examined the effects of the limited view by using two orientations of a kidney phantom to model typical laparoscopic and open partial nephrectomy views. Point-based registrations, using either rigidly attached markers or anatomical landmarks as fiducials, served as initial alignments for surface-based registrations. Laser range scanner (LRS) obtained surfaces were registered to the phantoms image surface using a rigid iterative closest point algorithm. Subsets of each orientations LRS surface were used in a robustness test to determine which parts of the surface yield the most accurate registrations. Results suggest that obtaining accurate registrations is a function of the percentage of the total surface and of geometric surface properties, such as curvature. Approximately 28% of the total surface is required regardless of the location of that surface subset. However, that percentage decreases when the surface subset contains information from opposite ends of the surface and/or unique anatomical features, such as the renal artery and vein.

Organ Surface Deformation Measurement and Analysis in Open Hepatic Surgery: Method and Preliminary Results From 12 Clinical Cases

The incidence of soft tissue deformation has been well documented in neurosurgical procedures and is known to compromise the spatial accuracy of image-guided surgery systems. Within the context of image-guided liver surgery (IGLS), no detailed method to study and analyze the observed organ shape change between preoperative imaging and the intraoperative presentation has been developed. Contrary to the studies of deformation in neurosurgical procedures, the majority of deformation in IGLS is imposed prior to resection and due to laparotomy and mobilization. As such, methods of analyzing the organ shape change must be developed to use the intraoperative data [e.g., laser range scan (LRS) surfaces] acquired with the organ in its fully deformed shape. To achieve this end we use a signed closest point distance deformation metric computed after rigid alignment of the intraoperative LRS data with organ surfaces generated from the preoperative tomograms. The rigid alignment between the intraoperative LRS surfaces and preoperative image data was computed with a feature weighted surface registration algorithm. In order to compare the deformation metrics across patients, an interpatient nonrigid registration of the preoperative CT images was performed. Given the interpatient liver registrations, an analysis was performed to determine the potential similarities in the distribution of measured deformation between patients for which similar procedures had been performed. The results of the deformation measurement and analysis indicate the potential for soft tissue deformation to compromise surgical guidance information and suggests a similarity in imposed deformation among similar procedure types.

Atlas-based method for model updating in image-guided liver surgery

Similar to the well documented brain shift experienced during neurosurgical procedures, intra-operative soft tissue deformation in open hepatic resections is the primary source of error in current image-guided liver surgery (IGLS) systems. The use of bio-mechanical models has shown promise in providing the link between the deformed, intra-operative patient anatomy and the pre-operative image data. More specifically, the current protocol for deformation compensation in IGLS involves the determination of displacements via registration of intra-operatively acquired sparse data and subsequent use of the displacements to drive solution of a linear elastic model via the finite element method (FEM). However, direct solution of the model during the surgical procedure has several logistical limitations including computational time and the ability to accurately prescribe boundary conditions and material properties. Recently, approaches utilizing an atlas of pre-operatively computed model solutions based on a priori information concerning the surgical loading conditions have been proposed as a more realistic avenue for intra-operative deformation compensation. Similar to previous work, we propose the use of a simple linear inverse model to match the intra-operatively acquired data to the pre-operatively computed atlas. Additionally, an iterative approach is implemented whereby point correspondence is updated during the matching process, being that the correspondence between intra-operative data and the pre-operatively computed atlas is not explicitly known in liver applications. Preliminary validation experiments of the proposed algorithm were performed using both simulation and phantom data. The proposed method provided comparable results in the phantom experiments with those obtained using the traditional incremental FEM approach.

Adaptive directional region growing segmentation of the hepatic vasculature

Accurate analysis of the hepatic vasculature is of great importance for many medical applications, such as liver surgical planning and diagnosis of tumors and/or vascular diseases. Vessel segmentation is a pivotal step for the morphological and topological analysis of the vascular systems. Physical imaging limitations together with the inherent geometrical complexity of the vessels make the problem challenging. In this paper, we propose a series of methods and techniques that separate and segment the portal vein and the hepatic vein from CT images, and extract the centerlines of both vessel trees. We compare the results obtained with our iterative segmentation-and-reconnection approach with those obtained with a traditional region growing method, and we show that our results are substantially better.

Robust surface registration using salient anatomical features in image-guided liver surgery

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.