Prashanth Dumpuri

An atlas-based method to compensate for brain shift: preliminary results.

Compensating for intraoperative brain shift using computational models has shown promising results. Since computational time is an important factor during neurosurgery, a priori knowledge of the possible sources of deformation can increase the accuracy of model-updated image-guided systems. In this paper, a strategy to compensate for distributed loading conditions in the brain such as brain sag, volume changes due to drug reactions, and brain swelling due to edema is presented. An atlas of model deformations based on these complex loading conditions is computed preoperatively and used with a constrained linear inverse model to predict the intraoperative distributed brain shift. This relatively simple inverse finite-element approach is investigated within the context of a series of phantom experiments, two in vivo cases, and a simulation study. Preliminary results indicate that the approach recaptured on average 93% of surface shift for the simulation, phantom, and in vivo experiments. With respect to subsurface shift, comparisons were only made with simulation and phantom experiments and demonstrated an ability to recapture 85% of the shift. This translates to a remaining surface and subsurface shift error of 0.7+/-0.3 mm, and 1.0+/-0.4 mm, respectively, for deformations on the order of 1cm.

Laser range scanning for image-guided neurosurgery: investigation of image-to-physical space registrations.

In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial (skin marker point-based registration (PBR), and face-based surface registration) and intracranial methods (feature PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness). The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patients intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance (MR) image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6 +/- 0.5 mm, 1.7 +/- 0.5 mm, 3.9 +/- 3.4 mm, and 2.0 +/- 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial registration methods from the earliest stages. Based on the results reported here, organ-based approaches to registration would improve this, especially for shallow lesions.

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.

Intraoperative Brain Shift Compensation: Accounting for Dural Septa

Biomechanical models that describe soft tissue deformation provide a relatively inexpensive way to correct registration errors in image-guided neurosurgical systems caused by nonrigid brain shift. Quantifying the factors that cause this deformation to sufficient precision is a challenging task. To circumvent this difficulty, atlas-based methods have been developed recently that allow for uncertainty, yet still capture the first-order effects associated with deformation. The inverse solution is driven by sparse intraoperative surface measurements, which could bias the reconstruction and affect the subsurface accuracy of the model prediction. Studies using intraoperative MR have shown that the deformation in the midline, tentorium, and contralateral hemisphere is relatively small. The dural septa act as rigid membranes supporting the brain parenchyma and compartmentalizing the brain. Accounting for these structures in models may be an important key to improving subsurface shift accuracy. A novel method to segment the tentorium cerebelli will be described, along with the procedure for modeling the dural septa. Results in seven clinical cases show a qualitative improvement in subsurface shift accuracy making the predicted deformation more congruous with previous observations in the literature. The results also suggest a considerably more important role for hyperosmotic drug modeling for the intraoperative shift correction environment.

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 Fast and Efficient Method to Compensate for Brain Shift for Tumor Resection Therapies Measured Between Preoperative and Postoperative Tomograms

In this paper, an efficient paradigm is presented to correct for brain shift during tumor resection therapies. For this study, high resolution preoperative (pre-op) and postoperative (post-op) MR images were acquired for eight in vivo patients, and surface/subsurface shift was identified by manual identification of homologous points between the pre-op and immediate post-op tomograms. Cortical surface deformation data were then used to drive an inverse problem framework. The manually identified subsurface deformations served as a comparison toward validation. The proposed framework recaptured 85% of the mean subsurface shift. This translated to a subsurface shift error of 0.4 ± 0.4 mm for a measured shift of 3.1 ± 0.6 mm. The patients pre-op tomograms were also deformed volumetrically using displacements predicted by the model. Results presented allow a preliminary evaluation of correction both quantitatively and visually. While intraoperative (intra-op) MR imaging data would be optimal, the extent of shift measured from pre- to post-op MR was comparable to clinical conditions. This study demonstrates the accuracy of the proposed framework in predicting full-volume displacements from sparse shift measurements. It also shows that the proposed framework can be extended and used to update pre-op images on a time scale that is compatible with surgery.

Semiautomatic Registration of Pre- and Postbrain Tumor Resection Laser Range Data: Method and Validation

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the post-resection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vessels are identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy.

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.

Estimation of intra-operative brain shift using a tracked laser range scanner

Intra-operative brain shift limits the usefulness of image-guided neurosurgery systems (IGNS), which are based on pre-operative images. Methods that are being developed to address this problem need intra-operative measurements as input. In this work, we present an intra-operative surface shift measurement technique that relies on a tracked 3D laser range scanner. This scanner acquires both 3D range data and 2D images, which are co-registered. We compare two methods to derive displacements at every point in the field of view. The first one relies on the registration of the 2D images; the second relies on the direct 3D registration of the 3D range data. Our results, based on five data sets, show that the 2D method is preferable.

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.