We compare a surface-driven, model-based deformation correction method to a clinically relevant rigid registration approach within the application of image-guided microwave ablation for the purpose of demonstrating improved localization and antenna placement in a deformable hepatic phantom. Furthermore, we present preliminary computational modeling of microwave ablation integrated within the navigational environment to lay the groundwork for a more comprehensive procedural planning and guidance framework. To achieve this, we employ a simple, retrospective model of microwave ablation after registration, which allows a preliminary evaluation of the combined therapeutic and navigational framework. When driving registrations with full organ surface data (i.e., as could be available in a percutaneous procedure suite), the deformation correction method improved average ablation antenna registration error by 58.9% compared to rigid registration (i.e., 2.5 ± 1.1 mm, 5.6 ± 2.3 mm of average target error for corrected and rigid registration, respectively) and on average improved volumetric overlap between the modeled and ground-truth ablation zones from 67.0 ± 11.8 % to 85.6 ± 5.0 % for rigid and corrected, respectively. Furthermore, when using sparse-surface data (i.e., as is available in an open surgical procedure), the deformation correction improved registration error by 38.3% and volumetric overlap from 64.8 ± 12.4 % to 77.1 ± 8.0 % for rigid and corrected, respectively. We demonstrate, in an initial phantom experiment, enhanced navigation in image-guided hepatic ablation procedures and identify a clear multiphysics pathway toward a more comprehensive thermal dose planning and deformation-corrected guidance framework.
Over the last 25 years, the number of papers written that involve image guidance, liver, and registration has, on average, doubled every 6-8 years. While image guidance has a long history within the neurosurgical theatre, it’s translation to other soft-tissue organs such as the liver has been slower given the inherent difficulty in image-to-physical registration. More specifically, deformations have been recognized to compromise image guidance fidelity in virtually all soft-tissue image guidance applications. As a result, an active area of investigation is the development of sparse-data-driven nonrigid image-to-physical liver registration techniques to compensate for deformation and provide accurate localization for image guided liver surgery. In this work we have leveraged our extensive human-to-phantom registration testing framework based on the work in [1] and Amazon Web Services to create a sparse data challenge for the image guided liver surgery community (https://sparsedatachallenge.org/). Our sparse data challenge will allow research groups from across the world to extensively test their approaches on common data and have quantitative accuracy measurements provided for assessment of fidelity. Welcome to the Sparse Data Challenge for image-to-physical liver registration assessment.
Although resection and transplantation are primary curative methods of treatment for hepatocellular carcinoma, many patients are not candidates. In these cases, other treatment methods such as selective internal radiation therapy, chemotherapy, or external beam radiation are used. While these treatments are effective, patient-specific customization of treatment could be beneficial. Recent advances in personalized medicine are making this possible, but often there are multiple phenotypes within a proliferating tumor. While not standard, one could envision a serial longitudinal biopsy approach with more phenotypically-targeted therapeutics if one could detect responding and non-responding regions of tumor over time. This work proposes a method to determine active regions of the tumor that differentially respond to treatment to better guide biopsy for longitudinal personalization of treatment. While PET may serve this purpose, it is not easily used for real-time image guidance, is not effective for many types of tumors, and can be confounded by inflammatory responses. In this work, ten total patients with imaging sequences from before and after treatment were retrospectively obtained. Five of these were selected for analysis based on the total liver volume change. A two-phase alignment process comprised of an intensity-based rigid registration followed by a nonrigid refining process driven by bulk deformation of the organ surface was performed. To assess the accuracy of the registration, two metrics were used for preliminary results. The mean closest point surface distance was used to quantify how well the surfaces of the registered livers match and was found to be 2.65±3.54mm. Anatomical features visible in pre- and post-treatment images were also identified. After registration, the mean Euclidean distance between features was found to be 5.22±4.06mm. To assess potential areas of tumor change, the registered tumor pre- and post-treatment were overlaid.
Brain shift during neurosurgery can compromise the fidelity of image guidance and potentially lead to surgical error. We have developed a finite element model-based brain shift compensation strategy to correct preoperative images for improved intraoperative navigation. This workflow-friendly approach precomputes potential intraoperative deformations (a ‘deformation atlas’) via a biphasic-biomechanical-model accounting for brain deformation associated with cerebrospinal fluid drainage, osmotic agents, resection, and swelling. Intraoperatively, an inverse problem approach is employed to provide a combinatory fit from the atlas that best matches sparse intraoperative measurements. Subsequently, preoperative image is deformed accordingly to better reflect patient’s intraoperative anatomy. While we have performed several retrospective studies examining model’s accuracy using post- or intra-operative magnetic resonance imaging, one challenging task is to examine model’s ability to recapture shift due to the aforementioned effects independently with clinical data and in a longitudinal manner under varying conditions. The work here is a case study where swelling was observed at the initial stage of surgery (after craniotomy and dura opening), subsequently sag was observed in a later stage of resection. Intraoperative tissue swelling and sag were captured via an optically tracked stylus by identifying cortical surface vessel features (n = 9), and model-based correction was performed for these two distinct types of brain shift at different stages of the procedure. Within the course of the entire surgery, we estimate the cortical surface experienced a deformation trajectory absolute path length of approximately 19.4 ± 2.1 mm reflecting swelling followed by sag. Overall, model reduced swelling-induced shift from 7.3 ± 1.1 to 1.8 ± 0.5 mm (~74.6% correction); for subsequent sag movement, model reduced shift from 6.4 ± 1.5 to 1.4 ± 0.5 mm (~76.6% correction).
When negative tumor margins are achieved at the time of resection, breast conserving therapy (lumpectomy followed with radiation therapy) offers patients improved cosmetic outcomes and quality of life with equivalent survival outcomes to mastectomy. However, high reoperation rates ranging 10-59% continue to challenge adoption and suggest that improved intraoperative tumor localization is a pressing need. We propose to couple an optical tracker and stereo camera system for automated monitoring of surgical instruments and non-rigid breast surface deformations. A bracket was designed to rigidly pair an optical tracker with a stereo camera, optimizing overlap volume. Utilizing both devices allowed for precise instrument tracking of multiple objects with reliable, workflow friendly tracking of dynamic breast movements. Computer vision techniques were employed to automatically track fiducials, requiring one-time initialization with bounding boxes in stereo camera images. Point based rigid registration was performed between fiducial locations triangulated from stereo camera images and fiducial locations recorded with an optically tracked stylus. We measured fiducial registration error (FRE) and target registration error (TRE) with two different stereo camera devices using a phantom breast with five fiducials. Average FREs of 2.7 ± 0.4 mm and 2.4 ± 0.6 mm with each stereo-camera device demonstrate considerable promise for this approach in monitoring the surgical field. Automated tracking was shown to reduce error when compared to manually selected fiducial locations in stereo camera image-based localization. The proposed instrumentation framework demonstrated potential for the continuous measurement of surgical instruments in relation to the dynamic deformations of a breast during lumpectomy.
Conventional optical tracking systems use cameras sensitive to near-infra-red (NIR) light detecting cameras and passively/actively NIR-illuminated markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely-used within the neurosurgical theatre and is a staple in the standard of care in craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure with the patient in a fixed OR head presentation orientation. In the work presented herein, we propose a framework to achieve this in the OR that is free of conventional tracking technology, i.e. a trackerless approach. Briefly, we are investigating a collaborative extension of 3D slicer that combines surgical planning and craniotomy designation in a novel manner. While taking advantage of the well-developed 3D slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with a novel intraoperative MR-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without use of a conventional optical tracking technology. To test this novel approach, an experienced neurosurgeon performed experiments on four different mock surgical cases using our module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this early-stage craniotomy planning and delivery approach, and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental new realization of an integrated trackerless surgical guidance platform.
Soft tissue deformation can be a major source of error for image-guided interventions. Deformations associated with laparoscopic liver surgery can be substantially different from those concomitant with open approaches due to intraoperative practices such as abdominal insufflation and variable degrees of mobilization from the supporting ligaments of the liver. This technical note outlines recent contributions towards nonrigid registration for laparoscopic liver surgery published in the Journal of Medical Imaging special issue on image-guided procedures, robotic interventions, and modeling [10]. In particular, we review (1) a characterization of intraoperative liver deformation from clinically-acquired sparse digitizations of the organ surface through a series of laparoscopic-to-open conversions, and (2) a novel deformation correction strategy that leverages a set of control points placed across anatomical regions of mechanical support provided to the organ. Perturbations of these control points on a finite element model were used to iteratively reconstruct the intraoperative deformed organ shape from sparse measurements of the liver surface. These characterization and correction methods for laparoscopic deformation were applied to a retrospective clinical series of 25 laparoscopic-to-open conversions performed under image guidance and a phantom validation framework.
Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflation and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver. We first measure the degree of laparoscopic deformation at two insufflation pressures over the course of laparoscopic-to-open conversion in 25 patients. With this clinical data alongside a mock laparoscopic phantom setup, we report a biomechanical correction approach that leverages anatomically load-bearing support surfaces from ligament attachments to iteratively reconstruct and account for intraoperative deformations. Laparoscopic deformations were significantly larger than deformations associated with open surgery, and our correction approach yielded subsurface target error of 6.7±1.3 mm and surface error of 0.8±0.4 mm using only sparse surface data with realistic surgical extent. Laparoscopic surface data extents were examined and found to impact registration accuracy. Finally, we demonstrate viability of the correction method with clinical data.
Brain shift during tumor resection compromises the spatial validity of registered preoperative imaging data that is critical to image-guided procedures. One current clinical solution to mitigate the effects is to reimage using intraoperative magnetic resonance (iMR) imaging. Although iMR has demonstrated benefits in accounting for preoperative-to-intraoperative tissue changes, its cost and encumbrance have limited its widespread adoption. While iMR will likely continue to be employed for challenging cases, a cost-effective model-based brain shift compensation strategy is desirable as a complementary technology for standard resections. We performed a retrospective study of n=9 tumor resection cases, comparing iMR measurements with intraoperative brain shift compensation predicted by our model-based strategy, driven by sparse intraoperative cortical surface data. For quantitative assessment, homologous subsurface targets near the tumors were selected on preoperative MR and iMR images. Once rigidly registered, intraoperative shift measurements were determined and subsequently compared to model-predicted counterparts as estimated by the brain shift correction framework. When considering moderate and high shift (>3 mm, n=13±6 measurements per case), the alignment error due to brain shift reduced from 5.7±2.6 to 2.3±1.1 mm, representing ∼59% correction. These first steps toward validation are promising for model-based strategies.
Intraoperative soft tissue deformation, referred to as brain shift, compromises the application of current image-guided surgery navigation systems in neurosurgery. A computational model driven by sparse data has been proposed as a cost-effective method to compensate for cortical surface and volumetric displacements. We present a mock environment developed to acquire stereoimages from a tracked operating microscope and to reconstruct three-dimensional point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. When comparing our tracked microscope stereo-pair measure of mock vessel displacements to that of the measurement determined by the independent optically tracked stylus marking, the displacement error was ∼2 mm on average. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to laser range scanners to collect sufficient intraoperative information for brain shift correction.
Intra-operative soft tissue deformation, referred to as brain shift, compromises the application of current imageguided surgery (IGS) navigation systems in neurosurgery. A computational model driven by sparse data has been used as a cost effective method to compensate for cortical surface and volumetric displacements. Stereoscopic microscopes and laser range scanners (LRS) are the two most investigated sparse intra-operative imaging modalities for driving these systems. However, integrating these devices in the clinical workflow to facilitate development and evaluation requires developing systems that easily permit data acquisition and processing. In this work we present a mock environment developed to acquire stereo images from a tracked operating microscope and to reconstruct 3D point clouds from these images. A reconstruction error of 1 mm is estimated by using a phantom with a known geometry and independently measured deformation extent. The microscope is tracked via an attached tracking rigid body that facilitates the recording of the position of the microscope via a commercial optical tracking system as it moves during the procedure. Point clouds, reconstructed under different microscope positions, are registered into the same space in order to compute the feature displacements. Using our mock craniotomy device, realistic cortical deformations are generated. Our experimental results report approximately 2mm average displacement error compared with the optical tracking system. These results demonstrate the practicality of using tracked stereoscopic microscope as an alternative to LRS to collect sufficient intraoperative information for brain shift correction.
In order to rigorously validate techniques for image-guided liver surgery (IGLS), an accurate mock representation of the
intraoperative surgical scene with quantifiable localization of subsurface targets would be highly desirable. However,
many attempts to reproduce the laparoscopic environment have encountered limited success due to neglect of several
crucial design aspects. The laparoscopic setting is complicated by factors such as gas insufflation of the abdomen,
changes in patient orientation, incomplete organ mobilization from ligaments, and limited access to organ surface data.
The ability to accurately represent the influences of anatomical changes and procedural limitations is critical for
appropriate evaluation of IGLS methodologies such as registration and deformation correction. However, these
influences have not yet been comprehensively integrated into a platform usable for assessment of methods in
laparoscopic IGLS. In this work, a mock laparoscopic liver simulator was created with realistic ligamenture to emulate
the complexities of this constrained surgical environment for the realization of laparoscopic IGLS. The mock surgical
system reproduces an insufflated abdominal cavity with dissectible ligaments, variable levels of incline matching
intraoperative patient positioning, and port locations in accordance with surgical protocol. True positions of targets
embedded in a tissue-mimicking phantom are measured from CT images. Using this setup, image-to-physical
registration accuracy was evaluated for simulations of laparoscopic right and left lobe mobilization to assess rigid
registration performance under more realistic laparoscopic conditions. Preliminary results suggest that non-rigid organ
deformations and the region of organ surface data collected affect the ability to attain highly accurate registrations in
laparoscopic applications.
Sparse surface digitization with an optically tracked stylus for use in an organ surface-based image-to-physical registration is an established approach for image-guided open liver surgery procedures. However, variability in sparse data collections during open hepatic procedures can produce disparity in registration alignments. In part, this variability arises from inconsistencies with the patterns and fidelity of collected intraoperative data. The liver lacks distinct landmarks and experiences considerable soft tissue deformation. Furthermore, data coverage of the organ is often incomplete or unevenly distributed. While more robust feature-based registration methodologies have been developed for image-guided liver surgery, it is still unclear how variation in sparse intraoperative data affects registration. In this work, we have developed an application to allow surgeons to study the performance of surface digitization patterns on registration. Given the intrinsic nature of soft-tissue, we incorporate realistic organ deformation when assessing fidelity of a rigid registration methodology. We report the construction of our application and preliminary registration results using four participants. Our preliminary results indicate that registration quality improves as users acquire more experience selecting patterns of sparse intraoperative surface data.
KEYWORDS: Brain, Magnetic resonance imaging, Computational modeling, Surgery, Modeling and simulation, Tumors, Data modeling, Performance modeling, Medical imaging, Image quality
The quality of brain tumor resection surgery is dependent on the spatial agreement between preoperative image and
intraoperative anatomy. However, brain shift compromises the aforementioned alignment. Currently, the clinical standard
to monitor brain shift is intraoperative magnetic resonance (iMR). While iMR provides better understanding of brain shift,
its cost and encumbrance is a consideration for medical centers. Hence, we are developing a model-based method that can
be a complementary technology to address brain shift in standard resections, with resource-intensive cases as referrals for
iMR facilities. Our strategy constructs a deformation ‘atlas’ containing potential deformation solutions derived from a
biomechanical model that account for variables such as cerebrospinal fluid drainage and mannitol effects. Volumetric
deformation is estimated with an inverse approach that determines the optimal combinatory ‘atlas’ solution fit to best
match measured surface deformation. Accordingly, preoperative image is updated based on the computed deformation
field. This study is the latest development to validate our methodology with iMR. Briefly, preoperative and intraoperative
MR images of 2 patients were acquired. Homologous surface points were selected on preoperative and intraoperative scans
as measurement of surface deformation and used to drive the inverse problem. To assess the model accuracy, subsurface
shift of targets between preoperative and intraoperative states was measured and compared to model prediction.
Considering subsurface shift above 3 mm, the proposed strategy provides an average shift correction of 59% across 2
cases. While further improvements in both the model and ability to validate with iMR are desired, the results reported are
encouraging.
KEYWORDS: Image registration, Liver, Image-guided intervention, Surgery, Tissues, Rigid registration, Data acquisition, Data analysis, Data modeling, Data centers, Computed tomography
In image-guided liver surgery (IGLS), sparse representations of the anterior organ surface may be collected
intraoperatively to drive image-to-physical space registration. Soft tissue deformation represents a significant source of
error for IGLS techniques. This work investigates the impact of surface data quality on current surface based IGLS
registration methods. In this work, we characterize the robustness of our IGLS registration methods to noise in organ
surface digitization. We study this within a novel human-to-phantom data framework that allows a rapid evaluation of
clinically realistic data and noise patterns on a fully characterized hepatic deformation phantom. Additionally, we
implement a surface data resampling strategy that is designed to decrease the impact of differences in surface acquisition.
For this analysis, n=5 cases of clinical intraoperative data consisting of organ surface and salient feature digitizations
from open liver resection were collected and analyzed within our human-to-phantom validation framework. As expected,
results indicate that increasing levels of noise in surface acquisition cause registration fidelity to deteriorate. With respect
to rigid registration using the raw and resampled data at clinically realistic levels of noise (i.e. a magnitude of 1.5 mm),
resampling improved TRE by 21%. In terms of nonrigid registration, registrations using resampled data outperformed
the raw data result by 14% at clinically realistic levels and were less susceptible to noise across the range of noise
investigated. These results demonstrate the types of analyses our novel human-to-phantom validation framework can
provide and indicate the considerable benefits of resampling strategies.
The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient’s preoperative image volume to more closely match the intraoperative state of the patient’s brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient’s neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.
Soft-tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface-based metrics, and subsurface validation has largely been performed via phantom experiments. The proposed method involves the analysis of two deformation-correction algorithms for open hepatic image-guided surgery systems via subsurface targets digitized with tracked intraoperative ultrasound (iUS). Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration and for use in retrospective deformation-correction algorithms. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. Mean closest-point distances between the feature contours delineated in the iUS images and corresponding three-dimensional anatomical model generated from preoperative tomograms were computed to quantify the extent to which the deformation-correction algorithms improved registration accuracy. The results for six patients, including eight anatomical targets, indicate that deformation correction can facilitate reduction in target error of ∼52%.
Brain shift compensation using computer modeling strategies is an important research area in the field of image-guided neurosurgery (IGNS). One important source of available sparse data during surgery to drive these frameworks is deformation tracking of the visible cortical surface. Possible methods to measure intra-operative cortical displacement include laser range scanners (LRS), which typically complicate the clinical workflow, and reconstruction of cortical surfaces from stereo pairs acquired with the operating microscopes. In this work, we propose and demonstrate a craniotomy simulation device that permits simulating realistic cortical displacements designed to measure and validate the proposed intra-operative cortical shift measurement systems. The device permits 3D deformations of a mock cortical surface which consists of a membrane made of a Dragon Skin® high performance silicone rubber on which vascular patterns are drawn. We then use this device to validate our stereo pair-based surface reconstruction system by comparing landmark positions and displacements measured with our systems to those positions and displacements as measured by a stylus tracked by a commercial optical system. Our results show a 1mm average difference in localization error and a 1.2mm average difference in displacement measurement. These results suggest that our stereo-pair technique is accurate enough for estimating intra-operative displacements in near real-time without affecting the surgical workflow.
Brain shift describes the deformation that the brain undergoes from mechanical and physiological effects typically during a neurosurgical or neurointerventional procedure. With respect to image guidance techniques, brain shift has been shown to compromise the fidelity of these approaches. In recent work, a computational pipeline has been developed to predict “brain shift” based on preoperatively determined surgical variables (such as head orientation), and subsequently correct preoperative images to more closely match the intraoperative state of the brain. However, a clinical workflow difficulty in the execution of this pipeline has been acquiring the surgical variables by the neurosurgeon prior to surgery. In order to simplify and expedite this process, an Android, Java-based application designed for tablets was developed to provide the neurosurgeon with the ability to orient 3D computer graphic models of the patient’s head, determine expected location and size of the craniotomy, and provide the trajectory into the tumor. These variables are exported for use as inputs for the biomechanical models of the preoperative computing phase for the brain shift correction pipeline. The accuracy of the application’s exported data was determined by comparing it to data acquired from the physical execution of the surgeon’s plan on a phantom head. Results indicated good overlap of craniotomy predictions, craniotomy centroid locations, and estimates of patient’s head orientation with respect to gravity. However, improvements in the app interface and mock surgical setup are needed to minimize error.
Soft tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface based metrics and sub-surface validation has largely been performed via phantom experiments. Tracked intraoperative ultrasound (iUS) provides a means to digitize sub-surface anatomical landmarks during clinical procedures. The proposed method involves the validation of a deformation correction algorithm for open hepatic image-guided surgery systems via sub-surface targets digitized with tracked iUS. Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration within the guidance system and for use in retrospective deformation correction. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. After the procedure, the clinician reviewed the iUS images to delineate contours of anatomical target features for use in the validation procedure. Mean closest point distances between the feature contours delineated in the iUS images and corresponding 3-D anatomical model generated from the preoperative tomograms were computed to quantify the extent to which the deformation correction algorithm improved registration accuracy. The preliminary results for two patients indicate that the deformation correction method resulted in a reduction in target error of approximately 50%.
KEYWORDS: 3D modeling, Data modeling, Liver, Antennas, Microwave radiation, Tumors, Tissues, Radiofrequency ablation, Finite element methods, Animal model studies
Development of a clinically accurate predictive model of microwave ablation (MWA) procedures would represent a significant advancement and facilitate an implementation of patient-specific treatment planning to achieve optimal probe placement and ablation outcomes. While studies have been performed to evaluate predictive models of MWA, the ability to quantify the performance of predictive models via clinical data has been limited to comparing geometric measurements of the predicted and actual ablation zones. The accuracy of placement, as determined by the degree of spatial overlap between ablation zones, has not been achieved. In order to overcome this limitation, a method of evaluation is proposed where the actual location of the MWA antenna is tracked and recorded during the procedure via a surgical navigation system. Predictive models of the MWA are then computed using the known position of the antenna within the preoperative image space. Two different predictive MWA models were used for the preliminary evaluation of the proposed method: (1) a geometric model based on the labeling associated with the ablation antenna and (2) a 3-D finite element method based computational model of MWA using COMSOL. Given the follow-up tomographic images that are acquired at approximately 30 days after the procedure, a 3-D surface model of the necrotic zone was generated to represent the true ablation zone. A quantification of the overlap between the predicted ablation zones and the true ablation zone was performed after a rigid registration was computed between the pre- and post-procedural tomograms. While both model show significant overlap with the true ablation zone, these preliminary results suggest a slightly higher degree of overlap with the geometric model.
The current protocol for image-guidance in liver surgeries involves rigid registration algorithm. Systematic studies
have shown that the liver can deform up to 2cms during surgeries thereby compromising the accuracy of the surgical
navigation systems. Compensating for intraoperative deformations using computational models has shown promising
results. In this work, we follow up the initial rigid registration with a computational approach. The proposed
computational approach relies on the closest point distances between the undeformed pre-operative surface and the
rigidly registered deformed intra-operative surface. We also introduce a spatial smoothing filter to generate a
realistic deformation field using the closest point distances. The proposed approach was validated in both phantom
experiments and clinical cases. Preliminary results are encouraging and suggest that computational models can be
used to improve the accuracy of image-guided liver surgeries.
Preoperative planning combined with image-guidance has shown promise towards increasing the accuracy of liver
resection procedures. The purpose of this study was to validate one such preoperative planning tool for four patients
undergoing hepatic resection. Preoperative computed tomography (CT) images acquired before surgery were used to
identify tumor margins and to plan the surgical approach for resection of these tumors. Surgery was then performed
with intraoperative digitization data acquire by an FDA approved image-guided liver surgery system (Pathfinder
Therapeutics, Inc., Nashville, TN). Within 5-7 days after surgery, post-operative CT image volumes were acquired.
Registration of data within a common coordinate reference was achieved and preoperative plans were compared to
the postoperative volumes. Semi-quantitative comparisons are presented in this work and preliminary results indicate
that significant liver regeneration/hypertrophy in the postoperative CT images may be present post-operatively. This
could challenge pre/post operative CT volume change comparisons as a means to evaluate the accuracy of
preoperative surgical plans.
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.
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.
Image to physical space registration is a very challenging problem in image guided surgical procedures for the
liver, due to deformation and paucity of prominent surface anatomical landmarks. Iterative closest point (ICP) algorithm,
the surface registration method used for registering the intraoperative laser range scanner (LRS) data with the
preoperative CT data in image guided liver surgery, requires a good starting pose to reduce the number of iterations.
Currently anatomical landmarks such as vessel bifurcations are used for an initial registration. This paper presents a
computational approach to obtain the initial alignment that would reduce contact with probes for registration during
surgical procedures. A priori user information about the anatomical orientation of the liver is incorporated and used to
orient the point clouds for segmented CT data and LRS liver data. Four points are computationally selected on the
anatomical anterior surface of CT point cloud data and corresponding points are localized on the LRS data using the
orientation information. These four points are then used to find the rigid transformation using the singular value
decomposition method. Nine datasets were tested using the computational approach and the results were evaluated using
the anatomical landmarks method as the "gold standard". Seven of the nine datasets converged to the same solution
using both the methods. The computational method, being an approximated approach, may increase the number of
iterations to converge to the solution. However since the method does not require precise localization of anatomical
landmarks, it could potentially reduce OR time.
Currently, the removal of kidney tumor masses uses only direct or laparoscopic visualizations, resulting in prolonged procedure and recovery times and reduced clear margin. Applying current image guided surgery (IGS) techniques, as those used in liver cases, to kidney resections (nephrectomies) presents a number of complications. Most notably is the limited field of view of the intraoperative kidney surface, which constrains the ability to obtain a surface delineation that is geometrically descriptive enough to drive a surface-based registration. Two different phantom orientations were used to model the laparoscopic and traditional partial nephrectomy views. For the laparoscopic view, fiducial point sets were compiled from a CT image volume using anatomical features such as the renal artery and vein. For the traditional view, markers attached to the phantom set-up were used for fiducials and targets. The fiducial points were used to perform a point-based registration, which then served as a guide for the surface-based registration. Laser range scanner (LRS) obtained surfaces were registered to each phantom surface using a rigid iterative closest point algorithm. Subsets of each phantom's LRS surface were used in a robustness test to determine the predictability of their registrations to transform the entire surface. Results from both orientations suggest that about half of the kidney's surface needs to be obtained intraoperatively for accurate registrations between the image surface and the LRS surface, suggesting the obtained kidney surfaces were geometrically descriptive enough to perform accurate registrations. This preliminary work paves the way for further development of kidney IGS systems.
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.
KEYWORDS: Video, Video processing, Image processing, Endoscopy, Detection and tracking algorithms, Computing systems, Surgery, Real time imaging, Frame grabbers, Tissues
Interactive, image-guided surgery (IIGS) has proven to increase the specificity of a variety of surgical procedures. However, current IIGS systems do not compensate for changes that occur intraoperatively and are not reflected in preoperative tomograms. Endoscopes and intraoperative ultrasound, used in minimally invasive surgery, provide real-time (RT) information in a surgical setting. Combining the information from RT imaging modalities with traditional IIGS techniques will further increase surgical specificity by providing enhanced anatomical information. In order to merge these techniques and obtain quantitative data from RT imaging modalities, a platform was developed to allow both the display and processing of video streams in RT. Using a Bandit-II CV frame grabber board (Coreco Imaging, St. Laurent, Quebec) and the associated library API, a dynamic link library was created in Microsoft Visual C++ 6.0 such that the platform could be incorporated into the IIGS system developed at Vanderbilt University. Performance characterization, using two relatively inexpensive host computers, has shown the platform capable of performing simple image processing operations on frames captured from a CCD camera and displaying the processed video data at near RT rates both independent of and while running the IIGS system.
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