PurposeComputational methods for image-to-physical registration during surgical guidance frequently rely on sparse point clouds obtained over a limited region of the organ surface. However, soft tissue deformations complicate the ability to accurately infer anatomical alignments from sparse descriptors of the organ surface. The Image-to-Physical Liver Registration Sparse Data Challenge introduced at SPIE Medical Imaging 2019 seeks to characterize the performance of sparse data registration methods on a common dataset to benchmark and identify effective tactics and limitations that will continue to inform the evolution of image-to-physical registration algorithms.ApproachThree rigid and five deformable registration methods were contributed to the challenge. The deformable approaches consisted of two deep learning and three biomechanical boundary condition reconstruction methods. These algorithms were compared on a common dataset of 112 registration scenarios derived from a tissue-mimicking phantom with 159 subsurface validation targets. Target registration errors (TRE) were evaluated under varying conditions of data extent, target location, and measurement noise. Jacobian determinants and strain magnitudes were compared to assess displacement field consistency.ResultsRigid registration algorithms produced significant differences in TRE ranging from 3.8±2.4 mm to 7.7±4.5 mm, depending on the choice of technique. Two biomechanical methods yielded TRE of 3.1±1.8 mm and 3.3±1.9 mm, which outperformed optimal rigid registration of targets. These methods demonstrated good performance under varying degrees of surface data coverage and across all anatomical segments of the liver. Deep learning methods exhibited TRE ranging from 4.3±3.3 mm to 7.6±5.3 mm but are likely to improve with continued development. TRE was weakly correlated among methods, with greatest agreement and field consistency observed among the biomechanical approaches.ConclusionsThe choice of registration algorithm significantly impacts registration accuracy and variability of deformation fields. Among current sparse data driven image-to-physical registration algorithms, biomechanical simulations that incorporate task-specific insight into boundary conditions seem to offer best performance.
The ability to accurately account for intraoperative soft tissue deformations has been a longstanding barrier to efficacious translation of image-guided frameworks into abdominal interventions. In surgical applications, few data acquisition systems are amenable to stringent operative workflow constraints, and many are too costly for widespread adoption. Consequently, computational methods for surgical guidance based on sparse data obtained over the organ surface have become prevalent within approaches for image-to-patient alignment of soft tissue. However, the sparse data environment presents an especially challenging algorithmic landscape for accurately inferring deformable anatomical alignments between preoperative and intraoperative organ states from incomplete information sources. This work, presented as a preliminary conclusion to the image-to-physical liver registration sparse data challenge introduced at SPIE Medical Imaging 2019, seeks to characterize the potential for sparse data registration algorithms to achieve high fidelity predictions of intraoperative organ deformations from sparse descriptors of organ surface shape. A total of seven rigid and nonrigid biomechanical and deep learning registration techniques are compared, and the findings suggest that the family of biomechanically simulated boundary condition reconstruction techniques offers a promising opportunity for accurately estimating intraoperative organ deformation states from sparse intraoperative point clouds. Over a common dataset of 112 registration scenarios, this family of deformable registration techniques was found to outperform globally optimal rigid registrations, was robust to varying degrees of surface data coverage, and maintained good performance under added sources of measurement noise. Further analysis investigates error correlations among methods to illuminate sparse data performance within state-of-the-art image-to-physical registration algorithms.
Computational tools, such as "digital twin" modeling, are beginning to enable patient-specific surgical planning of ablative therapies to treat hepatocellular carcinoma. Digital twins models use patient functional data and biomarker imaging to build anatomically accurate models to forecast therapeutic outcomes through simulation, i.e., providing accurate information for guiding clinical decision-making. In microwave ablation (MWA), tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) can affect ablative therapies, but current thermal dosing guidelines do not account for these differences. This study establishes an imaging-data-driven framework to construct digital twin biophysical models to predict ablation extents in livers with varying fat content in MWA. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models, and fat-quantification images were acquired to reconstruct spatially accurate biophysical material properties. Simulated patient-specific microwave ablations of homogenous digital-twin models (control) and enhanced digital twin models were performed at four levels of fatty liver disease. When looking at the short diameter (SD), long diameter (LD), ablation volume, and spherical index of the ablation margins - the heterogenous digital-twin models did not produce significantly different ablation margins compared to the control models. Both models produced results that report ablation margins for patients with high-fat livers are larger than low-fat livers (LD of 6.17cm vs. 6.30cm and SD of 2.10 vs. 1.99, respectively). Overall, the results suggest that modeling heterogeneous clinical fatty liver disease using fat-quantitative imaging data has the potential to improve patient specificity for this treatment modality.
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.
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.
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: 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.
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.
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%.
The purpose of this work is to develop an anatomically and mechanically representative breast phantom for the validation of breast conserving surgical therapies, specifically, in this case, image guided surgeries. Using three patients scheduled for lumpectomy and four healthy volunteers in mock surgical presentations, the magnitude, direction, and location of breast deformations was analyzed. A phantom setup was then designed to approximate such deformations in a mock surgical environment. Specifically, commercially available and custom-built polyvinyl alcohol (PVA) phantoms were used to mimic breast tissue during surgery. A custom designed deformation apparatus was then created to reproduce deformations seen in typical clinical setups of the pre- and intra-operative breast geometry. Quantitative analysis of the human subjects yielded a positive correlation between breast volume and amount of breast deformation. Phantom results reflected similar behavior with the custom-built PVA phantom outperforming the commercial phantom.
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.
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