Intraoperative Deformation Research Articles

SlicerCBM: automatic framework for biomechanical analysis of the brain

PurposeBrain shift that occurs during neurosurgery disturbs the brain’s anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations.MethodsWe created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI.ResultsOur framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI.ConclusionOur framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.

Soft-tissue simulation of the breast for intraoperative navigation and fusion of preoperative planning.

Computational preoperative planning offers the opportunity to reduce surgery time and patient risk. However, on soft tissues such as the breast, deviations between the preoperative and intraoperative settings largely limit the applicability of preoperative planning. In this work, we propose a high-performance accurate simulation model of the breast, to fuse preoperative information with the intraoperative deformation setting. Our simulation method encompasses three major elements: high-quality finite-element modeling (FEM), efficient handling of anatomical couplings for high-performance computation, and personalized parameter estimation from surface scans. We show the applicability of our method on two problems: 1) transforming high-quality preoperative scans to the intraoperative setting for fusion of preoperative planning data, and 2) real-time tracking of breast tumors for navigation during intraoperative radiotherapy. We have validated our methodology on a test cohort of nine patients who underwent tumor resection surgery and intraoperative radiotherapy, and we have quantitatively compared simulation results to intraoperative scans. The accuracy of our simulation results suggest clinical viability of the proposed methodology.

A particle filter approach to dynamic kidney pose estimation in robotic surgical exposure.

Traditional soft tissue registration methods require direct intraoperative visualization of a significant portion of the target anatomy in order to produce acceptable surface alignment. Image guidance is therefore generally not available during the robotic exposure of structures like the kidneys which are not immediately visualized upon entry into the abdomen. This paper proposes guiding surgical exposure with an iterative state estimator that assimilates small visual cues into an a priori anatomical model as exposure progresses, thereby evolving pose estimates for the occluded structures of interest. Intraoperative surface observations of a right kidney are simulated using endoscope tracking and preoperative tomography from a representative robotic partial nephrectomy case. Clinically relevant random perturbations of the true kidney pose are corrected using this sequence of observations in a particle filter framework to estimate an optimal similarity transform for fitting a patient-specific kidney model at each step. The temporal response of registration error is compared against that of serial rigid coherent point drift (CPD) in both static and simulated dynamic surgical fields, and for varying levels of observation persistence. In the static case, both particle filtering and persistent CPD achieved sub-5mm accuracy, with CPD processing observations 75% faster. Particle filtering outperformed CPD in the dynamic case under equivalent computation times due to the former requiring only minimal persistence. This proof-of-concept simulation study suggests that Bayesian state estimation may provide a viable pathway to image guidance for surgical exposure in the abdomen, especially in the presence of dynamic intraoperative tissue displacement and deformation.

An observational assessment of aortic deformation during infrarenal and complex endovascular aortic aneurysm repair

ObjectiveReal-time aortic deformation during endovascular aortic aneurysm repair (EVAR) has not been reported. Successful EVAR relies on predicting intraoperative aortic-endograft deformation from preoperative imaging. Correct prediction is essential, because malalignment of endografts decreases patient survival. We describe intraoperative aortic deformation during infrarenal EVAR and complex fenestrated/branched EVAR (F/BEVAR), relating deformation to preoperative anatomy and follow-up outcomes. MethodsA multicenter, retrospective cohort of aortic aneurysm patients undergoing operation between January 2019 and February 2021, substratified by repair, infrarenal EVAR (n = 50), F/BEVAR (n = 80), and iliac branch graft with F/B/EVAR (IBG + F/B/EVAR; n = 27), were compared using software-based nonrigid two- and three-dimensional aortic deformational intraoperative assessment (CYDAR). Preoperative computed tomography reconstructions of aortic and iliac tortuosities were assessed against intraoperative deformation, the primary outcome, and related to perioperative and follow-up adverse outcomes. ResultsAll treatment groups had low preoperative visceral aortic tortuosity; the EVAR group had higher iliac tortuosity (1.43 ± 0.05; P = .018). Intraoperative aortic visceral deformation was consistently cranial and anterior; IBG + F/B/EVAR patients had the largest magnitude deformation (superior mesenteric artery, EVAR 5.1 ± 0.9 mm; F/BEVAR 4.4 ± 0.4 mm; IBG 8.3 ± 1.2 mm; P = .004). Celiac artery, superior mesenteric artery, and bilateral renal artery deformations were correlated (R = 0.923-0.983). Iliac deformation was variable in magnitude and direction. Preoperative tortuosity was not correlated with the magnitude of intraoperative deformation nor was deformation magnitude related to endograft instability during follow-up, including endoleak development, reinterventions, or visceral vessel complications. ConclusionsThe aorta deforms consistently during EVAR at the visceral aortic segment but unpredictably at the iliac bifurcation. Aortoiliac deformation is unrelated to adverse perioperative outcomes, branch instability, or reinterventions during short-term follow-up.

A method of 3D-3D multi-stage non-rigid registration of the spine based on binocular structured light.

Intraoperative deformation and radiation are common problems in spinal surgery. A three-dimensional multi-stage dynamic iterative non-rigid registration method of the spine based on binocular structured light is proposed in this paper to overcome these problems. The problem of intraoperative radiation in traditional X-ray and CT is overcome by using binocular structured light. A three-dimensional spinal mask based on binary code is designed to reduce the influence of non-interested regions on the operation. Principal component analysis (PCA) algorithm is used to complete the rough registration between the preoperative CT model of the spine and the reconstructed surface of the intraoperative structured light. A new framework of multi-stage dynamic iterative non-rigid registration of the spine is proposed. The Iterative Closest Point (ICP) algorithm based on bidirectional selection is proposed to complete the single-stage registration of the spine. Then the multi-stage dynamic iterative registration of the spine is completed to solve the problem of large registration error caused by the deformation of the spine. The method proposed in this paper is compared with traditional registration methods, and its application is verified experimentally. The results show that the registration accuracy and time of the proposed method are mm and s, respectively. The accuracy of the method is 81.5% and 78.2% higher than that of the contour method and the method of marker points, respectively. The method can effectively avoid intraoperative radiation, reduce the registration error caused by the deformation of the spine, and has a high practicability.

Deep Learning Based Lung Region Segmentation with Data Preprocessing by Generative Adversarial Nets.

In endoscopic surgery, it is necessary to understand the three-dimensional structure of the target region to improve safety. For organs that do not deform much during surgery, preoperative computed tomography (CT) images can be used to understand their three-dimensional structure, however, deformation estimation is necessary for organs that deform substantially. Even though the intraoperative deformation estimation of organs has been widely studied, two-dimensional organ region segmentations from camera images are necessary to perform this estimation. In this paper, we propose a region segmentation method using U-net for the lung, which is an organ that deforms substantially during surgery. Because the accuracy of the results for smoker lungs is lower than that for non-smoker lungs, we improved the accuracy by translating the texture of the lung surface using a CycleGAN.

Evaluation of Neuronavigation in Glioma Surgery

Background: Glioma is one of the most common intracranial tumors, the treatment for which is surgical resection. Cerebral gliomas in eloquent brain areas are closely associated with important structures. The deep location of such gliomas renders complete resection difficult overlaying structures can be injured, causing hemiplegia, aphasia, coma, and other severe complications. The degree of resection directly influences the prognosis of patients. The application of neuronavigation enables the accurate localization of lesions during surgery, significantly improving microneurosurgery. The accuracy of navigation, however, has typically been compromised by several factors, including operation error, shifting of registration system, and intraoperative brain tissue deformation (brain shift). Aim of Study: The aim of the study is to evaluate and demonstrate the efficacy of neuronavigation in the management of brain gliomas. Patients and Methods: This is a prospective randomized clinical trial study included 35 patients with brain glioma were operated for excision or biopsy taken. Results: Localization of burr hole and craniotomy flap was excellent in 33 patients (94.2%) and fault in 2 cases (5.7%), 29 patients (82.8%) had near total excision of the lesions, and 6 patients (17.1%) had burr hole biopsy, intra operative course was smooth in 33 patients (94.2%) with no intra operative complications, 2 cases (5.7%) developed intra operative bleeding that was due to liver problems. Reading the post operative imaging shows that 27 patients (77.1%) had mild to moderate post operative edema around the site of the lesion, the other eight cases were free of hemorrhage, infarction or edema. Conclusion: We found that neuronavigation is very helpful in our cases in localization of the lesions, decreasing time of surgery, reducing morbidity and mortality and decreasing hospital stay. There are concerns about navigation systems including time consuming, calculation and registration, restriction of space and view inside the operating field, and so on, we didn’t find these issues as obstacles in our work, but we found that there are many advantages that can be helpful in the process of operation.

Soft tissue deformation tracking by means of an optimized fiducial marker layout with application to cancer tumors.

Interventional radiology methods have been adopted for intraoperative control of the surgical region of interest (ROI) in a wide range of minimally invasive procedures. One major obstacle that hinders the success of procedures using interventional radiology methods is the preoperative and intraoperative deformation of the ROI. While fiducial markers (FM) tracing has been shown to be promising in tracking such deformations, determining the optimal placement of the FM in the ROI remains a significant challenge. The current study proposes a computational framework to address this problem by preoperatively optimizing the layout of FM, thereby enabling an accurate tracking of the ROI deformations. The proposed approach includes three main components: (1) creation of virtual deformation benchmarks, (2) method of predicting intraoperative tissue deformation based on FM registration, and (3) FM layout optimization. To account for the large variety of potential ROI deformations, virtual benchmarks are created by applying a multitude of random force fields on the tumor surface in physically based simulations. The ROI deformation prediction is carried out by solving the inverse problem of finding the smoothest force field that leads to the observed FM displacements. Based on this formulation, a simulated annealing approach is employed to optimize the FM layout that produces the best prediction accuracy. The proposed approach is capable of finding an FM layout that outperforms the rationally chosen layouts by 40% in terms of ROI prediction accuracy. For a maximum induced displacement of 20mm on the tumor surface, the average maximum error between the benchmarks and our FM-optimized predictions is about 1.72mm, which falls within the typical resolution of ultrasound imaging. The proposed framework can optimize FM layout to effectively reduce the errors in the intraoperative deformation prediction process, thus bridging the gap between preoperative imaging and intraoperative tissue deformation.

Resection Process Map: A novel dynamic simulation system for pulmonary resection

ObjectiveUse of 3-dimensional computed tomography for preoperative and intraoperative simulation has been introduced in the field of thoracic surgery. However, 3-dimensional computed tomography provides only static simulation, which is a significant limitation of surgical simulation. Dynamic simulation, reflecting the intraoperative deformation of the lung, has not been developed. The aim of this study was to develop a novel simulation system that generates dynamic images based on patient-specific computed tomography data. MethodsWe developed an original software, the Resection Process Map, for anatomic pulmonary resection. The Resection Process Map semi-automatically generates virtual dynamic images based on patient-specific computed tomography data. We retrospectively evaluated its accuracy in 18 representative cases by comparing the virtual dynamic images with the actual surgical images. ResultsIn this study, 9 patients who underwent lobectomy and 9 patients who underwent segmentectomy were included. For each case, a virtual dynamic image was successfully generated semi-automatically by the Resection Process Map. The Resection Process Map accurately delineated 98.6% of vessel branches and all the bronchi. The median time required to obtain the images was 121.3 seconds. ConclusionsWe successfully developed a novel dynamic simulation system, the Resection Process Map, for anatomic pulmonary resection.

A Numerical Preoperative Planning Model to Predict Arterial Deformations in Endovascular Aortic Aneurysm Repair

Endovascular aneurysm repair is rapidly emerging as the primary preferred method for treating abdominal aortic aneurysm. In this image-guided interventional procedure, to obtain the roadmap and decrease contrast injections, preoperative CT images are overlaid onto live fluoroscopy images using various 2D/3D image fusion techniques. However, the structural changes due to the insertion of stiff tools degrade the fusion accuracy. To correct the mismatch and quantify the intraoperative deformations, we present a patient-specific biomechanical model of the aorto-iliac structure and its surrounding tissues. The predictive capability of the model was evaluated against intraoperative data for a group of four patients. Incorporating the perivascular tissues into the model significantly improved the results and the mean distance between the real and simulated endovascular tools was 2.99 ± 1.78mm on the ipsilateral side and 4.59 ± 3.25mm on the contralateral side. Moreover, the distance between the deformed iliac ostia and their corresponding landmarks on intraoperative images was 2.99 ± 2.48mm.

Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging.

Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed. This model captures the interdependency of two image modalities; MRI as an intensity image and iUS as a depth image. Based on this model, the transformation between the two modalities is minimized by using a bimodal pair of analysis operators which are learned by optimizing a joint co-sparsity function using a conjugate gradient.Experimental validation of our algorithm confirms that our registration approach outperforms several of other state-of-the-art registration methods quantitatively. The evaluation was performed using seven patient dataset with the mean registration error of only 1.83 mm. Our intensity-based co-sparse analysis model has improved the accuracy of non-rigid multimodal medical image registration by 15.37% compared to the curvelet based residual complexity as a powerful registration method, in a computational time compatible with clinical use.

Biomechanics-based graph matching for augmented CT-CBCT.

Augmenting intraoperative cone beam computed tomography (CBCT) images with preoperative computed tomography data in the context of image-guided liver therapy is proposed. The expected benefit is an improved visualization of tumor(s), vascular system and other internal structures of interest. An automatic elastic registration based on matching of vascular trees extracted from both the preoperative and intraoperative images is presented. Although methods dedicated to nonrigid graph matching exist, they are not efficient when large intraoperative deformations of tissues occur, as is the case during the liver surgery. The contribution is an extension of the graph matching algorithm using Gaussian process regression (GPR)(Serradell et al. in IEEE Trans Pattern Anal Mach Intell 37(3):625-638, 2015): First, an improved GPR matching is introduced by imposing additional constraints during the matching when the number of hypothesis is large; like the original algorithm, this extended version does not require a manual initialization of matching. Second, a fast biomechanical model is employed to make the method capable of handling large deformations. The proposed automatic intraoperative augmentation is evaluated on both synthetic and real data. It is demonstrated that the algorithm is capable of handling large deformations, thus being more robust and reliable than previous approaches. Moreover, the time required to perform the elastic registration is compatible with the intraoperative navigation scenario. A biomechanics-based graph matching method, which can handle large deformations and augment intraoperative CBCT, is presented and evaluated.

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery.

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 [Formula: see text] and surface error of [Formula: see text] 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.

Intraoperative implant rod three-dimensional geometry measured by dual camera system during scoliosis surgery.

Treatment for severe scoliosis is usually attained when the scoliotic spine is deformed and fixed by implant rods. Investigation of the intraoperative changes of implant rod shape in three-dimensions is necessary to understand the biomechanics of scoliosis correction, establish consensus of the treatment, and achieve the optimal outcome. The objective of this study was to measure the intraoperative three-dimensional geometry and deformation of implant rod during scoliosis corrective surgery.A pair of images was obtained intraoperatively by the dual camera system before rotation and after rotation of rods during scoliosis surgery. The three-dimensional implant rod geometry before implantation was measured directly by the surgeon and after surgery using a CT scanner. The images of rods were reconstructed in three-dimensions using quintic polynomial functions. The implant rod deformation was evaluated using the angle between the two three-dimensional tangent vectors measured at the ends of the implant rod.The implant rods at the concave side were significantly deformed during surgery. The highest rod deformation was found after the rotation of rods. The implant curvature regained after the surgical treatment.Careful intraoperative rod maneuver is important to achieve a safe clinical outcome because the intraoperative forces could be higher than the postoperative forces. Continuous scoliosis correction was observed as indicated by the regain of the implant rod curvature after surgery.

Toward a real time multi-tissue Adaptive Physics-Based Non-Rigid Registration framework for brain tumor resection

This paper presents an adaptive non-rigid registration method for aligning pre-operative MRI with intra-operative MRI (iMRI) to compensate for brain deformation during brain tumor resection. This method extends a successful existing Physics-Based Non-Rigid Registration (PBNRR) technique implemented in ITKv4.5. The new method relies on a parallel adaptive heterogeneous biomechanical Finite Element (FE) model for tissue/tumor removal depicted in the iMRI. In contrast the existing PBNRR in ITK relies on homogeneous static FE model designed for brain shift only (i.e., it is not designed to handle brain tumor resection). As a result, the new method (1) accurately captures the intra-operative deformations associated with the tissue removal due to tumor resection and (2) reduces the end-to-end execution time to within the time constraints imposed by the neurosurgical procedure. The evaluation of the new method is based on 14 clinical cases with: (i) brain shift only (seven cases), (ii) partial tumor resection (two cases), and (iii) complete tumor resection (five cases). The new adaptive method can reduce the alignment error up to seven and five times compared to a rigid and ITK's PBNRR registration methods, respectively. On average, the alignment error of the new method is reduced by 9.23 and 5.63 mm compared to the alignment error from the rigid and PBNRR method implemented in ITK. Moreover, the total execution time for all the case studies is about 1 min or less in a Linux Dell workstation with 12 Intel Xeon 3.47 GHz CPU cores and 96 GB of RAM.

Analysis of carotid artery deformation in different head and neck positions for maxillofacial catheter navigation in advanced oral cancer treatment

BackgroundTo improve the accuracy of catheter navigation, it is important to develop a method to predict shifts of carotid artery (CA) bifurcations caused by intraoperative deformation. An important factor affecting the accuracy of electromagnetic maxillofacial catheter navigation systems is CA deformations. We aimed to assess CA deformation in different head and neck positions.MethodsUsing two sets of computed tomography angiography (CTA) images of six patients, displacements of the skull (maxillofacial segments), C1–C4 cervical vertebrae, mandible (mandibular segment), and CA along with its branches were analyzed. Segmented rigid bones around CA were considered the main causes of CA deformation. After superimposition of maxillofacial segments, C1–C4 and mandible segments were superimposed separately for displacement measurements. Five bifurcation points (vA–vE) were assessed after extracting the CA centerline. A new standardized coordinate system, regardless of patient-specific scanning positions, was employed. It was created using the principal axes of inertia of the maxillofacial bone segments of patients. Position and orientation parameters were transferred to this coordinate system. CA deformation in different head and neck positions was assessed.ResultsAbsolute shifts in the center of gravity in the bone models for different segments were C1, 1.02 ± 0.9; C2, 2.18 ± 1.81; C3, 4.25 ± 3.85; C4, 5.90 ± 5.14; and mandible, 1.75 ± 2.76 mm. Shifts of CA bifurcations were vA, 5.52 ± 4.12; vB, 4.02 ± 3.27; vC, 4.39 ± 2.42; vD, 4.48 ± 1.88; and vE, 2.47 ± 1.32. Displacements, position changes, and orientation changes of C1–C4 segments as well as the displacements of all CA bifurcation points were similar in individual patients.ConclusionsCA deformation was objectively proven as an important factor contributing to errors in maxillofacial navigation. Our study results suggest that small movements of the bones around CA can result in small CA deformations. Although patients’ faces were not fixed properly during CT scanning, C1–C4 and vA–vE displacements were similar in individual patients. We proposed a novel method for accumulation of the displacement data, and this study indicated the importance of surrounding bone displacements in predicting CA bifurcation.

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 patient’s 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.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time; Application to non-rigid neuroimage registration

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer.

Vessel target location estimation during the TIPS procedure

Creation of a transjugular intrahepatic portosystemic shunt (TIPS) requires passage of a needle toward a moving target that is only seen transiently by X-ray prior to needle passage. Intraoperative, 3D target localization would facilitate target access and improve the safety of the procedure. The clinical assumption is that patients undergoing the TIPS procedure possess rigid, cirrhotic livers that undergo only intraoperative translation without significant deformation or rotation. Based upon this assumption, we hypothesize that the position of any unseen, 3D target point within the liver can be determined intraoperatively by precalculation of the relative positions of the target point to a different 3D point that can be tracked intraoperatively. This paper examines this hypothesis using intraoperatively acquired, biplane, X-ray images of seven patients. In six, we tracked the effects of cardiac and respiratory motion, and in three the effects of needle pressure. Methods involved reconstruction of 3D vessel bifurcation and other trackable intrahepatic points from biplane angiograms, measurement of liver deformation by examining changing distances between these 3D points over time, and comparison of expected to actual displacements of these points with respect to a fixed reference point in the liver. We conclude that, for the rigid livers associated with patients undergoing TIPS, that there is less intraoperative deformation than previously reported by other groups addressing healthy liver deformation, and that the location of an unseen target can be predicted within 3mm accuracy.

Concepts and Preliminary Data Toward the Realization of Image-guided Liver Surgery

Image-guided surgery provides navigational assistance to the surgeon by displaying the surgical probe position on a set of preoperative tomograms in real time. In this study, the feasibility of implementing image-guided surgery concepts into liver surgery was examined during eight hepatic resection procedures. Preoperative tomographic image data were acquired and processed. Accompanying intraoperative data on liver shape and position were obtained through optically tracked probes and laser range scanning technology. The preoperative and intraoperative representations of the liver surface were aligned using the iterative closest point surface matching algorithm. Surface registrations resulted in mean residual errors from 2 to 6 mm, with errors of target surface regions being below a stated goal of 1 cm. Issues affecting registration accuracy include liver motion due to respiration, the quality of the intraoperative surface data, and intraoperative organ deformation. Respiratory motion was quantified during the procedures as cyclical, primarily along the cranial–caudal direction. The resulting registrations were more robust and accurate when using laser range scanning to rapidly acquire thousands of points on the liver surface and when capturing unique geometric regions on the liver surface, such as the inferior edge. Finally, finite element models recovered much of the observed intraoperative deformation, further decreasing errors in the registration. Image-guided liver surgery has shown the potential to provide surgeons with important navigation aids that could increase the accuracy of targeting lesions and the number of patients eligible for surgical resection.