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BACKGROUND - Although systems of 3-dimensional image-guided surgery are a valuable adjunct across numerous procedures, differences in organ shape between that reflected in the preoperative image data and the intraoperative state can compromise the fidelity of such guidance based on the image. In this work, we assessed in real time a novel, 3-dimensional image-guided operation platform that incorporates soft tissue deformation.
METHODS - A series of 125 alignment evaluations were performed across 20 patients. During the operation, the surgeon assessed the liver by swabbing an optically tracked stylus over the liver surface and viewing the image-guided operation display. Each patient had approximately 6 intraoperative comparative evaluations. For each assessment, 1 of only 2 types of alignments were considered: conventional rigid and novel deformable. The series of alignment types used was randomized and blinded to the surgeon. The surgeon provided a rating, R, from -3 to +3 for each display compared with the previous display, whereby a negative rating indicated degradation in fidelity and a positive rating an improvement.
RESULTS - A statistical analysis of the series of rating data by the clinician indicated that the surgeons were able to perceive an improvement (defined as a R > 1) of the model-based registration over the rigid registration (P = .01) as well as a degradation (defined as R < -1) when the rigid registration was compared with the novel deformable guidance information (P = .03).
CONCLUSION - This study provides evidence of the benefit of deformation correction in providing an accurate location for the liver for use in image-guided surgery systems.
Copyright © 2017 Elsevier Inc. All rights reserved.
Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.
Our objective was to prospectively evaluate implementation of a new cochlear implant (CI) mapping technique, image-guided cochlear implant programming (IGCIP), at a site distant to the site of development. IGCIP consists of identifying the geometric relationship between CI electrodes and the modiolus and deactivating electrodes that interfere with neighboring electrodes. IGCIP maps for 17 ears of 15 adult CI patients were developed at a central image-processing center, Vanderbilt, and implemented at a distant tertiary care center, House Ear Institute. Before IGCIP and again 4 weeks after, qualitative and quantitative measures were made. While there were no statistically significant groupwise differences detected between baseline and IGCIP qualitative or quantitative measures, 11 of the 17 (64.7%) elected to keep the IGCIP map. Computed tomography (CT) image quality appears to be crucial for successful IGCIP, with 100% of those with high-resolution CT scans keeping their maps compared to 53.8% without.
In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n = 13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods. An important feature of the approach is that it centers on combining workflow-realistic data acquisition and surgical deformations that are appropriate in behavior and magnitude. Using the approach, we investigate volumetric target registration error (TRE) with both current rigid IGLS and our improved nonrigid registration methods. Additionally, we introduce a spatial data resampling approach to mitigate the workflow-sensitive sampling problem. Using our human-to-phantom approach, TRE after routine rigid registration was 10.9 ± 0.6 mm with a signed closest point distance associated with residual surface fit in the range of ±10 mm, highly representative of open liver resections. After applying our novel resampling strategy and improved deformation correction method, TRE was reduced by 51%, i.e., a TRE of 5.3 ± 0.5 mm. This paper reported herein realizes a novel tractable approach for the validation of image-to-physical registration methods and demonstrates promising results for our correction method.
PURPOSE - Organ-level registration is critical to image-guided therapy in soft tissue. This is especially important in organs such as the kidney which can freely move. We have developed a method for registration that combines three-dimensional locations from a holographic conoscope with an endoscopically obtained textured surface. By combining these data sources clear decisions as to the tissue from which the points arise can be made.
METHODS - By localizing the conoscope's laser dot in the endoscopic space, we register the textured surface to the cloud of conoscopic points. This allows the cloud of points to be filtered for only those arising from the kidney surface. Once a valid cloud is obtained we can use standard surface registration techniques to perform the image-space to physical-space registration. Since our methods use two distinct data sources we test for spatial accuracy and characterize temporal effects in phantoms, ex vivo porcine and human kidneys. In addition we use an industrial robot to provide controlled motion and positioning for characterizing temporal effects.
RESULTS - Our initial surface acquisitions are hand-held. This means that we take approximately 55 s to acquire a surface. At that rate we see no temporal effects due to acquisition synchronization or probe speed. Our surface registrations were able to find applied targets with submillimeter target registration errors.
CONCLUSION - The results showed that the textured surfaces could be reconstructed with submillimetric mean registration errors. While this paper focuses on kidney applications, this method could be applied to any anatomical structures where a line of sight can be created via open or minimally invasive surgical techniques.
PURPOSE - Brain shift during neurosurgical procedures must be corrected for in order to reestablish accurate alignment for successful image-guided tumor resection. Sparse-data-driven biomechanical models that predict physiological brain shift by accounting for typical deformation-inducing events such as cerebrospinal fluid drainage, hyperosmotic drugs, swelling, retraction, resection, and tumor cavity collapse are an inexpensive solution. This study evaluated the robustness and accuracy of a biomechanical model-based brain shift correction system to assist with tumor resection surgery in 16 clinical cases.
METHODS - Preoperative computation involved the generation of a patient-specific finite element model of the brain and creation of an atlas of brain deformation solutions calculated using a distribution of boundary and deformation-inducing forcing conditions (e.g., sag, tissue contraction, and tissue swelling). The optimum brain shift solution was determined using an inverse problem approach which linearly combines solutions from the atlas to match the cortical surface deformation data collected intraoperatively. The computed deformations were then used to update the preoperative images for all 16 patients.
RESULTS - The mean brain shift measured ranged on average from 2.5 to 21.3 mm, and the biomechanical model-based correction system managed to account for the bulk of the brain shift, producing a mean corrected error ranging on average from 0.7 to 4.0 mm.
CONCLUSIONS - Biomechanical models are an inexpensive means to assist intervention via correction for brain deformations that can compromise surgical navigation systems. To our knowledge, this study represents the most comprehensive clinical evaluation of a deformation correction pipeline for image-guided neurosurgery.
PURPOSE - Unfortunately, the current re-excision rates for breast conserving surgeries due to positive margins average 20-40 %. The high re-excision rates arise from difficulty in localizing tumor boundaries intraoperatively and lack of real-time information on the presence of residual disease. The work presented here introduces the use of supine magnetic resonance (MR) images, digitization technology, and biomechanical models to investigate the capability of using an image guidance system to localize tumors intraoperatively.
METHODS - Preoperative supine MR images were used to create patient-specific biomechanical models of the breast tissue, chest wall, and tumor. In a mock intraoperative setup, a laser range scanner was used to digitize the breast surface and tracked ultrasound was used to digitize the chest wall and tumor. Rigid registration combined with a novel nonrigid registration routine was used to align the preoperative and intraoperative patient breast and tumor. The registration framework is driven by breast surface data (laser range scan of visible surface), ultrasound chest wall surface, and MR-visible fiducials. Tumor localizations by tracked ultrasound were only used to evaluate the fidelity of aligning preoperative MR tumor contours to physical patient space. The use of tracked ultrasound to digitize subsurface features to constrain our nonrigid registration approach and to assess the fidelity of our framework makes this work unique. Two patient subjects were analyzed as a preliminary investigation toward the realization of this supine image-guided approach.
RESULTS - An initial rigid registration was performed using adhesive MR-visible fiducial markers for two patients scheduled for a lumpectomy. For patient 1, the rigid registration resulted in a root-mean-square fiducial registration error (FRE) of 7.5 mm and the difference between the intraoperative tumor centroid as visualized with tracked ultrasound imaging and the registered preoperative MR counterpart was 6.5 mm. Nonrigid correction resulted in a decrease in FRE to 2.9 mm and tumor centroid difference to 5.5 mm. For patient 2, rigid registration resulted in a FRE of 8.8 mm and a 3D tumor centroid difference of 12.5 mm. Following nonrigid correction for patient 2, the FRE was reduced to 7.4 mm and the 3D tumor centroid difference was reduced to 5.3 mm.
CONCLUSION - Using our prototype image-guided surgery platform, we were able to align intraoperative data with preoperative patient-specific models with clinically relevant accuracy; i.e., tumor centroid localizations of approximately 5.3-5.5 mm.
BACKGROUND - Finding the optimal location for the implantation of the electrode in deep brain stimulation (DBS) surgery is crucial for maximizing the therapeutic benefit to the patient. Such targeting is challenging for several reasons, including anatomic variability between patients as well as the lack of consensus about the location of the optimal target.
OBJECTIVE - To compare the performance of popular manual targeting methods against a fully automatic nonrigid image registration-based approach.
METHODS - In 71 Parkinson disease subthalamic nucleus (STN)-DBS implantations, an experienced functional neurosurgeon selected the target manually using 3 different approaches: indirect targeting using standard stereotactic coordinates, direct targeting based on the patient magnetic resonance imaging, and indirect targeting relative to the red nucleus. Targets were also automatically predicted by using a leave-one-out approach to populate the CranialVault atlas with the use of nonrigid image registration. The different targeting methods were compared against the location of the final active contact, determined through iterative clinical programming in each individual patient.
RESULTS - Targeting by using standard stereotactic coordinates corresponding to the center of the motor territory of the STN had the largest targeting error (3.69 mm), followed by direct targeting (3.44 mm), average stereotactic coordinates of active contacts from this study (3.02 mm), red nucleus-based targeting (2.75 mm), and nonrigid image registration-based automatic predictions using the CranialVault atlas (2.70 mm). The CranialVault atlas method had statistically smaller variance than all manual approaches.
CONCLUSION - Fully automatic targeting based on nonrigid image registration with the use of the CranialVault atlas is as accurate and more precise than popular manual methods for STN-DBS.
RATIONALE AND OBJECTIVES - The objective of this study was to evaluate and compare contrast-enhanced subharmonic and harmonic ultrasound as tools for characterizing solid renal masses and monitoring their response to cryoablation therapy.
MATERIALS AND METHODS - Sixteen patients undergoing percutaneous ablation of a renal mass provided informed consent to undergo ultrasound examinations the morning before and approximately 4 months after cryoablation. Ultrasound contrast parameters during pretreatment imaging were compared to biopsy results obtained during ablation (n = 13). Posttreatment changes were evaluated by a radiologist and compared to contrast-enhanced magnetic resonance imaging (MRI)/computed tomography (CT) follow-up.
RESULTS - All masses initially showed heterogeneous enhancement with both subharmonic and harmonic ultrasound. Early contrast washout in the mass relative to the cortex was observed in 6 of 9 malignant and 0 of 4 benign lesions in subharmonic mode and 8 of 9 malignant and 1 of 4 benign lesions in harmonic imaging. In cases where the lesion was adequately visualized at follow-up (n = 12), subharmonic and harmonic ultrasound showed accuracies of 83% and 75%, respectively, in predicting treatment outcome. Although harmonic imaging showed less overall error, no significant differences (P > .29) in ablation cavity volumes were observed between MRI/CT and either contrast-imaging mode.
CONCLUSIONS - Subharmonic and harmonic contrast-enhanced ultrasound may be a safe and accurate imaging alternative for characterizing renal masses and evaluating their response to cryoablation therapy. Although subharmonic imaging was more accurate in detecting effective cryoablation, harmonic imaging was superior in quantifying ablation cavity volumes.
Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.
In brain tumor surgery, soft-tissue deformation, known as brain shift, introduces inaccuracies in the application of the preoperative surgical plan and impedes the advancement of image-guided surgical (IGS) systems. Considerable progress in using patient-specific biomechanical models to update the preoperative images intraoperatively has been made. These model-update methods rely on accurate intraoperative 3D brain surface displacements. In this work, we investigate and develop a fully automatic method to compute these 3D displacements for lengthy (~15 minutes) stereo-pair video sequences acquired during neurosurgery. The first part of the method finds homologous points temporally in the video and the second part computes the nonrigid transformation between these homologous points. Our results, based on parts of 2 clinical cases, show that this speedy and promising method can robustly provide 3D brain surface measurements for use with model-based updating frameworks.