Design and evaluation of an optically-tracked single-CCD laser range scanner.

PURPOSE: Acquisition of laser range scans of an organ surface has the potential to efficiently provide measurements of geometric changes to soft tissue during a surgical procedure. A laser range scanner design is reported here which has been developed to drive intraoperative updates to conventional image-guided neurosurgery systems. METHODS: The scanner is optically-tracked in the operating room with a multiface passive target. The novel design incorporates both the capture of surface geometry (via laser illumination) and color information (via visible light collection) through a single-lens onto the same charge-coupled device (CCD). The accuracy of the geometric data was evaluated by scanning a high-precision phantom and comparing relative distances between landmarks in the scans with the corresponding ground truth (known) distances. The range-of-motion of the scanner with respect to the optical camera was determined by placing the scanner in common operating room configurations while sampling the visibility of the reflective spheres. The tracking accuracy was then analyzed by fixing the scanner and phantom in place, perturbing the optical camera around the scene, and observing variability in scan locations with respect to a tracked pen probe ground truth as the camera tracked the same scene from different positions. RESULTS: The geometric accuracy test produced a mean error and standard deviation of 0.25 ± 0.40 mm with an RMS error of 0.47 mm. The tracking tests showed that the scanner could be tracked at virtually all desired orientations required in the OR set up, with an overall tracking error and standard deviation of 2.2 ± 1.0 mm with an RMS error of 2.4 mm. There was no discernible difference between any of the three faces on the lasers range scanner (LRS) with regard to tracking accuracy. CONCLUSIONS: A single-lens laser range scanner design was successfully developed and implemented with sufficient scanning and tracking accuracy for image-guided surgery.

Clinical evaluation of a model-updated image-guidance approach to brain shift compensation: experience in 16 cases.

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.

Toward a generic real-time compression correction framework for tracked ultrasound.

PURPOSE: Tissue compression during ultrasound imaging leads to error in the location and geometry of subsurface targets during soft tissue interventions. We present a novel compression correction method, which models a generic block of tissue and its subsurface tissue displacements resulting from application of a probe to the tissue surface. The advantages of the new method are that it can be realized independent of preoperative imaging data and is capable of near-video framerate compression compensation for real-time guidance. METHODS: The block model is calibrated to the tip of any tracked ultrasound probe. Intraoperative digitization of the tissue surface is used to measure the depth of compression and provide boundary conditions to the biomechanical model of the tissue. The tissue displacement field solution of the model is inverted to nonrigidly transform the ultrasound images to an estimation of the tissue geometry prior to compression. This method was compared to a previously developed method using a patient-specific model and within the context of simulation, phantom, and clinical data. RESULTS: Experimental results with gel phantoms demonstrated that the proposed generic method reduced the mock tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 2.1 ± 0.7 mm and reduced mock tumor centroid alignment error from 7.6 ± 2.6 to 2.6 ± 1.1mm. The method was applied to a clinical case and reduced the in vivo tumor margin MHD error from 5.4 ± 0.1 to 2.9 ± 0.1mm, and the centroid alignment error from 7.2 ± 0.2 to 3.8 ± 0.4 mm. CONCLUSIONS: The correction method was found to effectively improve alignment of ultrasound and tomographic images and was more efficient compared to a previously proposed correction.

Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI.

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.

Persistent and automatic intraoperative 3D digitization of surfaces under dynamic magnifications of an operating microscope.

One of the major challenges impeding advancement in image-guided surgical (IGS) systems is the soft-tissue deformation during surgical procedures. These deformations reduce the utility of the patient's preoperative images and may produce inaccuracies in the application of preoperative surgical plans. Solutions to compensate for the tissue deformations include the acquisition of intraoperative tomographic images of the whole organ for direct displacement measurement and techniques that combines intraoperative organ surface measurements with computational biomechanical models to predict subsurface displacements. The later solution has the advantage of being less expensive and amenable to surgical workflow. Several modalities such as textured laser scanners, conoscopic holography, and stereo-pair cameras have been proposed for the intraoperative 3D estimation of organ surfaces to drive patient-specific biomechanical models for the intraoperative update of preoperative images. Though each modality has its respective advantages and disadvantages, stereo-pair camera approaches used within a standard operating microscope is the focus of this article. A new method that permits the automatic and near real-time estimation of 3D surfaces (at 1 Hz) under varying magnifications of the operating microscope is proposed. This method has been evaluated on a CAD phantom object and on full-length neurosurgery video sequences (∼1 h) acquired intraoperatively by the proposed stereovision system. To the best of our knowledge, this type of validation study on full-length brain tumor surgery videos has not been done before. The method for estimating the unknown magnification factor of the operating microscope achieves accuracy within 0.02 of the theoretical value on a CAD phantom and within 0.06 on 4 clinical videos of the entire brain tumor surgery. When compared to a laser range scanner, the proposed method for reconstructing 3D surfaces intraoperatively achieves root mean square errors (surface-to-surface distance) in the 0.28-0.81 mm range on the phantom object and in the 0.54-1.35 mm range on 4 clinical cases. The digitization accuracy of the presented stereovision methods indicate that the operating microscope can be used to deliver the persistent intraoperative input required by computational biomechanical models to update the patient's preoperative images and facilitate active surgical guidance.

Model-based correction of tissue compression for tracked ultrasound in soft tissue image-guided surgery.

Acquisition of ultrasound data negatively affects image registration accuracy during image-guided therapy because of tissue compression by the probe. We present a novel compression correction method that models sub-surface tissue displacement resulting from application of a tracked probe to the tissue surface. Patient landmarks are first used to register the probe pose to pre-operative imaging. The ultrasound probe geometry is used to provide boundary conditions to a biomechanical model of the tissue. The deformation field solution of the model is inverted to non-rigidly transform the ultrasound images to an estimation of the tissue geometry before compression. Experimental results with gel phantoms indicated that the proposed method reduced the tumor margin modified Hausdorff distance (MHD) from 5.0 ± 1.6 to 1.9 ± 0.6 mm, and reduced tumor centroid alignment error from 7.6 ± 2.6 to 2.0 ± 0.9 mm. The method was applied to a clinical case and reduced the tumor margin MHD error from 5.4 ± 0.1 to 2.6 ± 0.1 mm and the centroid alignment error from 7.2 ± 0.2 to 3.5 ± 0.4 mm.

Evaluation of conoscopic holography for estimating tumor resection cavities in model-based image-guided neurosurgery.

Surgical navigation relies on accurately mapping the intraoperative state of the patient to models derived from preoperative images. In image-guided neurosurgery, soft tissue deformations are common and have been shown to compromise the accuracy of guidance systems. In lieu of whole-brain intraoperative imaging, some advocate the use of intraoperatively acquired sparse data from laser-range scans, ultrasound imaging, or stereo reconstruction coupled with a computational model to drive subsurface deformations. Some authors have reported on compensating for brain sag, swelling, retraction, and the application of pharmaceuticals such as mannitol with these models. To date, strategies for modeling tissue resection have been limited. In this paper, we report our experiences with a novel digitization approach, called a conoprobe, to document tissue resection cavities and assess the impact of resection on model-based guidance systems. Specifically, the conoprobe was used to digitize the interior of the resection cavity during eight brain tumor resection surgeries and then compared against model prediction results of tumor locations. We should note that no effort was made to incorporate resection into the model but rather the objective was to determine if measurement was possible to study the impact on modeling tissue resection. In addition, the digitized resection cavity was compared with early postoperative MRI scans to determine whether these scans can further inform tissue resection. The results demonstrate benefit in model correction despite not having resection explicitly modeled. However, results also indicate the challenge that resection provides for model-correction approaches. With respect to the digitization technology, it is clear that the conoprobe provides important real-time data regarding resection and adds another dimension to our noncontact instrumentation framework for soft-tissue deformation compensation in guidance systems.

Automatic tracking of intraoperative brain surface displacements in brain tumor surgery.

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.

Near Real-Time Computer Assisted Surgery for Brain Shift Correction Using Biomechanical Models.

Conventional image-guided neurosurgery relies on preoperative images to provide surgical navigational information and visualization. However, these images are no longer accurate once the skull has been opened and brain shift occurs. To account for changes in the shape of the brain caused by mechanical (e.g., gravity-induced deformations) and physiological effects (e.g., hyperosmotic drug-induced shrinking, or edema-induced swelling), updated images of the brain must be provided to the neuronavigation system in a timely manner for practical use in the operating room. In this paper, a novel preoperative and intraoperative computational processing pipeline for near real-time brain shift correction in the operating room was developed to automate and simplify the processing steps. Preoperatively, a computer model of the patient's brain with a subsequent atlas of potential deformations due to surgery is generated from diagnostic image volumes. In the case of interim gross changes between diagnosis, and surgery when reimaging is necessary, our preoperative pipeline can be generated within one day of surgery. Intraoperatively, sparse data measuring the cortical brain surface is collected using an optically tracked portable laser range scanner. These data are then used to guide an inverse modeling framework whereby full volumetric brain deformations are reconstructed from precomputed atlas solutions to rapidly match intraoperative cortical surface shift measurements. Once complete, the volumetric displacement field is used to update, i.e., deform, preoperative brain images to their intraoperative shifted state. In this paper, five surgical cases were analyzed with respect to the computational pipeline and workflow timing. With respect to postcortical surface data acquisition, the approximate execution time was 4.5 min. The total update process which included positioning the scanner, data acquisition, inverse model processing, and image deforming was ~11-13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.

A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data.

In open abdominal image-guided liver surgery, sparse measurements of the organ surface can be taken intraoperatively via a laser-range scanning device or a tracked stylus with relatively little impact on surgical workflow. We propose a novel nonrigid registration method which uses sparse surface data to reconstruct a mapping between the preoperative CT volume and the intraoperative patient space. The mapping is generated using a tissue mechanics model subject to boundary conditions consistent with surgical supportive packing during liver resection therapy. Our approach iteratively chooses parameters which define these boundary conditions such that the deformed tissue model best fits the intraoperative surface data. Using two liver phantoms, we gathered a total of five deformation datasets with conditions comparable to open surgery. The proposed nonrigid method achieved a mean target registration error (TRE) of 3.3 mm for targets dispersed throughout the phantom volume, using a limited region of surface data to drive the nonrigid registration algorithm, while rigid registration resulted in a mean TRE of 9.5 mm. In addition, we studied the effect of surface data extent, the inclusion of subsurface data, the trade-offs of using a nonlinear tissue model, robustness to rigid misalignments, and the feasibility in five clinical datasets.

Comparison study of intraoperative surface acquisition methods for surgical navigation.

Soft-tissue image-guided interventions often require the digitization of organ surfaces for providing correspondence from medical images to the physical patient in the operating room. In this paper, the effect of several inexpensive surface acquisition techniques on target registration error and surface registration error (SRE) for soft tissue is investigated. A systematic approach is provided to compare image-to-physical registrations using three different methods of organ spatial digitization: 1) a tracked laser-range scanner (LRS), 2) a tracked pointer, and 3) a tracked conoscopic holography sensor (called a conoprobe). For each digitization method, surfaces of phantoms and biological tissues were acquired and registered to CT image volume counterparts. A comparison among these alignments demonstrated that registration errors were statistically smaller with the conoprobe than the tracked pointer and LRS (p<0.01). In all acquisitions, the conoprobe outperformed the LRS and tracked pointer: for example, the arithmetic means of the SRE over all data acquisitions with a porcine liver were 1.73 ± 0.77 mm, 3.25 ± 0.78 mm, and 4.44 ± 1.19 mm for the conoprobe, LRS, and tracked pointer, respectively. In a cadaveric kidney specimen, the arithmetic means of the SRE over all trials of the conoprobe and tracked pointer were 1.50 ± 0.50 mm and 3.51 ± 0.82 mm, respectively. Our results suggest that tissue displacements due to contact force and attempts to maintain contact with tissue, compromise registrations that are dependent on data acquired from a tracked surgical instrument and we provide an alternative method (tracked conoscopic holography) of digitizing surfaces for clinical usage. The tracked conoscopic holography device outperforms LRS acquisitions with respect to registration accuracy.

Automatic generation of boundary conditions using demons nonrigid image registration for use in 3-D modality-independent elastography.

Modality-independent elastography (MIE) is a method of elastography that reconstructs the elastic properties of tissue using images acquired under different loading conditions and a biomechanical model. Boundary conditions are a critical input to the algorithm and are often determined by time-consuming point correspondence methods requiring manual user input. This study presents a novel method of automatically generating boundary conditions by nonrigidly registering two image sets with a demons diffusion-based registration algorithm. The use of this method was successfully performed in silico using magnetic resonance and X-ray-computed tomography image data with known boundary conditions. These preliminary results produced boundary conditions with an accuracy of up to 80% compared to the known conditions. Demons-based boundary conditions were utilized within a 3-D MIE reconstruction to determine an elasticity contrast ratio between tumor and normal tissue. Two phantom experiments were then conducted to further test the accuracy of the demons boundary conditions and the MIE reconstruction arising from the use of these conditions. Preliminary results show a reasonable characterization of the material properties on this first attempt and a significant improvement in the automation level and viability of the method.

Tracking of vessels in intra-operative microscope video sequences for cortical displacement estimation.

This article presents a method designed to automatically track cortical vessels in intra-operative microscope video sequences. The main application of this method is the estimation of cortical displacement that occurs during tumor resection procedures. The method works in three steps. First, models of vessels selected in the first frame of the sequence are built. These models are then used to track vessels across frames in the video sequence. Finally, displacements estimated using the vessels are extrapolated to the entire image. The method has been tested retrospectively on images simulating large displacement, tumor resection, and partial occlusion by surgical instruments and on 21 video sequences comprising several thousand frames acquired from three patients. Qualitative results show that the method is accurate, robust to the appearance and disappearance of surgical instruments, and capable of dealing with large differences in images caused by resection. Quantitative results show a mean vessel tracking error (VTE) of 2.4 pixels (0.3 or 0.6 mm, depending on the spatial resolution of the images) and an average target registration error (TRE) of 3.3 pixels (0.4 or 0.8 mm).