[Show abstract][Hide abstract] ABSTRACT: 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&#x- 013;13 min. In addition, easily implemented hardware, software, and workflow processes were identified for improved performance in the near future.
IEEE Journal of Translational Engineering in Health and Medicine 04/2014; 2:1-13. DOI:10.1109/JTEHM.2014.2327628
[Show abstract][Hide abstract] ABSTRACT: Brain shift compromises the accuracy of neurosurgical image-guided
interventions if not corrected by either intraoperative imaging or
computational modeling. The latter requires intraoperative sparse
measurements for constraining and driving model-based compensation
strategies. Conoscopic holography, an interferometric technique that
measures the distance of a laser light illuminated surface point from a
fixed laser source, was recently proposed for non-contact surface data
acquisition in image-guided surgery and is used here for validation of
our modeling strategies. In this contribution, we use this inexpensive,
hand-held conoscopic holography device for intraoperative validation of
our computational modeling approach to correcting for brain shift. Laser
range scan, instrument swabbing, and conoscopic holography data sets
were collected from two patients undergoing brain tumor resection
therapy at Vanderbilt University Medical Center. The results of our
study indicate that conoscopic holography is a promising method for
surface acquisition since it requires no contact with delicate tissues
and can characterize the extents of structures within confined spaces.
We demonstrate that for two clinical cases, the acquired conoprobe
points align with our model-updated images better than the uncorrected
images lending further evidence that computational modeling approaches
improve the accuracy of image-guided surgical interventions in the
presence of soft tissue deformations.
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a new method designed to track operative microscope video images recorded during tumor resection neurosurgery. Two steps are involved in this method. The first uses feature vectors constructed from color information of video images and shape information of selected vessels to find homologous points in consecutive frames. The second uses smoothing thin-plate splines (TPS) to interpolate the transformation computed with the vessels over the entire image. This approach only requires several pairs of starting and ending points selected on segments of vessels in the first frame of a video sequence. Then, the proposed method tracks the identified vessels automatically, rapidly, and robustly, even when surgical instruments obscure parts of the image frames.
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on; 08/2009
[Show abstract][Hide abstract] ABSTRACT: In this article a comprehensive set of registration methods is utilized to provide image-to-physical space registration for image-guided neurosurgery in a clinical study. Central to all methods is the use of textured point clouds as provided by laser range scanning technology. The objective is to perform a systematic comparison of registration methods that include both extracranial (skin marker point-based registration (PBR), and face-based surface registration) and intracranial methods (feature PBR, cortical vessel-contour registration, a combined geometry/intensity surface registration method, and a constrained form of that method to improve robustness). The platform facilitates the selection of discrete soft-tissue landmarks that appear on the patient's intraoperative cortical surface and the preoperative gadolinium-enhanced magnetic resonance (MR) image volume, i.e., true corresponding novel targets. In an 11 patient study, data were taken to allow statistical comparison among registration methods within the context of registration error. The results indicate that intraoperative face-based surface registration is statistically equivalent to traditional skin marker registration. The four intracranial registration methods were investigated and the results demonstrated a target registration error of 1.6 +/- 0.5 mm, 1.7 +/- 0.5 mm, 3.9 +/- 3.4 mm, and 2.0 +/- 0.9 mm, for feature PBR, cortical vessel-contour registration, unconstrained geometric/intensity registration, and constrained geometric/intensity registration, respectively. When analyzing the results on a per case basis, the constrained geometric/intensity registration performed best, followed by feature PBR, and finally cortical vessel-contour registration. Interestingly, the best target registration errors are similar to targeting errors reported using bone-implanted markers within the context of rigid targets. The experience in this study as with others is that brain shift can compromise extracranial registration methods from the earliest stages. Based on the results reported here, organ-based approaches to registration would improve this, especially for shallow lesions.
Medical Physics 05/2008; 35(4):1593-605. DOI:10.1118/1.2870216 · 2.64 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, preliminary results from an image-to-physical space registration platform are presented. The current platform employs traditional and novel methods of registration which use a variety of data sources to include: traditional synthetic skin-fiducial point-based registration, surface registration based on facial contours, brain feature point-based registration, brain vessel-to-vessel registration, and a more comprehensive cortical surface registration method that utilizes both geometric and intensity information from both the image volume and physical patient. The intraoperative face and cortical surfaces were digitized using a laser range scanner (LRS) capable of producing highly resolved textured point clouds. In two in vivo cases, a series of registrations were performed using these techniques and compared within the context of a true target error. One of the advantages of using a textured point cloud data stream is that true targets among the physical cortical surface and the preoperative image volume can be identified and used to assess image-to-physical registration methods. The results suggest that iterative closest point (ICP) method for intraoperative face surface registration is equivalent to point-based registration (PBR) method of skin fiducial markers. With regard to the initial image and physical space registration, for patient 1, mean target registration error (TRE) were 3.1±0.4 mm and 3.6 ±0.9 mm for face ICP and skin fiducial PBR, respectively. For patient 2, the mean TRE were 5.7 ±1.3 mm, and 6.6 ±0.9 mm for face ICP and skin fiducial PBR, respectively. With regard to intraoperative cortical surface registration, SurfaceMI outperformed feature based PBR and vessel ICP with 1.7±1.8 mm for patient 1. For patient 2, the best result was achieved by using vessel ICP with 1.9±0.5 mm.
Proceedings of SPIE - The International Society for Optical Engineering 03/2007; DOI:10.1117/12.711496 · 0.20 Impact Factor