SignificanceALA-PpIX and second-window indocyanine green (ICG) have been studied widely for guiding the resection of high-grade gliomas. These agents have different mechanisms of action and uptake characteristics, which can affect their performance as surgical guidance agents. Elucidating these differences in animal models that approach the size and anatomy of the human brain would help guide the use of these agents. Herein, we report on the use of a new pig glioma model and fluorescence cryotomography to evaluate the 3D distributions of both agents throughout the whole brain.AimWe aim to assess and compare the 3D spatial distributions of ALA-PpIX and second-window ICG in a glioma-bearing pig brain using fluorescence cryotomography.ApproachA glioma was induced in the brain of a transgenic Oncopig via adeno-associated virus delivery of Cre-recombinase plasmids. After tumor induction, the pro-drug 5-ALA and ICG were administered to the animal 3 and 24 h prior to brain harvest, respectively. The harvested brain was imaged using fluorescence cryotomography. The fluorescence distributions of both agents were evaluated in 3D in the whole brain using various spatial distribution and contrast performance metrics.ResultsSignificant differences in the spatial distributions of both agents were observed. Indocyanine green accumulated within the tumor core, whereas ALA-PpIX appeared more toward the tumor periphery. Both ALA-PpIX and second-window ICG provided elevated tumor-to-background contrast (13 and 23, respectively).ConclusionsThis study is the first to demonstrate the use of a new glioma model and large-specimen fluorescence cryotomography to evaluate and compare imaging agent distribution at high resolution in 3D.
In image-guided neurosurgery, preoperative magnetic resonance (pMR) images are rigidly registered with the patient’s head in the operating room. Image-guided systems incorporate this spatial information to provide real-time information on where surgical instruments are located with respect to preoperative imaging. The accuracy of these systems rely on the rigid relationship between the patient’s brain and the preoperative scan, which typically does not hold true due to intraoperative brain shift. To account for this brain shift, we previously developed an image-guidance updating framework that incorporates brain shift information acquired from registering intraoperative stereovision (iSV) surface with the pMR surface to create an updated magnetic resonance image (uMR). To register the iSV surface and the pMR surface, the two surfaces must have some matching features that can be used for registration. However, for some cases, the matching features could fall outside of the segmented brain volume causing a lack of matching features for registration between iSV and pMR surfaces. To capture features falling outside of the brain volume, we have developed a method to improve feature extraction, which involves performing a selective dilation in the region of the stereovision surface. The goal of this method is to capture features that fall outside of the brain volume without capturing too much noise. With further testing, this method has potential in supplementing brain segmentation to improve image registration between iSV and pMR surfaces within the image-guidance updating framework.
Introduction In image-guided open cranial surgeries, brain deformation may compromise the accuracy of image guidance immediately following the opening of the dura. A biomechanical model has been developed to update pre-operative MR images to match intraoperative stereovision (iSV), and maintain the accuracy of image guidance. Current methods necessitate manual segmentation of the cortical surface from iSV, a process that demands expertise and prolongs computational time . Methods In this study, we adopted the Fast Segment Anything Model (FastSAM), a newly developed deep learning model that automatically can segment the cortical surface from iSV after dural opening without customized training. We evaluated its performance against manual segmentation as well as a U-Net model. In one patient case, FastSAM was applied to segment the cortical surface with an automatic box prompt, and the segmentation was used for image updating. We compared the three cortical surface segmentation methods in terms of segmentation accuracy (Dice Similarity Coefficient; DSC) and image updating accuracy (target registration errors; TRE). Results All three segmentation methods demonstrated high DSC (>0.95). FastSAM and manual segmentation produced similar performance in terms of image updating efficiency and TRE (~2.2 mm). Conclusion In summary, the performance of FastSAM was consistent with manual segmentation in terms of segmentation accuracy and image updating accuracy. The results suggest FastSAM can be employed in the image updating process to replace manual segmentation to improve efficiency and reduce user dependency.
Tracked intraoperative ultrasound (iUS) is growing in use. Accurate spatial calibration is essential to enable iUS navigation. Utilizing sterilizable probes introduces new challenges that can be solved by time-of-surgery calibration that is robust, efficient and user independent performed within the sterile field. This study demonstrates a smart line detection scheme to perform calibration based on video acquisition data and investigates the effect of pose variation on the accuracy of a plane-based calibration. A user-independent US video is collected of a calibration phantom and a smart line detection and tracking filter applied to the video-tracking data pairs to remove poor calibration candidates. A localized point target phantom is imaged to provide a TRE assessment of the calibration. The tracking data is decoupled into 6 degrees of freedom and these ranges iteratively reduced to study the effect on the spatial calibration accuracy in order to indicate the sufficient amount of pose variation required during video acquisition to maintain high TRE accuracy. This work facilitates a larger development toward user-independent, video based iUS calibration at the time of surgery.
Pre-operative MRI with gadolinium-based contrast agents (Gd-MRI) is a central feature in surgical planning and intra-surgical navigation of glioma, yet brain movement during the surgical procedure can degrade the accuracy of these pre-operative images. Fluorescence guided neurosurgery is a technique which can complement MRI guidance by providing direct visualization of the tumor during surgery, and several agents either used routinely or under clinical development have shown effective tumor discrimination and impact on surgical outcomes. We have built a multi-spectral kinetic imaging system to acquire behavior of fluorophores overtime in animal models. Here, we exhibit this fluorescence kinetic imaging system and report its performance with tissue-simulating phantoms with multiple fluorophores. Also reported is our first experience with multiple fluorescent contrast agents in a novel oncopig model.
Fluorescence cryo-imaging is a high-resolution optical imaging technique that produces 3-D whole-body biodistributions of fluorescent molecules within an animal specimen. To accomplish this, animal specimens are administered a fluorescent molecule or reporter and are frozen to be autonomously sectioned and imaged at a temperature of -20°C or below. Thus, to apply this technique effectively, administered fluorescent molecules should be relatively invariant to low temperature conditions for cryo-imaging and ideally the fluorescence intensity should be stable and consistent in both physiological and cryo-imaging conditions. Herein, we assessed the mean fluorescence intensity of 11 fluorescent contrast agents as they are frozen in a tissue-simulating phantom experiment and show an example of a tested fluorescent contrast agent in a cryo-imaged whole pig brain. Most fluorescent contrast agents were stable within ~25% except for FITC and PEGylated FITC derivatives, which showed a dramatic decrease in fluorescence intensity when frozen.
The success of deep brain stimulation (DBS) is dependent on the accurate placement of electrodes in the operating room (OR). However, due to intraoperative brain shift, the accuracy of pre-operative scans and pre-surgical planning are often degraded. To compensate for brain shift, we created a finite element bio-mechanical brain model that updates preoperative images by assimilating intraoperative sparse data from the brain surface or deep brain targets. Additionally, we constructed an artificial neural network (ANN) that leveraged a large number of ventricle nodal displacements to estimate brain shift. The machine learning method showed potential in incorporating ventricle sparse data to accurately compute shift at the brain surface. Thus, in this paper, we propose using this machine learning model to estimate brain atrophy at deep brain targets such as the anterior commissure (AC) and the posterior commissure (PC). The ANN consists of an input layer with nine hand-engineered features, such as the distance between the deep brain target and the ventricle node, two hidden layers and an output layer. This model was trained using eight patient cases and tested on two patient cases.
In open cranial procedures, the accuracy of image guidance using preoperative MR (pMR) images can be degraded by intraoperative brain deformation. Intraoperative stereovision (iSV) has been used to acquire 3D surface profile of the exposed cortex at different surgical stages, and surface displacements can be extracted to drive a biomechanical model as sparse data to provide updated MR (uMR) images that match the surgical scene. In previous studies, we have employed an Optical Flow (OF) based registration technique to register iSV surfaces acquired from different surgical stages and estimate cortical surface shift throughout surgery. The technique was efficient and accurate but required manually selected Regions of Interest (ROI) in each image after resection began. In this study, we present a registration technique based on Scale Invariant Feature Transform (SIFT) algorithm and illustrate the methods using an example patient case. Stereovision images of the cortical surface were acquired and reconstructed at different time points during surgery. Both SIFT and OF based registration techniques were used to estimate cortical shift, and extracted displacements were compared against ground truth data. Results show that the overall errors of SIFT and OF based techniques were 0.65±0.53 mm and 2.18±1.35 mm in magnitude, respectively, on the intact cortical surface. The OF-based technique generated inaccurate sparse data near the resection cavity region, whereas SIFT-based technique only generated accurate sparse data. The computational efficiency was ⪅0.5 s and ⪆20 s for SIFT and OF based techniques, respectively. Thus, the SIFT-based registration technique shows promise for OR applications.
Registration of preoperative or intraoperative imaging is necessary to facilitate surgical navigation in spine surgery. After image acquisition, intervertebral motion and spine pose changes can occur during surgery from instrumentation, decompression, physician manipulation or correction. This causes deviations from the reference imaging reducing the navigation accuracy. To evaluate the ability to use the registration between stereovision surfaces in order to account for this intraoperative spine motion through a simulation study. Co-registered CT and stereovision surface data were obtained of a swine cadaver’s exposed lumbar spine in the prone position. Data was segmented and labeled by vertebral level. A simulation of biomechanically bounded motion was applied to each vertebral level to move the prone spine to a new position. A reduced surface data set was then registered level-wise back to the prone spines original position. The average surface to surface distance was recorded between simulated and prone positions. Localized targets on these surfaces were used for a calculation of target registration error. Target registration error increases with distance between surfaces. Movement exceeding 2.43 cm between stereovision acquisitions exceeds registration accuracy of 2mm. Lateral bending of the spine contributes most to this effect compared to axial rotation and flexion-extension. In conclusion, the viability of using stereovision-to-stereovision registration to account for interoperative motion of the spine is shown through this simulation. It is suggested the distance of spine movement between corresponding points does not surpass 2.43 cm between stereovision acquisitions.
Miniature Screws, often used for fiducials, are currently localized on DICOM images manually. This time-consuming process can add tens of minutes to the computational process for registration, or error analysis. Through a series of morphological operations, this localization task can be completed in a time much less than a second when performed on a standard laptop. Two image sets were analyzed. The first data set consisted of six intraoperative CT (iCT) scans of the lumbar spine of both cadaver and live porcine samples. This dataset includes not only implanted mini-screws, but other metal instrumentation. The second dataset consists of 6 semi-rigidly deformed CT (uCT) scans of the lumbar spine of the same animals. This dataset has been intensity down sampled from 16 bits to eight bits as a pre-processing step. Also, due to other deformation steps, other artifacts are apparent. Both datasets show at least 18 mini-screws which were rigidly implanted in the lumbar vertebrae. Each vertebra has at least three mini-screws implanted. These images were processed as follows: projection image forming via maximum row values, thresholding, opening, non-circular regions were removed, and circular regions were eroded. Leaving voxel locations of the center of each mini-screw. The aforementioned steps can be completed with a mean computational efficiency of .0365 seconds. Which is an unobtainable time for manual localization. Even by the most skilled. The true positive rates of the iCT and uCT datasets were 96.
The success of deep brain stimulation (DBS) depends upon the accurate surgical placement of electrodes in the OR. However, the accuracy of pre-operative scans is often degraded by intraoperative brain shift. To compensate for brain shift, we developed a biomechanical brain model that updates preoperative images by assimilating intraoperative sparse data from either the brain surface or deep brain structures. In addition to constraining the finite element model, surface sparse data estimates model boundary conditions such as the level of cerebrospinal fluid (CSF). As a potentially cost-effective and safe alternative to intraoperative imaging techniques, a machine learning method was proposed to estimate surface brain atrophy by leveraging a large number of ventricle nodal displacements. Specifically, we constructed an artificial neural network (ANN) that consisted of an input layer with 9 hand-engineered features such as the surface-to-ventricle nodal distance. The multilayer perceptron was trained using 132,000 nodal pairs from eleven patient cases and tested using 48,000 from four cases. Results showed that in a testing case, the ANN estimated an overall surface displacement of 8.79 ± 0.765 mm to the left and 8.26 ± .455 mm to the right compared to the ground truth (10.36 ± 1.33 mm left and 7.40 ± 1.40 mm right). The average prediction error of all four testing cases was less than 2 mm. With further development and evaluation, the proposed method has the potential of supplementing the biomechanical brain model with surface sparse data and estimating boundary parameters.
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