Automatic scanning of large tissue areas in neurosurgery using optical coherence tomography

Microscope integrated MHz optical coherence tomography system for neurosurgery: development and clinical in-vivo imaging

Neurosurgical interventions on the brain are impeded by the requirement to keep damages to healthy tissue at a minimum. A new contrast channel enhancing the visual separation of malign tissue should be created. A commercially available surgical microscope was modified with adaptation optics adapting the MHz speed optical coherence tomography (OCT) imaging system developed in our group. This required the design of a scanner optics and beam delivery system overcoming constraints posed by the mechanical and optical parameters of the microscope. High quality volumetric OCT C-scans with dense sample spacing can be acquired in-vivo as part of surgical procedures within seconds and are immediately available for post-processing.

Label-free optical imaging for brain cancer assessment

Advances in label-free optical imaging offer a promising avenue for brain cancer assessment, providing high-resolution, real-time insights without the need for radiation or exogeneous agents. These cost-effective and intricately detailed techniques overcome the limitations inherent in magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans by offering superior resolution and more readily accessible imaging options. This comprehensive review explores a variety of such methods, including photoacoustic imaging (PAI), optical coherence tomography (OCT), Raman imaging, and IR microscopy. It focuses on their roles in the detection, diagnosis, and management of brain tumors. By highlighting recent advances in these imaging techniques, the review aims to underscore the importance of label-free optical imaging in enhancing early detection and refining therapeutic strategies for brain cancer.

Large area robotically assisted optical coherence tomography (LARA-OCT)

We demonstrate large-area robotically assisted optical coherence tomography (LARA-OCT), utilizing a seven-degree-of-freedom robotic arm in conjunction with a 3.3 MHz swept-source OCT to raster scan samples of arbitrary shape. By combining multiple fields of view (FOV), LARA-OCT can probe a much larger area than conventional OCT. Also, nonplanar and curved surfaces like skin on arms and legs can be probed. The lenses in the LARA-OCT scanner with their normal FOV can have fewer aberrations and less complex optics compared to a single wide field design. This may be especially critical for high resolution scans. We directly use our fast MHz-OCT for tracking and stitching, making additional machine vision systems like cameras, positioning, tracking or navigation devices obsolete. This also eliminates the need for complex coordinate system registration between OCT and the machine vision system. We implemented a real time probe-to-surface control that maintains the probe alignment orthogonal to the sample by only using surface information from the OCT images. We present OCT data sets with volume sizes of 140 × 170 × 20 mm3, captured in 2.5 minutes.

Automatic and real-time tissue sensing for autonomous intestinal anastomosis using hybrid MLP-DC-CNN classifier-based optical coherence tomography

Anastomosis is a common and critical part of reconstructive procedures within gastrointestinal, urologic, and gynecologic surgery. The use of autonomous surgical robots such as the smart tissue autonomous robot (STAR) system demonstrates an improved efficiency and consistency of the laparoscopic small bowel anastomosis over the current da Vinci surgical system. However, the STAR workflow requires auxiliary manual monitoring during the suturing procedure to avoid missed or wrong stitches. To eliminate this monitoring task from the operators, we integrated an optical coherence tomography (OCT) fiber sensor with the suture tool and developed an automatic tissue classification algorithm for detecting missed or wrong stitches in real time. The classification results were updated and sent to the control loop of STAR robot in real time. The suture tool was guided to approach the object by a dual-camera system. If the tissue inside the tool jaw was inconsistent with the desired suture pattern, a warning message would be generated. The proposed hybrid multilayer perceptron dual-channel convolutional neural network (MLP-DC-CNN) classification platform can automatically classify eight different abdominal tissue types that require different suture strategies for anastomosis. In MLP, numerous handcrafted features (∼1955) were utilized including optical properties and morphological features of one-dimensional (1D) OCT A-line signals. In DC-CNN, intensity-based features and depth-resolved tissues' attenuation coefficients were fully exploited. A decision fusion technique was applied to leverage the information collected from both classifiers to further increase the accuracy. The algorithm was evaluated on 69,773 testing A-line data. The results showed that our model can classify the 1D OCT signals of small bowels in real time with an accuracy of 90.06%, a precision of 88.34%, and a sensitivity of 87.29%, respectively. The refresh rate of the displayed A-line signals was set as 300 Hz, the maximum sensing depth of the fiber was 3.6 mm, and the running time of the image processing algorithm was ∼1.56 s for 1,024 A-lines. The proposed fully automated tissue sensing model outperformed the single classifier of CNN, MLP, or SVM with optimized architectures, showing the complementarity of different feature sets and network architectures in classifying intestinal OCT A-line signals. It can potentially reduce the manual involvement of robotic laparoscopic surgery, which is a crucial step towards a fully autonomous STAR system.

Robotic-OCT guided inspection and microsurgery of monolithic storage devices

Data recovery from monolithic storage devices (MSDs) is in high demand for legal or business purposes. However, the conventional data recovery methods are destructive, complicated, and time-consuming. We develop a robotic-arm-assisted optical coherence tomography (robotic-OCT) for non-destructive inspection of MSDs, offering ~7 μm lateral resolution, ~4 μm axial resolution and an adjustable field-of-view to accommodate various MSD sizes. Using a continuous scanning strategy, robotic-OCT achieves automated volumetric imaging of a micro-SD card in ~37 seconds, significantly faster than the traditional stop-and-stare scanning that typically takes tens of minutes. We also demonstrate the robotic-OCT-guided laser ablation as a microsurgical tool for targeted area removal with precision of ±10 μm and accuracy of ~50 μm, eliminating the need to remove the entire insulating layer and operator intervention, thus greatly improving the data recovery efficiency. This work has diverse potential applications in digital forensics, failure analysis, materials testing, and quality control.

Label-free intraoperative nerve detection and visualization using ratiometric diffuse reflectance spectroscopy

Iatrogenic nerve injuries contribute significantly to postoperative morbidity across various surgical disciplines and occur in approximately 500,000 cases annually in the US alone. Currently, there are no clinically adopted means to intraoperatively visualize nerves beyond the surgeon's visual assessment. Here, we report a label-free method for nerve detection using diffuse reflectance spectroscopy (DRS). Starting with an in vivo rat model, fiber- and imaging-based DRS independently identified similar wavelengths that provided optimal contrast for nerve identification with an accuracy of 92%. Optical property measurements of rat and human cadaver tissues verify that the source of contrast between nerve and surrounding tissues is largely due to higher scattering in nerve and differences in oxygenated hemoglobin content. Clinical feasibility was demonstrated in patients undergoing thyroidectomies using both probe-based and imaging-based approaches where the nerve were identified with 91% accuracy. Based on our preliminary results, DRS has the potential to both provide surgeons with a label-free, intraoperative means of nerve visualization and reduce the incidence of iatrogenic nerve injuries along with its detrimental complications.

A systematic survey of advances in retinal imaging modalities for Alzheimer's disease diagnosis

Recent advances in retinal imaging pathophysiology have shown a new function for biomarkers in Alzheimer's disease diagnosis and prognosis. The significant improvements in Optical coherence tomography (OCT) retinal imaging have led to significant clinical translation, particularly in Alzheimer's disease detection. This systematic review will provide a comprehensive overview of retinal imaging in clinical applications, with a special focus on biomarker analysis for use in Alzheimer's disease detection. Articles on OCT retinal imaging in Alzheimer's disease diagnosis were identified in PubMed, Google Scholar, IEEE Xplore, and Research Gate databases until March 2021. Those studies using simultaneous retinal imaging acquisition were chosen, while those using sequential techniques were rejected. "Alzheimer's disease" and "Dementia" were searched alone and in combination with "OCT" and "retinal imaging". Approximately 1000 publications were searched, and after deleting duplicate articles, 145 relevant studies focused on the diagnosis of Alzheimer's disease utilizing retinal imaging were chosen for study. OCT has recently been demonstrated to be a valuable technique in clinical practice as according to this survey, 57% of the researchers employed optical coherence tomography, 19% used ocular fundus imaging, 13% used scanning laser ophthalmoscopy, and 11% have used multimodal imaging to diagnose Alzheimer disease. Retinal imaging has become an important diagnostic technique for Alzheimer's disease. Given the scarcity of available literature, it is clear that future prospective trials involving larger and more homogeneous groups are necessary, and the work can be expanded by evaluating its significance utilizing a machine-learning platform rather than simply using statistical methodologies.

OCT-Guided Surgery for Gliomas: Current Concept and Future Perspectives

Optical coherence tomography (OCT) has been recently suggested as a promising method to obtain in vivo and real-time high-resolution images of tissue structure in brain tumor surgery. This review focuses on the basics of OCT imaging, types of OCT images and currently suggested OCT scanner devices and the results of their application in neurosurgery. OCT can assist in achieving intraoperative precision identification of tumor infiltration within surrounding brain parenchyma by using qualitative or quantitative OCT image analysis of scanned tissue. OCT is able to identify tumorous tissue and blood vessels detection during stereotactic biopsy procedures. The combination of OCT with traditional imaging such as MRI, ultrasound and 5-ALA fluorescence has the potential to increase the safety and accuracy of the resection. OCT can improve the extent of resection by offering the direct visualization of tumor with cellular resolution when using microscopic OCT contact probes. The theranostic implementation of OCT as a part of intelligent optical diagnosis and automated lesion localization and ablation could achieve high precision, automation and intelligence in brain tumor surgery. We present this review for the increase of knowledge and formation of critical opinion in the field of OCT implementation in brain tumor surgery.

Increased crystalline lens coverage in optical coherence tomography with oblique scanning and volume stitching

A three-dimensional optical coherence tomography (OCT) crystalline lens imaging method based on oblique scanning and image stitching is presented. The method was designed to increase OCT imaging volume of crystalline lens in vivo. A long-range swept-source (SS)-OCT imaging system, which can measure the entire anterior segment of eye in a single acquisition, is used to acquire one central volume and 4 extra volumes with different angles between optical axis of OCT instrument and the pupillary axis. The volumes are then stitched automatically by developed software. To show its effectiveness and verify its validity, we scanned the subjects before and after pupil dilation drops and compared the experimental results. By determining the number of voxels representing the signal from the crystalline lens in 3-D OCT images, our method can provide around 17% additional volumetric lens coverage compared with a regular imaging procedure. The proposed approach could be used clinically in early diagnosis of cortical cataract. Wider field of view offered by this method may facilitate more accurate lens biometry in its peripheral zones, which potentially contributes to understanding of lens shape modifications of the accommodating eye.

Longitudinal in-vivo OCM imaging of glioblastoma development in the mouse brain

We present in-vivo imaging of the mouse brain using custom made Gaussian beam optical coherence microscopy (OCM) with 800nm wavelength. We applied new instrumentation to longitudinal imaging of the glioblastoma (GBM) tumor microvasculature in the mouse brain. We have introduced new morphometric biomarkers that enable quantitative analysis of the development of GBM. We confirmed quantitatively an intensive angiogenesis in the tumor area between 3 and 14 days after GBM cells injection confirmed by considerably increased of morphometric parameters. Moreover, the OCM setup revealed heterogeneity and abnormality of newly formed vessels.

Spatio-temporal deep learning methods for motion estimation using 4D OCT image data

PURPOSE

Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature-based methods.

METHODS

We investigate whether using a temporal stream of OCT image volumes can improve deep learning-based motion estimation performance. For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach. Also, we propose a temporal regularization strategy at the model output.

RESULTS

Using a tissue dataset without additional markers, our deep learning methods using 4D data outperform previous approaches. The best performing 4D architecture achieves an correlation coefficient (aCC) of 98.58% compared to 85.0% of a previous 3D deep learning method. Also, our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06%. In particular, our 4D method works well for larger motion and is robust toward image rotations and motion distortions.

CONCLUSIONS

We propose 4D spatio-temporal deep learning for OCT-based motion estimation. On a tissue dataset, we find that using 4D information for the model input improves performance while maintaining reasonable inference times. Our regularization strategy demonstrates that additional temporal information is also beneficial at the model output.

Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography

Optical tracking systems are widely used, for example, to navigate medical interventions. Typically, they require the presence of known geometrical structures, the placement of artificial markers, or a prominent texture on the target’s surface. In this work, we propose a 6D tracking approach employing volumetric optical coherence tomography (OCT) images. OCT has a micrometer-scale resolution and employs near-infrared light to penetrate few millimeters into, for example, tissue. Thereby, it provides sub-surface information which we use to track arbitrary targets, even with poorly structured surfaces, without requiring markers. Our proposed system can shift the OCT’s field-of-view in space and uses an adaptive correlation filter to estimate the motion at multiple locations on the target. This allows one to estimate the target’s position and orientation. We show that our approach is able to track translational motion with root-mean-squared errors below 0.25 mm and in-plane rotations with errors below 0.3°. For out-of-plane rotations, our prototypical system can achieve errors around 0.6°.

Towards an Optical Biopsy during Visceral Surgical Interventions

Background

Cancer will replace cardiovascular diseases as the most frequent cause of death. Therefore, the goals of cancer treatment are prevention strategies and early detection by cancer screening and ideal stage therapy. From an oncological point of view, complete tumor resection is a significant prognostic factor. Optical coherence tomography (OCT) and confocal laser microscopy (CLM) are two techniques that have the potential to complement intraoperative frozen section analysis as in vivo and real-time optical biopsies.

Summary

In this review we present both procedures and review the progress of evaluation for intraoperative application in visceral surgery. For visceral surgery, there are promising studies evaluating OCT and CLM; however, application during routine visceral surgical interventions is still lacking.

Key Message

OCT and CLM are not competing but complementary approaches of tissue analysis to intraoperative frozen section analysis. Although intraoperative application of OCT and CLM is at an early stage, they are two promising techniques of intraoperative in vivo and real-time tissue examination. Additionally, deep learning strategies provide a significant supplement for automated tissue detection.

Speckle modulation enables high-resolution wide-field human brain tumor margin detection and in vivo murine neuroimaging

Current in vivo neuroimaging techniques provide limited field of view or spatial resolution and often require exogenous contrast. These limitations prohibit detailed structural imaging across wide fields of view and hinder intraoperative tumor margin detection. Here we present a novel neuroimaging technique, speckle-modulating optical coherence tomography (SM-OCT), which allows us to image the brains of live mice and ex vivo human samples with unprecedented resolution and wide field of view using only endogenous contrast. The increased visibility provided by speckle elimination reveals white matter fascicles and cortical layer architecture in brains of live mice. To our knowledge, the data reported herein represents the highest resolution imaging of murine white matter structure achieved in vivo across a wide field of view of several millimeters. When applied to an orthotopic murine glioblastoma xenograft model, SM-OCT readily identifies brain tumor margins with resolution of approximately 10 μm. SM-OCT of ex vivo human temporal lobe tissue reveals fine structures including cortical layers and myelinated axons. Finally, when applied to an ex vivo sample of a low-grade glioma resection margin, SM-OCT is able to resolve the brain tumor margin. Based on these findings, SM-OCT represents a novel approach for intraoperative tumor margin detection and in vivo neuroimaging.

Revealing brain pathologies with multimodal visible light optical coherence microscopy and fluorescence imaging

We present a multimodal visible light optical coherence microscopy (OCM) and fluorescence imaging (FI) setup. Specification and phantom measurements were performed to characterize the system. Two applications in neuroimaging were investigated. First, curcumin-stained brain slices of a mouse model of Alzheimer's disease were examined. Amyloid-beta plaques were identified based on the fluorescence of curcumin, and coregistered morphological images of the brain tissue were provided by the OCM channel. Second, human brain tumor biopsies retrieved intraoperatively were imaged prior to conventional neuropathologic work-up. OCM revealed the three-dimensional structure of the brain parenchyma, and FI added the tumor tissue-specific contrast. Attenuation coefficients computed from the OCM data and the florescence intensity values were analyzed and showed a statistically significant difference for 5-aminolevulinic acid (5-ALA)-positive and -negative brain tissues. OCM findings correlated well with malignant hot spots within brain tumor biopsies upon histopathology. The combination of OCM and FI seems to be a promising optical imaging modality providing complementary contrast for applications in the field of neuroimaging.

Tactile sensor-based real-time clustering for tissue differentiation

PURPOSE

Reliable intraoperative delineation of tumor from healthy brain tissue is essentially based on the neurosurgeon's visual aspect and tactile impression of the considered tissue, which is-due to inherent low brain consistency contrast-a challenging task. Development of an intelligent artificial intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when-as for neuroendoscopy-tactile perception will be damped or-as for surgical robotic applications-will not be a priori existent. Here, we present the enhancements and the evaluation of a tactile sensor based on the use of a piezoelectric tactile sensor.

METHODS

A robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E₁ and E₂ for the elastic moduli and η for the viscosity coefficient have been obtained allowing tissue classification. Data analysis was performed by a multivariate cluster algorithm.

RESULTS

Cluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.

CONCLUSIONS

Bimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising.

In Vivo Microscopy in Neurosurgical Oncology

Intraoperative neurosurgical histopathologic diagnoses rely on evaluation of rapid tissue preparations such as frozen sections and smears with conventional light microscopy. Although useful, these techniques are time consuming and therefore cannot provide real-time intraoperative feedback. In vivo molecular imaging techniques are emerging as novel methods for generating real-time diagnostic histopathologic images of tumors and their surrounding tissues. These imaging techniques rely on contrast generated by exogenous fluorescent dyes, autofluorescence of endogenous molecules, fluorescence decay of excited molecules, or light scattering. Large molecular imaging instruments are being miniaturized for clinical in vivo use. This review discusses pertinent imaging systems that have been developed for neurosurgical use and imaging techniques currently under development for neurosurgical molecular imaging.

A deep learning approach for pose estimation from volumetric OCT data

Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework. For this purpose, we design a new 3D convolutional neural network (CNN) architecture to directly predict the 6D pose of a small marker geometry from OCT volumes. We use a hexapod robot to automatically acquire labeled data points which we use to train 3D CNN architectures for multi-output regression. We use this setup to provide an in-depth analysis on deep learning-based pose estimation from volumes. Specifically, we demonstrate that exploiting volume information for pose estimation yields higher accuracy than relying on 2D representations with depth information. Supporting this observation, we provide quantitative and qualitative results that 3D CNNs effectively exploit the depth structure of marker objects. Regarding the deep learning aspect, we present efficient design principles for 3D CNNs, making use of insights from the 2D deep learning community. In particular, we present Inception3D as a new architecture which performs best for our application. We show that our deep learning approach reaches errors at our ground-truth label's resolution. We achieve a mean average error of 14.89 ± 9.3 µm and 0.096 ± 0.072° for position and orientation learning, respectively.

Optical coherence tomography for precision brain imaging, neurosurgical guidance and minimally invasive theranostics

This review focuses on optical coherence tomography (OCT)-based neurosurgical application for imaging and treatment of brain tumors. OCT has emerged as one of the most innovative and successful translational biomedical-diagnostic techniques. It is a useful imaging tool for noninvasive, in vivo, in situ and real-time imaging in soft biological tissues, such as brain tumor imaging. OCT can detect the structure of biological tissue in a micrometer scale, and functional OCT has some clinical researches and applications, such as nerve fiber tracts and neurovascular imaging. OCT is able to identify tumor margins, and it gives intraoperative precision identification and resection guidance. OCT-based theranostics is introduced into preclinical neurosurgical resection, such as the integration of OCT and laser ablation. We discuss the challenges and opportunities of OCT-based system in the field of combination of intraoperative structural and functional imaging, neurosurgical guidance and minimally invasive theranostics. We point out that OCT and laser ablation-based theranostics can give more precision and intelligence for intraoperative diagnosis and therapeutics in clinical applications. The theranostics can precisely locate, or specifically target cancerous tissues, and then as much as possiblly eliminate them.

Review of optical coherence tomography in oncology

The application of optical coherence tomography (OCT) in the field of oncology has been prospering over the past decade. OCT imaging has been used to image a broad spectrum of malignancies, including those arising in the breast, brain, bladder, the gastrointestinal, respiratory, and reproductive tracts, the skin, and oral cavity, among others. OCT imaging has initially been applied for guiding biopsies, for intraoperatively evaluating tumor margins and lymph nodes, and for the early detection of small lesions that would often not be visible on gross examination, tasks that align well with the clinical emphasis on early detection and intervention. Recently, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This paper reviews the evolution of OCT technologies for the clinical application of OCT in surgical and noninvasive interventional oncology procedures and concludes with a discussion of the future directions for OCT technologies, with particular emphasis on their applications in oncology.

Optical Coherence Tomography: Basic Concepts and Applications in Neuroscience Research

Optical coherence tomography is a micrometer-scale imaging modality that permits label-free, cross-sectional imaging of biological tissue microstructure using tissue backscattering properties. After its invention in the 1990s, OCT is now being widely used in several branches of neuroscience as well as other fields of biomedical science. This review study reports an overview of OCT's applications in several branches or subbranches of neuroscience such as neuroimaging, neurology, neurosurgery, neuropathology, and neuroembryology. This study has briefly summarized the recent applications of OCT in neuroscience research, including a comparison, and provides a discussion of the remaining challenges and opportunities in addition to future directions. The chief aim of the review study is to draw the attention of a broad neuroscience community in order to maximize the applications of OCT in other branches of neuroscience too, and the study may also serve as a benchmark for future OCT-based neuroscience research. Despite some limitations, OCT proves to be a useful imaging tool in both basic and clinical neuroscience research.

Evaluation of collimated polarized light imaging for real-time intraoperative selective nerve identification in the human hand

Intraoperative peripheral nerve lesions are common complications due to misidentification and limitations of surgical nerve identification. This study validates a real-time non-invasive intraoperative method of nerve identification. Long working distance collimated polarized light imaging (CPLi) was used to identify peripheral radial nerve branches in a human cadaver hand by their nerve specific anisotropic optical reflection. Seven ex situ and six in situ samples were examined for nerves, resulting after histological validation, in a 100% positive correct score (CPLi) versus 77% (surgeon). Nerves were visible during a clinical in vivo observation using CPLi. Therefore CPLi is a promising technique for intraoperative nerve identification.

Intraoperative imaging techniques for glioma surgery

Gliomas are CNS neoplasms that infiltrate the surrounding brain parenchyma, complicating their treatment. Tools that increase extent of resection while preventing neurological deficit are essential to improve prognosis of patients diagnosed with gliomas. Tools such as intraoperative MRI, ultrasound and fluorescence-guided microsurgery have been used in the surgical resection of CNS gliomas with the goal of maximizing extent of resection to improve patient outcomes. In addition, emerging experimental techniques, for example, optical coherence tomography and Raman spectroscopy are promising techniques which could 1 day add to the increasing armamentarium used in the surgical resection of CNS gliomas. Here, we present the potential advantages and limitations of these imaging techniques for the purposes of identifying gliomas in the operating room.

Detection of cervical intraepithelial neoplasia by using optical coherence tomography in combination with microscopy

Optical coherence tomography (OCT) is a noninvasive high-resolution imaging technique that permits the detection of cancerous and precancerous lesions of the uterine cervix. The purpose of this study was to evaluate a new system that integrates an OCT device into a microscope. OCT images were taken from loop electrosurgical excision procedure (LEEP) specimens under microscopic guidance. The images were blinded with respect to their origin within the microscopic image and analyzed independently by two investigators using initially defined criteria and later compared to the corresponding histology. Sensitivity and specificity were calculated with respect to the correct identification of high-grade squamous intraepithelial lesions (HSIL). The interinvestigator agreement was assessed by using Cohen’s kappa statistics. About 160 OCT images were obtained from 20 LEEP specimens. Sixty randomly chosen images were used to define reproducible criteria for evaluation. The assessment of the remaining 100 images showed a sensitivity of 88% (second investigator 84%) and a specificity of 69% (65%) in detecting HSIL. Surgical microscopy-guided OCT appears to be a promising technique for immediate assessment of microanatomical changes. In the gynecological setting, the combination of OCT with a colposcope may improve the detection of high-grade squamous intraepithelial lesions.

Detection of human brain cancer infiltration ex vivo and in vivo using quantitative optical coherence tomography

More complete brain cancer resection can prolong survival and delay recurrence. However, it is challenging to distinguish cancer from noncancer tissues intraoperatively, especially at the transitional, infiltrative zones. This is especially critical in eloquent regions (for example, speech and motor areas). This study tested the feasibility of label-free, quantitative optical coherence tomography (OCT) for differentiating cancer from noncancer in human brain tissues. Fresh ex vivo human brain tissues were obtained from 32 patients with grade II to IV brain cancer and 5 patients with noncancer brain pathologies. On the basis of volumetric OCT imaging data, pathologically confirmed brain cancer tissues (both high- and low-grade) had significantly lower optical attenuation values at both cancer core and infiltrated zones when compared with noncancer white matter, and OCT achieved high sensitivity and specificity at an attenuation threshold of 5.5 mm(-1) for brain cancer patients. We also used this attenuation threshold to confirm the intraoperative feasibility of performing in vivo OCT-guided surgery using a murine model harboring human brain cancer. Our OCT system was capable of processing and displaying a color-coded optical property map in real time at a rate of 110 to 215 frames per second, or 1.2 to 2.4 s for an 8- to 16-mm(3) tissue volume, thus providing direct visual cues for cancer versus noncancer areas. Our study demonstrates the translational and practical potential of OCT in differentiating cancer from noncancer tissue. Its intraoperative use may facilitate safe and extensive resection of infiltrative brain cancers and consequently lead to improved outcomes when compared with current clinical standards.

Feasibility of interactive gesture control of a robotic microscope

Robotic devices become increasingly available in the clinics. One example are motorized surgical microscopes. While there are different scenarios on how to use the devices for autonomous tasks, simple and reliable interaction with the device is a key for acceptance by surgeons. We study, how gesture tracking can be integrated within the setup of a robotic microscope. In our setup, a Leap Motion Controller is used to track hand motion and adjust the field of view accordingly. We demonstrate with a survey that moving the field of view over a specified course is possible even for untrained subjects. Our results indicate that touch-less interaction with robots carrying small, near field gesture sensors is feasible and can be of use in clinical scenarios, where robotic devices are used in direct proximity of patient and physicians.

Microstructural Characterization of the Pia-Arachnoid Complex Using Optical Coherence Tomography

Traumatic brain injury (TBI) is one of the leading causes of death and disability in the world, and is often identified by the presence of subdural and/or subarachnoid hemorrhages that develop from ruptured cortical vessels during brain-skull displacement. The pia-arachnoid complex (PAC), also known as the leptomeninges, is the major mechanical connection between the brain and skull, and influences cortical vessel deformation and rupture following brain trauma. This complex consists of cerebrospinal fluid, arachnoid trabeculae, and subarachnoid vasculature sandwiched between the arachnoid and pia mater membranes. Remarkably, studies of the tissues in the PAC are largely qualitative and do not provide numerical metrics of population density and variability of the arachnoid trabeculae and subarachnoid vasculature. In this study, microstructural imaging was performed on the PAC to numerically quantify these metrics. Five porcine brains were perfusion-fixed and imaged in situ using optical coherence tomography with micrometer resolution. Image processing was performed to estimate the volume fraction (VF) of the arachnoid trabeculae and subarachnoid vasculature in 12 regions of the brain. High regional variability was found within each brain, with individual brains exhibiting up to a 38.4 percentage-point range in VF. Regions with high VF were often next to regions with low VF. This suggests that some areas of the brain may be mechanically weaker, increasing their susceptibility to hemorrhage during TBI events. This study provides the first quantifiable data of arachnoid trabeculae and subarachnoid vasculature distribution within the PAC and will be valuable to understanding brain biomechanics during head trauma.

Automated registration of optical coherence tomography and dermoscopy in the assessment of sub-clinical spread in basal cell carcinoma

Optical coherence tomography (OCT) has been shown to be of clinical value in imaging basal cell carcinoma (BCC). A novel dual OCT-video imaging system, providing automated registration of OCT and dermoscopy, has been developed to assess the potential of OCT in measuring the degree of sub-clinical spread of BCC. Seventeen patients selected for Mohs micrographic surgery (MMS) for BCC were recruited to the study. The extent of BCC infiltration beyond a segment of the clinically assessed pre-surgical border was evaluated using OCT. Sufficiently accurate (<0.5 mm) registration of OCT and dermoscopy images was achieved in 9 patients. The location of the OCT-assessed BCC border was also compared with that of the final surgical defect. Infiltration of BCC across the clinical border ranged from 0 mm to >2.5 mm. In addition, the OCT border lay between 0.5 mm and 2.0 mm inside the final MMS defect in those cases where this could be assessed. In one case, where the final MMS defect was over 17 mm from the clinical border, OCT showed >2.5 mm infiltration across the clinical border at the FOV limit. These results provide evidence that OCT allows more accurate assessment of sub-clinical spread of BCC than clinical observation alone. Such a capability may have clinical value in reducing the number of surgical stages in MMS for BCC. There may also be a role for OCT in aiding the selection of patients most suitable for MMS.

Evaluation of a completely robotized neurosurgical operating microscope

BACKGROUND

Operating microscopes are essential for most neurosurgical procedures. Modern robot-assisted controls offer new possibilities, combining the advantages of conventional and automated systems.

OBJECTIVE

We evaluated the prototype of a completely robotized operating microscope with an integrated optical coherence tomography module.

METHODS

A standard operating microscope was fitted with motors and control instruments, with the manual control mode and balance preserved. In the robot mode, the microscope was steered by a remote control that could be fixed to a surgical instrument. External encoders and accelerometers tracked microscope movements. The microscope was additionally fitted with an optical coherence tomography-scanning module.

RESULTS

The robotized microscope was tested on model systems. It could be freely positioned, without forcing the surgeon to take the hands from the instruments or avert the eyes from the oculars. Positioning error was about 1 mm, and vibration faded in 1 second. Tracking of microscope movements, combined with an autofocus function, allowed determination of the focus position within the 3-dimensional space. This constituted a second loop of navigation independent from conventional infrared reflector-based techniques. In the robot mode, automated optical coherence tomography scanning of large surface areas was feasible.

CONCLUSION

The prototype of a robotized optical coherence tomography-integrated operating microscope combines the advantages of a conventional manually controlled operating microscope with a remote-controlled positioning aid and a self-navigating microscope system that performs automated positioning tasks such as surface scans. This demonstrates that, in the future, operating microscopes may be used to acquire intraoperative spatial data, volume changes, and structural data of brain or brain tumor tissue.