Evaluation of a system for high-accuracy 3D image-based registration of endoscopic video to C-arm cone-beam CT for image-guided skull base surgery

Point Cloud Registration in Laparoscopic Liver Surgery Using Keypoint Correspondence Registration Network

Laparoscopic liver surgery is a newly developed minimally invasive technique and represents an inevitable trend in the future development of surgical methods. By using augmented reality (AR) technology to overlay preoperative CT models with intraoperative laparoscopic videos, surgeons can accurately locate blood vessels and tumors, significantly enhancing the safety and precision of surgeries. Point cloud registration technology is key to achieving this effect. However, there are two major challenges in registering the CT model with the point cloud surface reconstructed from intraoperative laparoscopy. First, the surface features of the organ are not prominent. Second, due to the limited field of view of the laparoscope, the reconstructed surface typically represents only a very small portion of the entire organ. To address these issues, this paper proposes the keypoint correspondence registration network (KCR-Net). This network first uses the neighborhood feature fusion module (NFFM) to aggregate and interact features from different regions and structures within a pair of point clouds to obtain comprehensive feature representations. Then, through correspondence generation, it directly generates keypoints and their corresponding weights, with keypoints located in the common structures of the point clouds to be registered, and corresponding weights learned automatically by the network. This approach enables accurate point cloud registration even under conditions of extremely low overlap. Experiments conducted on the ModelNet40, 3Dircadb, DePoLL demonstrate that our method achieves excellent registration accuracy and is capable of meeting the requirements of real-world scenarios.

DSIFNet: Implicit feature network for nasal cavity and vestibule segmentation from 3D head CT

This study is dedicated to accurately segment the nasal cavity and its intricate internal anatomy from head CT images, which is critical for understanding nasal physiology, diagnosing diseases, and planning surgeries. Nasal cavity and it's anatomical structures such as the sinuses, and vestibule exhibit significant scale differences, with complex shapes and variable microstructures. These features require the segmentation method to have strong cross-scale feature extraction capabilities. To effectively address this challenge, we propose an image segmentation network named the Deeply Supervised Implicit Feature Network (DSIFNet). This network uniquely incorporates an Implicit Feature Function Module Guided by Local and Global Positional Information (LGPI-IFF), enabling effective fusion of features across scales and enhancing the network's ability to recognize details and overall structures. Additionally, we introduce a deep supervision mechanism based on implicit feature functions in the network's decoding phase, optimizing the utilization of multi-scale feature information, thus improving segmentation precision and detail representation. Furthermore, we constructed a dataset comprising 7116 CT volumes (including 1,292,508 slices) and implemented PixPro-based self-supervised pretraining to utilize unlabeled data for enhanced feature extraction. Our tests on nasal cavity and vestibule segmentation, conducted on a dataset comprising 128 head CT volumes (including 34,006 slices), demonstrate the robustness and superior performance of proposed method, achieving leading results across multiple segmentation metrics.

Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Today, image-guided systems play a significant role in improving the outcome of diagnostic and therapeutic interventions. They provide crucial anatomical information during the procedure to decrease the size and the extent of the approach, to reduce intraoperative complications, and to increase accuracy, repeatability, and safety. Image-to-patient registration is the first step in image-guided procedures. It establishes a correspondence between the patient's preoperative imaging and the intraoperative data. When it comes to the head-and-neck region, the presence of many sensitive structures such as the central nervous system or the neurosensory organs requires a millimetric precision. This review allows evaluating the characteristics and the performances of different registration methods in the head-and-neck region used in the operation room from the perspectives of accuracy, invasiveness, and processing times. Our work led to the conclusion that invasive marker-based methods are still considered as the gold standard of image-to-patient registration. The surface-based methods are recommended for faster procedures and applied on the surface tissues especially around the eyes. In the near future, computer vision technology is expected to enhance these systems by reducing human errors and cognitive load in the operating room.

Use of Neuronavigation and Augmented Reality in Transsphenoidal Pituitary Adenoma Surgery

The aim of this study was to report on the clinical experience with microscope-based augmented reality (AR) in transsphenoidal surgery compared to the classical microscope-based approach. AR support was established using the head-up displays of the operating microscope, with navigation based on fiducial-/surface- or automatic intraoperative computed tomography (iCT)-based registration. In a consecutive single surgeon series of 165 transsphenoidal procedures, 81 patients underwent surgery without AR support and 84 patients underwent surgery with AR support. AR was integrated straightforwardly within the workflow. ICT-based registration increased AR accuracy significantly (target registration error, TRE, 0.76 ± 0.33 mm) compared to the landmark-based approach (TRE 1.85 ± 1.02 mm). The application of low-dose iCT protocols led to a significant reduction in applied effective dosage being comparable to a single chest radiograph. No major vascular or neurological complications occurred. No difference in surgical time was seen, time to set-up patient registration prolonged intraoperative preparation time on average by twelve minutes (32.33 ± 13.35 vs. 44.13 ± 13.67 min), but seems justifiable by the fact that AR greatly and reliably facilitated surgical orientation and increased surgeon comfort and patient safety, not only in patients who had previous transsphenoidal surgery but also in cases with anatomical variants. Automatic intraoperative imaging-based registration is recommended.

Pre-Clinical Development of Robot-Assisted Ventriculoscopy for 3D Image Reconstruction and Guidance of Deep Brain Neurosurgery

Conventional neuro-navigation can be challenged in targeting deep brain structures via transventricular neuroendoscopy due to unresolved geometric error following soft-tissue deformation. Current robot-assisted endoscopy techniques are fairly limited, primarily serving to planned trajectories and provide a stable scope holder. We report the implementation of a robot-assisted ventriculoscopy (RAV) system for 3D reconstruction, registration, and augmentation of the neuroendoscopic scene with intraoperative imaging, enabling guidance even in the presence of tissue deformation and providing visualization of structures beyond the endoscopic field-of-view. Phantom studies were performed to quantitatively evaluate image sampling requirements, registration accuracy, and computational runtime for two reconstruction methods and a variety of clinically relevant ventriculoscope trajectories. A median target registration error of 1.2 mm was achieved with an update rate of 2.34 frames per second, validating the RAV concept and motivating translation to future clinical studies.

Cost-function testing methodology for image-based registration of endoscopy to CT images in the head and neck

One of the largest geometric uncertainties in designing radiotherapy treatment plans for squamous cell cancers of the head and neck is contouring the gross tumour volume. We have previously described a method of projecting mucosal disease contours, visible on endoscopy, to volumetrically reconstructed planning CT datasets, using electromagnetic (EM) tracking of a flexible endoscope, enabling rigid registration between endoscopic and CT images. However, to achieve better accuracy for radiotherapy planning, we propose refining this initial registration with image-based registration methods. In this paper, several types of cost functions are evaluated based on accuracy and robustness. Three phantoms and eight clinical cases are used to test each cost function, with initial registration of endoscopy to CT provided by the pose of the flexible endoscope recovered from EM tracking. Cost function classes include: cross correlation, mutual information and gradient methods. For each test case, a ground truth virtual camera pose was first defined by manual registration of anatomical features visible in both real and virtual endoscope images. A new set of evenly spaced fiducial points and a sample contour were created and projected onto the CT image to be used in assessing image registration quality. A new set of 5000 displaced poses was generated by random sampling displacements along each translational and rotational dimension. At each pose, fiducial and contour points in the real image were again projected on the CT image. The cost function, fiducial registration error and contouring error values were then calculated. While all cost functions performed well in select cases, only the normalized gradient field function consistently had registration errors less than 2 mm, which is the accuracy needed if this application of registering mucosal disease identified on optical image to CT images is to be used in the clinical practice of radiation treatment planning. (Registration: ClinicalTrials.gov NCT02704169).

3D augmentation of the surgical video stream: Toward a modular approach

BACKGROUND AND OBJECTIVE

We present an original approach to the development of augmented reality (AR) real-time solutions for robotic surgery navigation. The surgeon operating the robotic system through a console and a visor experiences reduced awareness of the operatory scene. In order to improve the surgeon's spatial perception during robot-assisted minimally invasive procedures, we provide him/her with a solid automatic software system to position, rotate and scale in real-time the 3D virtual model of a patient's organ aligned over its image captured by the endoscope.

METHODS

We observed that the surgeon may benefit differently from the 3D augmentation during each stage of the surgical procedure; moreover, each stage may present different visual elements that provide specific challenges and opportunities to exploit for organ detection strategies implementation. Hence we integrate different solutions, each dedicated to a specific stage of the surgical procedure, into a single software system.

RESULTS

We present a formal model that generalizes our approach, describing a system composed of integrated solutions for AR in robot-assisted surgery. Following the proposed framework, and application has been developed which is currently used during in vivo surgery, for extensive testing, by the Urology unity of the San Luigi Hospital, in Orbassano (To), Italy.

CONCLUSIONS

The main contribution of this paper is in presenting a modular approach to the tracking problem during in-vivo robotic surgery, whose efficacy from a medical point of view has been assessed in cited works. The segmentation of the whole procedure in a set of stages allows associating the best tracking strategy to each of them, as well as to re-utilize implemented software mechanisms in stages with similar features.

Fusion of augmented reality imaging with the endoscopic view for endonasal skull base surgery; a novel application for surgical navigation based on intraoperative cone beam computed tomography and optical tracking

Objective

Surgical navigation is a well-established tool in endoscopic skull base surgery. However, navigational and endoscopic views are usually displayed on separate monitors, forcing the surgeon to focus on one or the other. Aiming to provide real-time integration of endoscopic and diagnostic imaging information, we present a new navigation technique based on augmented reality with fusion of intraoperative cone beam computed tomography (CBCT) on the endoscopic view. The aim of this study was to evaluate the accuracy of the method.

Material and methods

An augmented reality surgical navigation system (ARSN) with 3D CBCT capability was used. The navigation system incorporates an optical tracking system (OTS) with four video cameras embedded in the flat detector of the motorized C-arm. Intra-operative CBCT images were fused with the view of the surgical field obtained by the endoscope’s camera. Accuracy of CBCT image co-registration was tested using a custom-made grid with incorporated 3D spheres.

Results

Co-registration of the CBCT image on the endoscopic view was performed. Accuracy of the overlay, measured as mean target registration error (TRE), was 0.55 mm with a standard deviation of 0.24 mm and with a median value of 0.51mm and interquartile range of 0.39˗˗0.68 mm.

Conclusion

We present a novel augmented reality surgical navigation system, with fusion of intraoperative CBCT on the endoscopic view. The system shows sub-millimeter accuracy.

Is intraoperative navigation for needle breakage mandatory?: A case report

BACKGROUND AND OVERVIEW

Needle breakage when administering local anesthetic in the oral cavity can be of major concern to both the patient and the dentist. Intraoperative navigation has become the most popular advanced imaging technique.

CASE DESCRIPTION

In this report, the authors describe a case of needle breakage during inferior alveolar nerve block for a dental procedure. Using preoperative imaging, the authors located the needle and removed it while the patient was under general anesthesia. The authors review studies and case reports similar to the pre- and intraoperative imaging modalities presented in their report.

CONCLUSIONS AND PRACTICAL IMPLICATIONS

Preoperative 3-dimensional imaging is sufficient for establishing the exact location of the broken needle, especially in cases in which potential migration is unlikely.

Evaluation and Stability Analysis of Video-Based Navigation System for Functional Endoscopic Sinus Surgery on In Vivo Clinical Data

Functional endoscopic sinus surgery (FESS) is one of the most common outpatient surgical procedures performed in the head and neck region. It is used to treat chronic sinusitis, a disease characterized by inflammation in the nose and surrounding paranasal sinuses, affecting about 15% of the adult population. During FESS, the nasal cavity is visualized using an endoscope, and instruments are used to remove tissues that are often within a millimeter of critical anatomical structures, such as the optic nerve, carotid arteries, and nasolacrimal ducts. To maintain orientation and to minimize the risk of damage to these structures, surgeons use surgical navigation systems to visualize the 3-D position of their tools on patients' preoperative Computed Tomographies (CTs). This paper presents an image-based method for enhanced endoscopic navigation. The main contributions are: (1) a system that enables a surgeon to asynchronously register a sequence of endoscopic images to a CT scan with higher accuracy than other reported solutions using no additional hardware; (2) the ability to report the robustness of the registration; and (3) evaluation on in vivo human data. The system also enables the overlay of anatomical structures, visible, or occluded, on top of video images. The methods are validated on four different data sets using multiple evaluation metrics. First, for experiments on synthetic data, we observe a mean absolute position error of 0.21mm and a mean absolute orientation error of 2.8° compared with ground truth. Second, for phantom data, we observe a mean absolute position error of 0.97mm and a mean absolute orientation error of 3.6° compared with the same motion tracked by an electromagnetic tracker. Third, for cadaver data, we use fiducial landmarks and observe an average reprojection distance error of 0.82mm. Finally, for in vivo clinical data, we report an average ICP residual error of 0.88mm in areas that are not composed of erectile tissue and an average ICP residual error of 1.09mm in areas that are composed of erectile tissue.

Automatic intraoperative stitching of nonoverlapping cone-beam CT acquisitions

PURPOSE

Cone-beam computed tomography (CBCT) is one of the primary imaging modalities in radiation therapy, dentistry, and orthopedic interventions. While CBCT provides crucial intraoperative information, it is bounded by a limited imaging volume, resulting in reduced effectiveness. This paper introduces an approach allowing real-time intraoperative stitching of overlapping and nonoverlapping CBCT volumes to enable 3D measurements on large anatomical structures.

METHODS

A CBCT-capable mobile C-arm is augmented with a red-green-blue-depth (RGBD) camera. An offline cocalibration of the two imaging modalities results in coregistered video, infrared, and x-ray views of the surgical scene. Then, automatic stitching of multiple small, nonoverlapping CBCT volumes is possible by recovering the relative motion of the C-arm with respect to the patient based on the camera observations. We propose three methods to recover the relative pose: RGB-based tracking of visual markers that are placed near the surgical site, RGBD-based simultaneous localization and mapping (SLAM) of the surgical scene which incorporates both color and depth information for pose estimation, and surface tracking of the patient using only depth data provided by the RGBD sensor.

RESULTS

On an animal cadaver, we show stitching errors as low as 0.33, 0.91, and 1.72 mm when the visual marker, RGBD SLAM, and surface data are used for tracking, respectively.

CONCLUSIONS

The proposed method overcomes one of the major limitations of CBCT C-arm systems by integrating vision-based tracking and expanding the imaging volume without any intraoperative use of calibration grids or external tracking systems. We believe this solution to be most appropriate for 3D intraoperative verification of several orthopedic procedures.

Advanced Endoscopic Navigation: Surgical Big Data, Methodology, and Applications

Interventional endoscopy (e.g., bronchoscopy, colonoscopy, laparoscopy, cystoscopy) is a widely performed procedure that involves either diagnosis of suspicious lesions or guidance for minimally invasive surgery in a variety of organs within the body cavity. Endoscopy may also be used to guide the introduction of certain items (e.g., stents) into the body. Endoscopic navigation systems seek to integrate big data with multimodal information (e.g., computed tomography, magnetic resonance images, endoscopic video sequences, ultrasound images, external trackers) relative to the patient's anatomy, control the movement of medical endoscopes and surgical tools, and guide the surgeon's actions during endoscopic interventions. Nevertheless, it remains challenging to realize the next generation of context-aware navigated endoscopy. This review presents a broad survey of various aspects of endoscopic navigation, particularly with respect to the development of endoscopic navigation techniques. First, we investigate big data with multimodal information involved in endoscopic navigation. Next, we focus on numerous methodologies used for endoscopic navigation. We then review different endoscopic procedures in clinical applications. Finally, we discuss novel techniques and promising directions for the development of endoscopic navigation.

Phantomless Auto-Calibration and Online Calibration Assessment for a Tracked Freehand 2-D Ultrasound Probe

This paper presents a method for automatically calibrating and assessing the calibration quality of an externally tracked 2-D ultrasound (US) probe by scanning arbitrary, natural tissues, as opposed a specialized calibration phantom as is the typical practice. A generative topic model quantifies the posterior probability of calibration parameters conditioned on local 2-D image features arising from a generic underlying substrate. Auto-calibration is achieved by identifying the maximum a-posteriori image-to-probe transform, and calibration quality is assessed online in terms of the posterior probability of the current image-to-probe transform. Both are closely linked to the 3-D point reconstruction error (PRE) in aligning feature observations arising from the same underlying physical structure in different US images. The method is of practical importance in that it operates simply by scanning arbitrary textured echogenic structures, e.g., in-vivo tissues in the context of the US-guided procedures, without requiring specialized calibration procedures or equipment. Observed data take the form of local scale-invariant features that can be extracted and fit to the model in near real-time. Experiments demonstrate the method on a public data set of in vivo human brain scans of 14 unique subjects acquired in the context of neurosurgery. Online calibration assessment can be performed at approximately 3 Hz for the US images of pixels. Auto-calibration achieves an internal mean PRE of 1.2 mm and a discrepancy of [2 mm, 6 mm] in comparison to the calibration via a standard phantom-based method.

Registration and fusion quantification of augmented reality based nasal endoscopic surgery

This paper quantifies the registration and fusion display errors of augmented reality-based nasal endoscopic surgery (ARNES). We comparatively investigated the spatial calibration process for front-end endoscopy and redefined the accuracy level of a calibrated endoscope by using a calibration tool with improved structural reliability. We also studied how registration accuracy was combined with the number and distribution of the deployed fiducial points (FPs) for positioning and the measured registration time. A physically integrated ARNES prototype was customarily configured for performance evaluation in skull base tumor resection surgery with an innovative approach of dynamic endoscopic vision expansion. As advised by surgical experts in otolaryngology, we proposed a hierarchical rendering scheme to properly adapt the fused images with the required visual sensation. By constraining the rendered sight in a known depth and radius, the visual focus of the surgeon can be induced only on the anticipated critical anatomies and vessel structures to avoid misguidance. Furthermore, error analysis was conducted to examine the feasibility of hybrid optical tracking based on point cloud, which was proposed in our previous work as an in-surgery registration solution. Measured results indicated that the error of target registration for ARNES can be reduced to 0.77 ± 0.07 mm. For initial registration, our results suggest that a trade-off for a new minimal time of registration can be reached when the distribution of five FPs is considered. For in-surgery registration, our findings reveal that the intrinsic registration error is a major cause of performance loss. Rigid model and cadaver experiments confirmed that the scenic integration and display fluency of ARNES are smooth, as demonstrated by three clinical trials that surpassed practicality.

The feasibility of endoscopy-CT image registration in the head and neck without prospective endoscope tracking

PURPOSE

Endoscopic examinations are frequently-used procedures for patients with head and neck cancer undergoing radiotherapy, but radiation treatment plans are created on computed tomography (CT) scans. Image registration between endoscopic video and CT could be used to improve treatment planning and analysis of radiation-related normal tissue toxicity. The purpose of this study was to explore the feasibility of endoscopy-CT image registration without prospective physical tracking of the endoscope during the examination.

METHODS

A novel registration technique called Location Search was developed. This technique uses physical constraints on the endoscope's view direction to search for the virtual endoscope coordinates that maximize the similarity between the endoscopic video frame and the virtual endoscopic image. Its performance was tested on phantom and patient images and compared to an established registration technique, Frame-To-Frame Tracking.

RESULTS

In phantoms, Location Search had average registration errors of 0.55 ± 0.60 cm for point measurements and 0.29 ± 0.15 cm for object surface measurements. Frame-To-Frame Tracking achieved similar results on some frames, but it failed on others due to the virtual endoscope becoming lost. This weakness was more pronounced in patients, where Frame-To-Frame tracking could not make it through the nasal cavity. On successful patient video frames, Location Search was able to find endoscope positions with an average distance of 0.98 ± 0.53 cm away from the ground truth positions. However, it failed on many frames due to false similarity matches caused by anatomical structural differences between the endoscopic video and the virtual endoscopic images.

CONCLUSIONS

Endoscopy-CT image registration without prospective physical tracking of the endoscope is possible, but more development is required to achieve an accuracy suitable for clinical translation.

A Systematic Approach to Predicting Spring Force for Sagittal Craniosynostosis Surgery

Spring-assisted surgery (SAS) can effectively treat scaphocephaly by reshaping crania with the appropriate spring force. However, it is difficult to accurately estimate spring force without considering biomechanical properties of tissues. This study presents and validates a reliable system to accurately predict the spring force for sagittal craniosynostosis surgery. The authors randomly chose 23 patients who underwent SAS and had been followed for at least 2 years. An elastic model was designed to characterize the biomechanical behavior of calvarial bone tissue for each individual. After simulating the contact force on accurate position of the skull strip with the springs, the finite element method was applied to calculating the stress of each tissue node based on the elastic model. A support vector regression approach was then used to model the relationships between biomechanical properties generated from spring force, bone thickness, and the change of cephalic index after surgery. Therefore, for a new patient, the optimal spring force can be predicted based on the learned model with virtual spring simulation and dynamic programming approach prior to SAS. Leave-one-out cross-validation was implemented to assess the accuracy of our prediction. As a result, the mean prediction accuracy of this model was 93.35%, demonstrating the great potential of this model as a useful adjunct for preoperative planning tool.

Video see-through augmented reality for oral and maxillofacial surgery

BACKGROUND

Oral and maxillofacial surgery has not been benefitting from image guidance techniques owing to the limitations in image registration.

METHODS

A real-time markerless image registration method is proposed by integrating a shape matching method into a 2D tracking framework. The image registration is performed by matching the patient's teeth model with intraoperative video to obtain its pose. The resulting pose is used to overlay relevant models from the same CT space on the camera video for augmented reality.

RESULTS

The proposed system was evaluated on mandible/maxilla phantoms, a volunteer and clinical data. Experimental results show that the target overlay error is about 1 mm, and the frame rate of registration update yields 3-5 frames per second with a 4 K camera.

CONCLUSIONS

The significance of this work lies in its simplicity in clinical setting and the seamless integration into the current medical procedure with satisfactory response time and overlay accuracy. Copyright © 2016 John Wiley & Sons, Ltd.

Image-Based Navigation for Functional Endoscopic Sinus Surgery Using Structure From Motion

Functional Endoscopic Sinus Surgery (FESS) is a challenging procedure for otolaryngologists and is the main surgical approach for treating chronic sinusitis, to remove nasal polyps and open up passageways. To reach the source of the problem and to ultimately remove it, the surgeons must often remove several layers of cartilage and tissues. Often, the cartilage occludes or is within a few millimeters of critical anatomical structures such as nerves, arteries and ducts. To make FESS safer, surgeons use navigation systems that register a patient to his/her CT scan and track the position of the tools inside the patient. Current navigation systems, however, suffer from tracking errors greater than 1 mm, which is large when compared to the scale of the sinus cavities, and errors of this magnitude prevent from accurately overlaying virtual structures on the endoscope images. In this paper, we present a method to facilitate this task by 1) registering endoscopic images to CT data and 2) overlaying areas of interests on endoscope images to improve the safety of the procedure. First, our system uses structure from motion (SfM) to generate a small cloud of 3D points from a short video sequence. Then, it uses iterative closest point (ICP) algorithm to register the points to a 3D mesh that represents a section of a patients sinuses. The scale of the point cloud is approximated by measuring the magnitude of the endoscope's motion during the sequence. We have recorded several video sequences from five patients and, given a reasonable initial registration estimate, our results demonstrate an average registration error of 1.21 mm when the endoscope is viewing erectile tissues and an average registration error of 0.91 mm when the endoscope is viewing non-erectile tissues. Our implementation SfM + ICP can execute in less than 7 seconds and can use as few as 15 frames (0.5 second of video). Future work will involve clinical validation of our results and strengthening the robustness to initial guesses and erectile tissues.

Endoscopic-CT: Learning-Based Photometric Reconstruction for Endoscopic Sinus Surgery

In this work we present a method for dense reconstruction of anatomical structures using white light endoscopic imagery based on a learning process that estimates a mapping between light reflectance and surface geometry. Our method is unique in that few unrealistic assumptions are considered (i.e., we do not assume a Lambertian reflectance model nor do we assume a point light source) and we learn a model on a per-patient basis, thus increasing the accuracy and extensibility to different endoscopic sequences. The proposed method assumes accurate video-CT registration through a combination of Structure-from-Motion (SfM) and Trimmed-ICP, and then uses the registered 3D structure and motion to generate training data with which to learn a multivariate regression of observed pixel values to known 3D surface geometry. We demonstrate with a non-linear regression technique using a neural network towards estimating depth images and surface normal maps, resulting in high-resolution spatial 3D reconstructions to an average error of 0.53mm (on the low side, when anatomy matches the CT precisely) to 1.12mm (on the high side, when the presence of liquids causes scene geometry that is not present in the CT for evaluation). Our results are exhibited on patient data and validated with associated CT scans. In total, we processed 206 total endoscopic images from patient data, where each image yields approximately 1 million reconstructed 3D points per image.

Augmented reality and cone beam CT guidance for transoral robotic surgery

In transoral robotic surgery preoperative image data do not reflect large deformations of the operative workspace from perioperative setup. To address this challenge, in this study we explore image guidance with cone beam computed tomographic angiography to guide the dissection of critical vascular landmarks and resection of base-of-tongue neoplasms with adequate margins for transoral robotic surgery. We identify critical vascular landmarks from perioperative c-arm imaging to augment the stereoscopic view of a da Vinci si robot in addition to incorporating visual feedback from relative tool positions. Experiments resecting base-of-tongue mock tumors were conducted on a series of ex vivo and in vivo animal models comparing the proposed workflow for video augmentation to standard non-augmented practice and alternative, fluoroscopy-based image guidance. Accurate identification of registered augmented critical anatomy during controlled arterial dissection and en bloc mock tumor resection was possible with the augmented reality system. The proposed image-guided robotic system also achieved improved resection ratios of mock tumor margins (1.00) when compared to control scenarios (0.0) and alternative methods of image guidance (0.58). The experimental results show the feasibility of the proposed workflow and advantages of cone beam computed tomography image guidance through video augmentation of the primary stereo endoscopy as compared to control and alternative navigation methods.

Rendering-Based Video-CT Registration with Physical Constraints for Image-Guided Endoscopic Sinus Surgery

We present a system for registering the coordinate frame of an endoscope to pre- or intra- operatively acquired CT data based on optimizing the similarity metric between an endoscopic image and an image predicted via rendering of CT. Our method is robust and semi-automatic because it takes account of physical constraints, specifically, collisions between the endoscope and the anatomy, to initialize and constrain the search. The proposed optimization method is based on a stochastic optimization algorithm that evaluates a large number of similarity metric functions in parallel on a graphics processing unit. Images from a cadaver and a patient were used for evaluation. The registration error was 0.83 mm and 1.97 mm for cadaver and patient images respectively. The average registration time for 60 trials was 4.4 seconds. The patient study demonstrated robustness of the proposed algorithm against a moderate anatomical deformation.

Multi-view stereo and advanced navigation for transanal endoscopic microsurgery

Transanal endoscopic microsurgery (TEM), i.e., the local excision of rectal carcinomas by way of a bimanual operating system with magnified binocular vision, is gaining acceptance in lieu of more radical total interventions. A major issue with this approach is the lack of information on submucosal anatomical structures. This paper presents an advanced navigation system, wherein the intraoperative 3D structure is stably estimated from multiple stereoscopic views. It is registered to a preoperatively acquired anatomical volume based on subject-specific priors. The endoscope motion is tracked based on the 3D scene and its field-of-view is visualised jointly with the preoperative information. Based on in vivo data, this paper demonstrates how the proposed navigation system provides intraoperative navigation for TEM1.

An augmented reality framework for soft tissue surgery

Augmented reality for soft tissue laparoscopic surgery is a growing topic of interest in the medical community and has potential application in intra-operative planning and image guidance. Delivery of such systems to the operating room remains complex with theoretical challenges related to tissue deformation and the practical limitations of imaging equipment. Current research in this area generally only solves part of the registration pipeline or relies on fiducials, manual model alignment or assumes that tissue is static. This paper proposes a novel augmented reality framework for intra-operative planning: the approach co-registers pre-operative CT with stereo laparoscopic images using cone beam CT and fluoroscopy as bridging modalities. It does not require fiducials or manual alignment and compensates for tissue deformation from insufflation and respiration while allowing the laparoscope to be navigated. The paper's theoretical and practical contributions are validated using simulated, phantom, ex vivo, in vivo and non medical data.

Evaluation of a system for high-accuracy 3D image-based registration of endoscopic video to C-arm cone-beam CT for image-guided skull base surgery

The safety of endoscopic skull base surgery can be enhanced by accurate navigation in preoperative computed tomography (CT) or, more recently, intraoperative cone-beam CT (CBCT). The ability to register real-time endoscopic video with CBCT offers an additional advantage by rendering information directly within the visual scene to account for intraoperative anatomical change. However, tracker localization error (  ∼ 1-2 mm ) limits the accuracy with which video and tomographic images can be registered. This paper reports the first implementation of image-based video-CBCT registration, conducts a detailed quantitation of the dependence of registration accuracy on system parameters, and demonstrates improvement in registration accuracy achieved by the image-based approach. Performance was evaluated as a function of parameters intrinsic to the image-based approach, including system geometry, CBCT image quality, and computational runtime. Overall system performance was evaluated in a cadaver study simulating transsphenoidal skull base tumor excision. Results demonstrated significant improvement in registration accuracy with a mean reprojection distance error of 1.28 mm for the image-based approach versus 1.82 mm for the conventional tracker-based method. Image-based registration was highly robust against CBCT image quality factors of noise and resolution, permitting integration with low-dose intraoperative CBCT.

Anatomical reconstructions of pediatric airways from endoscopic images: a pilot study of the accuracy of quantitative endoscopy

OBJECTIVES/HYPOTHESIS

To evaluate the accuracy of three-dimensional (3D) airway reconstructions obtained using quantitative endoscopy (QE). We developed this novel technique to reconstruct precise 3D representations of airway geometries from endoscopic video streams. This method, based on machine vision methodologies, uses a post-processing step of the standard videos obtained during routine laryngoscopy and bronchoscopy. We hypothesize that this method is precise and will generate assessment of airway size and shape similar to those obtained using computed tomography (CT).

STUDY DESIGN

This study was approved by the institutional review board (IRB). We analyzed video sequences from pediatric patients receiving rigid bronchoscopy.

METHODS

We generated 3D scaled airway models of the subglottis, trachea, and carina using QE. These models were compared to 3D airway models generated from CT. We used the CT data as the gold standard measure of airway size, and used a mixed linear model to estimate the average error in cross-sectional area and effective diameter for QE.

RESULTS

The average error in cross sectional area (area sliced perpendicular to the long axis of the airway) was 7.7 mm(2) (variance 33.447 mm(4)). The average error in effective diameter was 0.38775 mm (variance 2.45 mm(2)), approximately 9% error.

CONCLUSION

Our pilot study suggests that QE can be used to generate precise 3D reconstructions of airways. This technique is atraumatic, does not require ionizing radiation, and integrates easily into standard airway assessment protocols. We conjecture that this technology will be useful for staging airway disease and assessing surgical outcomes.

TREK: an integrated system architecture for intraoperative cone-beam CT-guided surgery

PURPOSE

A system architecture has been developed for integration of intraoperative 3D imaging [viz., mobile C-arm cone-beam CT (CBCT)] with surgical navigation (e.g., trackers, endoscopy, and preoperative image and planning data). The goal of this paper is to describe the architecture and its handling of a broad variety of data sources in modular tool development for streamlined use of CBCT guidance in application-specific surgical scenarios.

METHODS

The architecture builds on two proven open-source software packages, namely the cisst package (Johns Hopkins University, Baltimore, MD) and 3D Slicer (Brigham and Women's Hospital, Boston, MA), and combines data sources common to image-guided procedures with intraoperative 3D imaging. Integration at the software component level is achieved through language bindings to a scripting language (Python) and an object-oriented approach to abstract and simplify the use of devices with varying characteristics. The platform aims to minimize offline data processing and to expose quantitative tools that analyze and communicate factors of geometric precision online. Modular tools are defined to accomplish specific surgical tasks, demonstrated in three clinical scenarios (temporal bone, skull base, and spine surgery) that involve a progressively increased level of complexity in toolset requirements.

RESULTS

The resulting architecture (referred to as "TREK") hosts a collection of modules developed according to application-specific surgical tasks, emphasizing streamlined integration with intraoperative CBCT. These include multi-modality image display; 3D-3D rigid and deformable registration to bring preoperative image and planning data to the most up-to-date CBCT; 3D-2D registration of planning and image data to real-time fluoroscopy; infrared, electromagnetic, and video-based trackers used individually or in hybrid arrangements; augmented overlay of image and planning data in endoscopic or in-room video; and real-time "virtual fluoroscopy" computed from GPU-accelerated digitally reconstructed radiographs (DRRs). Application in three preclinical scenarios (temporal bone, skull base, and spine surgery) demonstrates the utility of the modular, task-specific approach in progressively complex tasks.

CONCLUSIONS

The design and development of a system architecture for image-guided surgery has been reported, demonstrating enhanced utilization of intraoperative CBCT in surgical applications with vastly different requirements. The system integrates C-arm CBCT with a broad variety of data sources in a modular fashion that streamlines the interface to application-specific tools, accommodates distinct workflow scenarios, and accelerates testing and translation of novel toolsets to clinical use. The modular architecture was shown to adapt to and satisfy the requirements of distinct surgical scenarios from a common code-base, leveraging software components arising from over a decade of effort within the imaging and computer-assisted interventions community.