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The application of augmented reality technology to the partial hepatectomy procedure has high practical significance, but the existing augmented reality navigation system has major drawbacks in the display and registration methods, which result in low precision. The augmented reality surgical navigation system proposed in this study has been improved in the above two aspects, which can significantly improve the surgical accuracy. The use of optical see-through head-mounted displays for imaging displays can prevent doctors from reconstructing the patient's two-dimensional image information in their minds and reduce the psychological burden of doctors. In the registration process, the biomechanical properties of the liver are introduced, and a non-rigid registration method based on biomechanics is proposed and realized by a meshless algorithm. In addition, this study uses the moving grid algorithm to carry out clinical experiments on ex-vivo pig liver for experimental verification. The mark-based interactive registration error is 4.21 ± 1.6 mm, and the registration error is reduced after taking the biomechanical properties of the liver into account, which is − 0.153 ± 0.398 mm. The cutting error of the liver model is 0.159 ± 0.292 mm. In addition, with the aid of the navigation system proposed in this paper, the experiment of ex-vivo pig liver cutting was completed with an error of − 1.164 ± 0.576 mm. As a proof-of-concept study, the augmented reality navigation system proposed in this study improves the traditional image-guided surgery in terms of display and registration methods, and the feasibility of the system is verified by ex-vivo pig liver experiments. Therefore, the navigation system has a certain guiding significance for clinical surgery.

Purpose Interactive image-guided surgery technologies enable accurate target localization while preserving critical nearby structures in many surgical interventions. Current state-of-the-art interfaces largely employ traditional anatomical cross-sectional views or augmented reality environments to present the actual spatial location of the surgical instrument in preoperatively acquired images. This work proposes an alternative, simple, minimalistic visual interface intended to assist during real-time surgical target localization. Methods The estimated 3D pose of the interventional instruments and their positional uncertainty are intuitively presented in a visual interface with respect to the target point. A usability study with multidisciplinary participants evaluates the proposed interface projected in surgical microscope oculars against cross-sectional views. The latter was presented on a screen both stand-alone and combined with the proposed interface. The instruments were electromagnetically navigated in phantoms. Results The usability study demonstrated that the participants were able to detect invisible targets marked in phantom imagery with significant enhancements for localization accuracy and duration time. Clinically experienced users reached the targets with shorter trajectories. The stand-alone and multi-modal versions of the proposed interface outperformed cross-sectional views-only navigation in both quantitative and qualitative evaluations. Conclusion The results and participants’ feedback indicate potential to accurately navigate users toward the target with less distraction and workload. An ongoing study evaluates the proposed system in a preclinical setting for auditory brainstem implantation.

Purpose Resection site repair during laparoscopic oncological surgery (e.g. laparoscopic partial nephrectomy) poses some unique challenges and opportunities for augmented reality (AR) navigation support. This work introduces an AR registration workflow that addresses the time pressure that is present during resection site repair. Methods We propose a two-step registration process: the AR content is registered as accurately as possible prior to the tumour resection (the primary registration). This accurate registration is used to apply artificial fiducials to the physical organ and the virtual model. After the resection, these fiducials can be used for rapid re-registration (the secondary registration). We tested this pipeline in a simulated-use study with $$N=18$$ N = 18 participants. We compared the registration accuracy and speed for our method and for landmark-based registration as a reference. Results Acquisition of and, thereby, registration with the artificial fiducials were significantly faster than the initial use of anatomical landmarks. Our method also had a trend to be more accurate in cases in which the primary registration was successful. The accuracy loss between the elaborate primary registration and the rapid secondary registration could be quantified with a mean target registration error increase of 2.35 mm. Conclusion This work introduces a registration pipeline for AR navigation support during laparoscopic resection site repair and provides a successful proof-of-concept evaluation thereof. Our results indicate that the concept is better suited than landmark-based registration during this phase, but further work is required to demonstrate clinical suitability and applicability.

Purpose Emerging holographic headsets can be used to register patient-specific virtual models obtained from medical scans with the patient’s body. Maximising accuracy of the virtual models’ inclination angle and position (ideally, ≤ 2° and ≤ 2 mm, respectively, as in currently approved navigation systems) is vital for this application to be useful. This study investigated the accuracy with which a holographic headset registers virtual models with real-world features based on the position and size of image markers. Methods HoloLens® and the image-pattern-recognition tool Vuforia Engine™ were used to overlay a 5-cm-radius virtual hexagon on a monitor’s surface in a predefined position. The headset’s camera detection of an image marker (displayed on the monitor) triggered the rendering of the virtual hexagon on the headset’s lenses. 4 × 4, 8 × 8 and 12 × 12 cm image markers displayed at nine different positions were used. In total, the position and dimensions of 114 virtual hexagons were measured on photographs captured by the headset’s camera. Results Some image marker positions and the smallest image marker (4 × 4 cm) led to larger errors in the perceived dimensions of the virtual models than other image marker positions and larger markers (8 × 8 and 12 × 12 cm). ≤ 2° and ≤ 2 mm errors were found in 70.7% and 76% of cases, respectively. Conclusion Errors obtained in a non-negligible percentage of cases are not acceptable for certain surgical tasks (e.g. the identification of correct trajectories of surgical instruments). Achieving sufficient accuracy with image marker sizes that meet surgical needs and regardless of image marker position remains a challenge.

PurposeSurgical annotation promotes effective communication between medical personnel during surgical procedures. However, existing approaches to 2D annotations are mostly static with respect to a display. In this work, we propose a method to achieve 3D annotations that anchor rigidly and stably to target structures upon camera movement in a transnasal endoscopic surgery setting.MethodsThis is accomplished through intra-operative endoscope tracking and monocular depth estimation. A virtual endoscopic environment is utilized to train a supervised depth estimation network. An adversarial network transfers the style from the real endoscopic view to a synthetic-like view for input into the depth estimation network, wherein framewise depth can be obtained in real time.Results(1) Accuracy: Framewise depth was predicted from images captured from within a nasal airway phantom and compared with ground truth, achieving a SSIM value of 0.8310 ± 0.0655. (2) Stability: mean absolute error (MAE) between reference and predicted depth of a target point was 1.1330 ± 0.9957 mm.ConclusionBoth the accuracy and stability evaluations demonstrated the feasibility and practicality of our proposed method for achieving 3D annotations.

Purpose In laparoscopic surgery training, experts guide novice physicians to desired instrument positions or indicate relevant areas of interest. These instructions are usually given via verbal communication or using physical pointing devices. To facilitate a sterile work flow and to improve training, new guiding methods are needed. This work proposes to use optical see-through augmented reality to visualize an interactive virtual pointer on the laparoscopic. Methods After an interdisciplinary development, the pointer’s applicability and feasibility for training was evaluated and it was compared to a standard condition based on verbal and gestural communication only. In this study, ten surgical trainees were guided by an experienced trainer during cholecystectomies on a laparoscopic training simulator. All trainees completed a virtual cholecystectomy with and without the interactive virtual pointer in alternating order. Measures included procedure time, economy of movement and error rates. Results Results of standardized variables revealed significantly improved economy of movement (p = 0.047) and error rates (p = 0.047), as well as an overall improved user performance (Total z-score; p = 0.031) in conditions using the proposed method. Conclusion The proposed HoloPointer is a feasible and applicable tool for laparoscopic surgery training. It improved objective performance metrics without prolongation of the task completion time in this pre-clinical setup.

Purpose Mixed reality (MR) is being evaluated as a visual tool for surgical navigation. Current literature presents unclear results on intraoperative accuracy using the Microsoft HoloLens 1®. This study aims to assess the impact of the surgeon’s sightline in an inside-out marker-based MR navigation system for open surgery. Methods Surgeons at Akershus University Hospital tested this system. A custom-made phantom was used, containing 18 wire target crosses within its inner walls. A CT scan was obtained in order to segment all wire targets into a single 3D-model (hologram). An in-house software application (CTrue), developed for the Microsoft HoloLens 1, uploaded 3D-models and automatically registered the 3D-model with the phantom. Based on the surgeon’s sightline while registering and targeting (free sightline /F/or a strictly perpendicular sightline /P/), 4 scenarios were developed (FF-PF-FP-PP). Target error distance (TED) was obtained in three different working axes-(XYZ). Results Six surgeons (5 males, age 29–62) were enrolled. A total of 864 measurements were collected in 4 scenarios, twice. Scenario PP showed the smallest TED in XYZ-axes mean = 2.98 mm ± SD 1.33; 2.28 mm ± SD 1.45; 2.78 mm ± SD 1.91, respectively. Scenario FF showed the largest TED in XYZ-axes with mean = 10.03 mm ± SD 3.19; 6.36 mm ± SD 3.36; 16.11 mm ± SD 8.91, respectively. Multiple comparison tests, grouped in scenarios and axes, showed that the majority of scenario comparisons had significantly different TED values (p Y-axis always presented the smallest TED regardless of scenario tested. Conclusion A strictly perpendicular working sightline in relation to the 3D-model achieves the best accuracy results. Shortcomings in this technology, as an intraoperative visual cue, can be overcome by sightline correction. Incidentally, this is the preferred working angle for open surgery.

The use of an optical see-through head-mounted display (OST-HMD) in augmented reality (AR) has significantly increased in recent years, but the alignment between the virtual scene and physical reality is still a challenge. A fast and accurate calibration method of OST-HMD is important for augmented reality in the medical field. We proposed a fast online calibration procedure for OST-HMD with the aid of an optical tracking system. Two 3D datasets are collected in this procedure: the virtual points rendered in front of the observer’s eyes and the corresponding points in optical tracking space. The transformation between these two 3D coordinates is solved to build the connection between virtual and real space. An AR-based surgical navigation system is developed with the help of this procedure, which is used for experiment verification and clinical trial. Phantom experiment based on the 3D-printed skull is performed, and the average root-mean-square error of control points between rendered object and skull model is $$1.30 \pm 0.39$$ mm, and the time consumption of the calibration procedure can be less than 30 s. A clinical trial is also conducted to show the feasibility in real surgery theatre. The proposed calibration method does not rely on the camera of the OST-HMD and is very easy to operate. Phantom experiment and clinical case demonstrated the feasibility of our AR-based surgical navigation system and indicated it has the potential in clinical application.

Purpose Augmented reality (AR) and head-mounted displays (HMD) in medical practice are current research topics. A commonly proposed use case of AR-HMDs is to display data in image-guided interventions. Although technical feasibility has been thoroughly shown, effects of AR-HMDs on interventions are not yet well researched, hampering clinical applicability. Therefore, the goal of this study is to better understand the benefits and limitations of this technology in ultrasound-guided interventions. Methods We used an AR-HMD system (based on the first-generation Microsoft Hololens) which overlays live ultrasound images spatially correctly at the location of the ultrasound transducer. We chose ultrasound-guided needle placements as a representative task for image-guided interventions. To examine the effects of the AR-HMD, we used mixed methods and conducted two studies in a lab setting: (1) In a randomized crossover study, we asked participants to place needles into a training model and evaluated task duration and accuracy with the AR-HMD as compared to the standard procedure without visual overlay and (2) in a qualitative study, we analyzed the user experience with AR-HMD using think-aloud protocols during ultrasound examinations and semi-structured interviews after the task. Results Participants (n = 20) placed needles more accurately (mean error of 7.4 mm vs. 4.9 mm, p = 0.022) but not significantly faster (mean task duration of 74.4 s vs. 66.4 s, p = 0.211) with the AR-HMD. All participants in the qualitative study (n = 6) reported limitations of and unfamiliarity with the AR-HMD, yet all but one also clearly noted benefits and/or that they would like to test the technology in practice. Conclusion We present additional, though still preliminary, evidence that AR-HMDs provide benefits in image-guided procedures. Our data also contribute insights into potential causes underlying the benefits, such as improved spatial perception. Still, more comprehensive studies are needed to ascertain benefits for clinical applications and to clarify mechanisms underlying these benefits.

Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot is a common treatment for organ-confined prostate cancer. Augmented reality (AR) can help during RALRP by showing the surgeon the location of anatomical structures and tumors from preoperative imaging. Previously, we proposed hand-eye and camera intrinsic matrix estimation procedures that can be carried out with conventional instruments within the patient during surgery, take

Purpose This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years. Methods At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice. Results Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research. Conclusion The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.

International audience; Purpose. The registration of a preoperative 3D model, reconstructed for example from MRI, to intraoperative laparoscopy 2D images, is the main challenge to achieve augmented reality in laparoscopy. The current systems have a major limitation: they require that the surgeon manually marks the occluding contours during surgery. This requires the surgeon to fully comprehend the non-trivial concept of occluding contours and surgeon time, directly impacting acceptance and usability. To overcome this limitation, we propose a complete framework for object-class occluding contour detection (OC2D), with application to uterus surgery. Methods. Our first contribution is a new distance-based evaluation score complying with all the relevant performance criteria. Our second contribution is a loss function combining cross-entropy and two new penalties designed to boost 1-pixel thickness responses. This allows us to train a U-Net end-to-end, outperforming all competing methods, which tends to produce thick responses. Our third contribution is a dataset of 3818 carefully labelled laparoscopy images of the uterus, which was used to train and evaluate our detector. Results. Evaluation shows that the proposed detector has a similar false negative rate to existing methods but substantially reduces both false positive rate and response thickness. Finally, we ran a user-study to evaluate the impact of OC2D against manually marked occluding contours in augmented laparoscopy. We used 10 recorded gynecologic laparoscopies and involved 5 surgeons. Using OC2D led to a reduction of 3 minutes and 53 seconds in surgeon time without sacrificing registration accuracy. Conclusions. We provide a new set of criteria and a distance-based measure to evaluate an OC2D method. We propose an OC2D method which outperforms the state of the art methods. The results obtained from the user study indicate that fully automatic augmented laparoscopy is feasible. 2 T. François et al. Registration Preoperative 3D model with internal structures Occluding contours deformation of the 3D model Occluding contours Occlusion boundaries Uterus Connection contours Fig. 1 (left) In augmented laparoscopy, the preoperative 3D model is registered by fitting the occluding contours of the organ in laparoscopy images. The current systems require the surgeon to mark these contours manually during surgery. (right) An occluding contour arises at an organ boundary where the organ occludes another structure, as opposed to an occlusion boundary where the organ is occluded by another structure. The set of occluding contours is the silhouette. OC2D is the task of detecting the occluding contours for a specific object, here the uterus. It forms a task of semantic detection far more challenging than organ segmentation.

Neuronavigation systems making use of augmented reality (AR) have been the focus of much research in the last couple of decades. In recent years, there has been considerable interest in using mobile devices for AR in the operating room (OR). We propose a complete system that performs real-time AR video augmentation on a mobile device in the context of image-guided neurosurgery.MARIN (mobile augmented reality interactive neuronavigation system) improves upon the state of the art in terms of performance, allowing real-time augmentation, and interactivity by allowing users to interact with the displayed data. The system was tested in a user study with 17 subjects for qualitative and quantitative evaluation in the context of target localization and brought into the OR for preliminary feasibility tests, where qualitative feedback from surgeons was obtained.The results of the user study showed that MARIN performs significantly better in terms of both time ([Formula: see text]) and accuracy ([Formula: see text]) for the task of target localization in comparison with a traditional image-guided neurosurgery (IGNS) navigation system. Further, MARIN AR visualization was found to be more intuitive and allowed users to estimate target depth more easily.MARIN improves upon previously proposed mobile AR neuronavigation systems with its real-time performance, higher accuracy, full integration in the normal workflow and greater interactivity and customizability of the displayed information. The improvement in efficiency and usability over previous systems will facilitate bringing AR into the OR.

Purpose A robotic intraoperative laser guidance system with hybrid optic-magnetic tracking for skull base surgery is presented. It provides in situ augmented reality guidance for microscopic interventions at the lateral skull base with minimal mental and workload overhead on surgeons working without a monitor and dedicated pointing tools. Methods Three components were developed: a registration tool (Rhinospider), a hybrid magneto-optic-tracked robotic feedback control scheme and a modified robotic end-effector. Rhinospider optimizes registration of patient and preoperative CT data by excluding user errors in fiducial localization with magnetic tracking. The hybrid controller uses an integrated microscope HD camera for robotic control with a guidance beam shining on a dual plate setup avoiding magnetic field distortions. A robotic needle insertion platform (iSYS Medizintechnik GmbH, Austria) was modified to position a laser beam with high precision in a surgical scene compatible to microscopic surgery. Results System accuracy was evaluated quantitatively at various target positions on a phantom. The accuracy found is 1.2 mm ± 0.5 mm. Errors are primarily due to magnetic tracking. This application accuracy seems suitable for most surgical procedures in the lateral skull base. The system was evaluated quantitatively during a mastoidectomy of an anatomic head specimen and was judged useful by the surgeon. Conclusion A hybrid robotic laser guidance system with direct visual feedback is proposed for navigated drilling and intraoperative structure localization. The system provides visual cues directly on/in the patient anatomy, reducing the standard limitations of AR visualizations like depth perception. The custom- built end-effector for the iSYS robot is transparent to using surgical microscopes and compatible with magnetic tracking. The cadaver experiment showed that guidance was accurate and that the end-effector is unobtrusive. This laser guidance has potential to aid the surgeon in finding the optimal mastoidectomy trajectory in more difficult interventions.

Purpose Video see-through augmented reality (VST-AR) navigation for laparoscopic partial nephrectomy (LPN) can enhance intraoperative perception of surgeons by visualizing surgical targets and critical structures of the kidney tissue. Image registration is the main challenge in the procedure. Existing registration methods in laparoscopic navigation systems suffer from limitations such as manual alignment, invasive external marker fixation, relying on external tracking devices with bulky tracking sensors and lack of deformation compensation. To address these issues, we present a markerless automatic deformable registration framework for LPN VST-AR navigation. Dense stereo matching and 3D reconstruction, automatic segmentation and surface stitching are combined to obtain a larger dense intraoperative point cloud of the renal surface. A coarse-to-fine deformable registration is performed to achieve a precise automatic registration between the intraoperative point cloud and the preoperative model using the iterative closest point algorithm followed by the coherent point drift algorithm. Kidney phantom experiments and in vivo experiments were performed to evaluate the accuracy and effectiveness of our approach. The average segmentation accuracy rate of the automatic segmentation was 94.9%. The mean target registration error of the phantom experiments was found to be 1.28 ± 0.68 mm (root mean square error). In vivo experiments showed that tumor location was identified successfully by superimposing the tumor model on the laparoscopic view. Experimental results have demonstrated that the proposed framework could accurately overlay comprehensive preoperative models on deformable soft organs automatically in a manner of VST-AR without using extra intraoperative imaging modalities and external tracking devices, as well as its potential clinical use.

Image registration lies in the core of augmented reality (AR), which aligns the virtual scene with the reality. In AR surgical navigation, the performance of image registration is vital to the surgical outcome. This paper presents a practical marker-less image registration method for AR-guided oral and maxillofacial surgery where a virtual scene is generated and mixed with reality to guide surgical operation or provide surgical outcome visualization in the manner of video see-through overlay. An intraoral 3D scanner is employed to acquire the patient’s teeth shape model intraoperatively. The shape model is then registered with a custom-made stereo camera system using a novel 3D stereo matching algorithm and with the patient’s CT-derived 3D model using an iterative closest point scheme, respectively. By leveraging the intraoral 3D scanner, the CT space and the stereo camera space are associated so that surrounding anatomical models and virtual implants could be overlaid on the camera’s view to achieve AR surgical navigation. Jaw phantom experiments were performed to evaluate the target registration error of the overlay, which yielded an average error of less than 0.50 mm with the time cost less than 0.5 s. Volunteer trial was also conducted to show the clinical feasibility. The proposed registration method does not rely on any external fiducial markers attached to the patient. It performs automatically so as to maintain a correct AR scene, overcoming the misalignment difficulty caused by patient’s movement. Therefore, it is noninvasive and practical in oral and maxillofacial surgery.

Purpose The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D–2D global registration in laparoscopic liver interventions. Methods Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. Results We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. Conclusions Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration.

Intra-operative augmented reality (AR) during surgery can mitigate incomplete cancer removal by overlaying the anatomical boundaries extracted from medical imaging data onto the camera image. In this paper, we present the first such completely markerless AR guidance system for robot-assisted laparoscopic radical prostatectomy (RALRP) that transforms medical data from transrectal ultrasound (TRUS) to endoscope camera image. Moreover, we reduce the total number of transformations by combining the hand–eye and camera calibrations in a single step. Our proposed solution requires two transformations: TRUS to robot, $$^\mathrm{DV}T_\mathrm{TRUS}$$ , and camera projection matrix, $$\mathbf{M} $$ (i.e., the transformation from endoscope to camera image frame). $$^\mathrm{DV}T_\mathrm{TRUS}$$ is estimated by the method proposed in Mohareri et al. (in J Urol 193(1):302–312, 2015). $$\mathbf{M} $$ is estimated by selecting corresponding 3D-2D data points in the endoscope and the image coordinate frame, respectively, by using a CAD model of the surgical instrument and a preoperative camera intrinsic matrix with an assumption of a projective camera. The parameters are estimated using Levenberg–Marquardt algorithm. Overall mean re-projection errors (MRE) are reported using simulated and real data using a water bath. We show that $$\mathbf{M} $$ can be re-estimated if the focus is changed during surgery. Using simulated data, we received an overall MRE in the range of 11.69–13.32 pixels for monoscopic and stereo left and right cameras. For the water bath experiment, the overall MRE is in the range of 26.04–30.59 pixels for monoscopic and stereo cameras. The overall system error from TRUS to camera world frame is 4.05 mm. Details of the procedure are given in supplementary material. We demonstrate a markerless AR guidance system for RALRP that does not need calibration markers and thus has the capability to re-estimate the camera projection matrix if it changes during surgery, e.g., due to a focus change.

Purpose Effective training of extracorporeal membrane oxygenation (ECMO) cannulation is key to fighting the persistently high mortality rate of ECMO interventions. Though augmented reality (AR) is a promising technology for improving information display, only a small percentage of AR projects have addressed training procedures. The present study investigates the potential benefits of AR-based, contextual instructions for ECMO cannulation training as compared to instructions used during conventional training at a university hospital. Methodology An AR step-by-step guide was developed for the Microsoft HoloLens 2 that combines text, images, and videos from the conventional training program with simple 3D models. A study was conducted with 21 medical students performing two surgical procedures on a simulator. Participants were divided into two groups, with one group using the conventional instructions for the first procedure and AR instructions for the second and the other group using instructions in reverse order. Training times, a detailed error protocol, and a standardized user experience questionnaire (UEQ) were evaluated. Results AR-based execution was associated with slightly higher training times and with significantly fewer errors for the more complex second procedure (p, International Journal of Computer Assisted Radiology and Surgery, 16 (6), ISSN:1861-6410, ISSN:1861-6429

The visualization of remote surgical scenes is the key to realizing the remote operation of surgical robots. However, current non-endoscopic surgical robot systems lack an effective visualization tool to offer sufficient surgical scene information and depth perception. We propose a novel autostereoscopic surgical visualization system integrating 3D intraoperative scene reconstruction, autostereoscopic 3D display, and augmented reality-based image fusion. The preoperative organ structure and the intraoperative surface point cloud are obtained from medical imaging and the RGB-D camera, respectively, and aligned by an automatic marker-free intraoperative registration algorithm. After registration, preoperative meshes with precalculated illumination and intraoperative textured point cloud are blended in real time. Finally, the fused image is shown on a 3D autostereoscopic display device to achieve depth perception. A prototype of the autostereoscopic surgical visualization system was built. The system had a horizontal image resolution of 1.31 mm, a vertical image resolution of 0.82 mm, an average rendering rate of 33.1 FPS, an average registration rate of 20.5 FPS, and average registration errors of approximately 3 mm. A telesurgical robot prototype based on 3D autostereoscopic display was built. The quantitative evaluation experiments showed that our system achieved similar operational accuracy (1.79 ± 0.87 mm) as the conventional system (1.95 ± 0.71 mm), while having advantages in terms of completion time (with 34.11% reduction) and path length (with 35.87% reduction). Post-experimental questionnaires indicated that the system was user-friendly for novices and experts. We propose a 3D surgical visualization system with augmented instruction and depth perception for telesurgery. The qualitative and quantitative evaluation results illustrate the accuracy and efficiency of the proposed system. Therefore, it shows great prospects in robotic surgery and telesurgery.

Augmented reality (AR) technology improves the learning process in interventional radiology. This study hypothesized that using AR to train for central venous access is superior to using ultrasound alone. This study used an AR central venous catheterization phantom with an internal jugular vein (IJV) and subclavian vein (SCV) made of resin body and soft tubing. Ten radiologists attempted to punctuate, using needle placement simulation, under three conditions (ultrasound-, augmented reality-, and ultrasound and AR-guided methods; US-only, AR-only, and US+AR, respectively) using a smart-glass device (HoloLens, Microsoft, Redmond, WA, USA). Subjective (anatomical understanding and self-confidence for procedure) and objective evaluations (optimized needle position and time) were recorded for each condition. The subjective IJV evaluation showed no difference among the guiding methods (p = 0.26 and p = 0.07 for anatomical understanding and self-confidence for procedure, respectively). Conversely, there were significant improvements in subjective and objective evaluations for SCV using the AR-only and US+AR methods (p

For augmented reality surgical navigation based on C-arm imaging, accuracy of the overlaid augmented reality onto the X-ray image is imperative. However, overlay displacement is generated when a conventional pinhole model describing a geometric relationship of a normal camera is adopted for C-arm calibration. Thus, a modified model for C-arm calibration is proposed to reduce this displacement, which is essential for accurate surgical navigation. Based on the analysis of displacement pattern generated for three-dimensional objects, we assumed that displacement originated by moving the X-ray source position according to the depth. In the proposed method, X-ray source movement was modeled as variable intrinsic parameters and represented in the pinhole model by replacing the point source with a planar source. The improvement which represents a reduced displacement was verified by comparing overlay accuracy for augmented reality surgical navigation between the conventional and proposed methods. The proposed method achieved more accurate overlay on the X-ray image in spatial position as well as depth of the object volume. We validated that intrinsic parameters that describe the source position were dependent on depth for a three-dimensional object and showed that displacement can be reduced and become independent of depth by using the proposed planar source model.

Purpose Successful use of augmented reality for laparoscopic surgery requires that the surgeon has a thorough understanding of the likely accuracy of any overlay. Whilst the accuracy of such systems can be estimated in the laboratory, it is difficult to extend such methods to the in vivo clinical setting. Herein we describe a novel method that enables the surgeon to estimate in vivo errors during use. We show that the method enables quantitative evaluation of in vivo data gathered with the SmartLiver image guidance system. Methods The SmartLiver system utilises an intuitive display to enable the surgeon to compare the positions of landmarks visible in both a projected model and in the live video stream. From this the surgeon can estimate the system accuracy when using the system to locate subsurface targets not visible in the live video. Visible landmarks may be either point or line features. We test the validity of the algorithm using an anatomically representative liver phantom, applying simulated perturbations to achieve clinically realistic overlay errors. We then apply the algorithm to in vivo data. Results The phantom results show that using projected errors of surface features provides a reliable predictor of subsurface target registration error for a representative human liver shape. Applying the algorithm to in vivo data gathered with the SmartLiver image-guided surgery system shows that the system is capable of accuracies around 12 mm; however, achieving this reliably remains a significant challenge. Conclusion We present an in vivo quantitative evaluation of the SmartLiver image-guided surgery system, together with a validation of the evaluation algorithm. This is the first quantitative in vivo analysis of an augmented reality system for laparoscopic surgery.

We present a novel augmented reality (AR) surgical navigation system based on ultrasound-assisted registration for pedicle screw placement. This system provides the clinically desired targeting accuracy and reduces radiation exposure. Ultrasound (US) is used to perform registration between preoperative computed tomography (CT) images and patient, and the registration is performed by least-squares fitting of these two three-dimensional (3D) point sets of anatomical landmarks taken from US and CT images. An integral videography overlay device is calibrated to accurately display naked-eye 3D images for surgical navigation. We use a 3.0-mm Kirschner wire (K-wire) instead of a pedicle screw in this study, and the K-wire is calibrated to obtain its orientation and tip location. Based on the above registration and calibration, naked-eye 3D images of the planning path and the spine are superimposed onto patient in situ using our AR navigation system. Simultaneously, a 3D image of the K-wire is overlaid accurately on the real one to guide the insertion procedure. The targeting accuracy is evaluated postoperatively by performing a CT scan. An agar phantom experiment was performed. Eight K-wires were inserted successfully after US-assisted registration, and the mean targeting error and angle error were 3.35 mm and $$2.74{^{\circ }}$$ , respectively. Furthermore, an additional sheep cadaver experiment was performed. Four K-wires were inserted successfully. The mean targeting error was 3.79 mm and the mean angle error was $$4.51{^{\circ }}$$ , and US-assisted registration yielded better targeting results than skin markers-based registration (targeting errors: 2.41 vs. 5.18 mm, angle errors: $$3.13{^{\circ }}$$ vs. $$5.89{^{\circ }})$$ . Experimental outcomes demonstrate that the proposed navigation system has acceptable targeting accuracy. In particular, the proposed navigation method reduces repeated radiation exposure to the patient and surgeons. Therefore, it has promising prospects for clinical use.

Using existing stereoelectroencephalography (SEEG) electrode implantation surgical robot systems, it is difficult to intuitively validate registration accuracy and display the electrode entry points (EPs) and the anatomical structure around the electrode trajectories in the patient space to the surgeon. This paper proposes a prototype system that can realize video see-through augmented reality (VAR) and spatial augmented reality (SAR) for SEEG implantation. The system helps the surgeon quickly and intuitively confirm the registration accuracy, locate EPs and visualize the internal anatomical structure in the image space and patient space. We designed and developed a projector-camera system (PCS) attached to the distal flange of a robot arm. First, system calibration is performed. Second, the PCS is used to obtain the point clouds of the surface of the patient’s head, which are utilized for patient-to-image registration. Finally, VAR is produced by merging the real-time video of the patient and the preoperative three-dimensional (3D) operational planning model. In addition, SAR is implemented by projecting the planning electrode trajectories and local anatomical structure onto the patient’s scalp. The error of registration, the electrode EPs and the target points are evaluated on a phantom. The fiducial registration error is $$0.25 \pm 0.23$$ mm (max 1.22 mm), and the target registration error is $$0.62\pm 0.28$$ mm (max 1.18 mm). The projection overlay error is $$0.75\pm 0.52$$ mm, and the TP error after the pre-warped projection is $$0.82\pm 0.23$$ mm. The TP error caused by a surgeon’s viewpoint deviation is also evaluated. The presented system can help surgeons quickly verify registration accuracy during SEEG procedures and can provide accurate EP locations and internal structural information to the surgeon. With more intuitive surgical information, the surgeon may have more confidence and be able to perform surgeries with better outcomes.

Purpose Minimally invasive surgery offers advantages over open surgery due to a shorter recovery time, less pain and trauma for the patient. However, inherent challenges such as lack of tactile feedback and difficulty in controlling bleeding lower the percentage of suitable cases. Augmented reality can show a better visualisation of sub-surface structures and tumour locations by fusing pre-operative CT data with real-time laparoscopic video. Such augmented reality visualisation requires a fast and robust video to CT registration that minimises interruption to the surgical procedure. Methods We propose to use view planning for efficient rigid registration. Given the trocar position, a set of camera positions are sampled and scored based on the corresponding liver surface properties. We implement a simulation framework to validate the proof of concept using a segmented CT model from a human patient. Furthermore, we apply the proposed method on clinical data acquired during a human liver resection. Results The first experiment motivates the viewpoint scoring strategy and investigates reliable liver regions for accurate registrations in an intuitive visualisation. The second experiment shows wider basins of convergence for higher scoring viewpoints. The third experiment shows that a comparable registration performance can be achieved by at least two merged high scoring views and four low scoring views. Hence, the focus could change from the acquisition of a large liver surface to a small number of distinctive patches, thereby giving a more explicit protocol for surface reconstruction. We discuss the application of the proposed method on clinical data and show initial results. Conclusion The proposed simulation framework shows promising results to motivate more research into a comprehensive view planning method for efficient registration in laparoscopic liver surgery.

International audience; Purpose: Locating the internal structures of an organ is a critical aspect of many surgical procedures. Minimally invasive surgery, associated with augmented reality techniques, offers the potential to visualize inner structures, allowing for improved analysis, depth perception or for supporting planning and decision systems.Methods: Most of the current methods dealing with rigid or non-rigid augmented reality make the assumption that the topology of the organ is not modified. As surgery relies essentially on cutting and dissection of anatomical structures, such methods are limited to the early stages of the surgery.We solve this shortcoming with the introduction of a method for physics-based elastic registration using a single view from a monocular camera.Singularities caused by topological changes are detected and propagated to the pre-operative model. This significantly improves the coherence between the actual laparoscopic view and the model, and provides added value in terms of navigation and decision-making, e.g. by overlaying the internal structures of an organ on the laparoscopic view.Results: Our real time augmentation method is assessed on several scenarios, using synthetic objects and real organs. In all cases, the impact of our approach is demonstrated, both qualitatively and quantitatively.Conclusion: The presented approach tackles the challenge of localizing internal structures throughout a complete surgical procedure, even after surgical cuts. This information is crucial for surgeons to improve the outcome for their surgical procedure and avoid complications.

International audience; Purpose: Surgical treatments for low rectal cancer require carefulconsiderations due to the low location of cancer in rectums. Successful sur-gical outcomes highly depend on surgeons' ability to determine clear distalmargins of rectal tumors. This is a challenge for surgeons in robot-assistedlaparoscopic surgery, since tumors are often concealed in rectums and roboticsurgical instruments do not provide tactile feedback for tissue diagnosis inreal-time. This paper presents the development and evaluation of an intraop-erative ultrasound-based augmented reality framework for surgical guidancein robot-assisted rectal surgery. Methods: Framework implementation consistsin calibrating the transrectal ultrasound (TRUS) and the endoscopic camera(hand-eye calibration), generating a virtual model and registering it to theendoscopic image via optical tracking, and displaying the augmented view ona head-mounted display. An experimental validation setup is designed to eval-uate the framework. Results: The evaluation process yields a mean error of0:9 mm for the TRUS calibration, a maximum error of 0:51 mm for the hand-eye calibration of endoscopic cameras, and a maximum RMS error of 0:8 mmfor the whole framework. In the experiment with a rectum phantom, our frame-work guides the surgeon to accurately localize the simulated tumor and thedistal resection margin. Conclusion: This framework is developed with ourclinical partner, based on actual clinical conditions. The experimental proto-col and the high level of accuracy show the feasibility of seamlessly integratingthis framework within the surgical workow.

In spinal fusion surgery, imprecise placement of pedicle screws can result in poor surgical outcome or may seriously harm a patient. Patient-specific instruments and optical systems have been proposed for improving precision through surgical navigation compared to freehand insertion. However, existing solutions are expensive and cannot provide in situ visualizations. Recent technological advancement enabled the production of more powerful and precise optical see-through head-mounted displays for the mass market. The purpose of this laboratory study was to evaluate whether such a device is sufficiently precise for the navigation of lumbar pedicle screw placement. A novel navigation method, tailored to run on the Microsoft HoloLens, was developed. It comprises capturing of the intraoperatively reachable surface of vertebrae to achieve registration and tool tracking with real-time visualizations without the need of intraoperative imaging. For both surface sampling and navigation, 3D printable parts, equipped with fiducial markers, were employed. Accuracy was evaluated within a self-built setup based on two phantoms of the lumbar spine. Computed tomography (CT) scans of the phantoms were acquired to carry out preoperative planning of screw trajectories in 3D. A surgeon placed the guiding wire for the pedicle screw bilaterally on ten vertebrae guided by the navigation method. Postoperative CT scans were acquired to compare trajectory orientation (3D angle) and screw insertion points (3D distance) with respect to the planning. The mean errors between planned and executed screw insertion were $$3.38^{\circ }\pm {1.73}^{\circ }$$ for the screw trajectory orientation and 2.77±1.46 mm for the insertion points. The mean time required for surface digitization was 125±27 s. First promising results under laboratory conditions indicate that precise lumbar pedicle screw insertion can be achieved by combining HoloLens with our proposed navigation method. As a next step, cadaver experiments need to be performed to confirm the precision on real patient anatomy.

Image-guided percutaneous interventions are safer alternatives to conventional orthopedic and trauma surgeries. To advance surgical tools in complex bony structures during these procedures with confidence, a large number of images is acquired. While image-guidance is the de facto standard to guarantee acceptable outcome, when these images are presented on monitors far from the surgical site the information content cannot be associated easily with the 3D patient anatomy. In this article, we propose a collaborative augmented reality (AR) surgical ecosystem to jointly co-localize the C-arm X-ray and surgeon viewer. The technical contributions of this work include (1) joint calibration of a visual tracker on a C-arm scanner and its X-ray source via a hand-eye calibration strategy, and (2) inside-out co-localization of human and X-ray observers in shared tracking and augmentation environments using vision-based simultaneous localization and mapping. We present a thorough evaluation of the hand-eye calibration procedure. Results suggest convergence when using 50 pose pairs or more. The mean translation and rotation errors at convergence are 5.7 mm and $$0.26^\circ $$ , respectively. Further, user-in-the-loop studies were conducted to estimate the end-to-end target augmentation error. The mean distance between landmarks in real and virtual environment was 10.8 mm. The proposed AR solution provides a shared augmented experience between the human and X-ray viewer. The collaborative surgical AR system has the potential to simplify hand-eye coordination for surgeons or intuitively inform C-arm technologists for prospective X-ray view-point planning.

In the past decade, augmented reality systems have been expected to support surgical operations by making it possible to view invisible objects that are inside or occluded by the skull, hands, or organs. However, the properties of biological tissues that are non-rigid and featureless require a large number of distributed features to track the movement of tissues in detail. With the goal of increasing the number of feature points in organ tracking, we propose a feature detection using multi-band and narrow-band imaging and a new band selection method. The depth of light penetration into an object depends on the wavelength of light based on optical characteristics. We applied typical feature detectors to detect feature points using three selected bands in a human hand. To consider surgical situations, we applied our method to a chicken liver with a variety of light conditions. Our experimental results revealed that the image of each band exhibited a different distribution of feature points. In addition, the total number of feature points determined by the proposed method exceeded that of the R, G, and B images obtained using a normal camera. The results using a chicken liver with various light sources and intensities also show different distributions with each selected band. We have proposed a feature detection method using multi-band and narrow-band imaging and a band selection method. The results of our experiments confirmed that the proposed method increased the number of distributed feature points. The proposed method was also effective for different light conditions.

An augmented reality system to visualize a 3D preoperative anatomical model on intra-operative patient is proposed. The hardware requirement is commercial tablet-PC equipped with a camera. Thus, no external tracking device nor artificial landmarks on the patient are required. We resort to visual SLAM to provide markerless real-time tablet-PC camera location with respect to the patient. The preoperative model is registered with respect to the patient through 4–6 anchor points. The anchors correspond to anatomical references selected on the tablet-PC screen at the beginning of the procedure. Accurate and real-time preoperative model alignment (approximately 5-mm mean FRE and TRE) was achieved, even when anchors were not visible in the current field of view. The system has been experimentally validated on human volunteers, in vivo pigs and a phantom. The proposed system can be smoothly integrated into the surgical workflow because it: (1) operates in real time, (2) requires minimal additional hardware only a tablet-PC with camera, (3) is robust to occlusion, (4) requires minimal interaction from the medical staff.

Common camera calibration methods employed in current laparoscopic augmented reality systems require the acquisition of multiple images of an entire checkerboard pattern from various poses. This lengthy procedure prevents performing laparoscope calibration in the operating room (OR). The purpose of this work was to develop a fast calibration method for electromagnetically (EM) tracked laparoscopes, such that the calibration can be performed in the OR on demand. We designed a mechanical tracking mount to uniquely and snugly position an EM sensor to an appropriate location on a conventional laparoscope. A tool named fCalib was developed to calibrate intrinsic camera parameters, distortion coefficients, and extrinsic parameters (transformation between the scope lens coordinate system and the EM sensor coordinate system) using a single image that shows an arbitrary portion of a special target pattern. For quick evaluation of calibration results in the OR, we integrated a tube phantom with fCalib prototype and overlaid a virtual representation of the tube on the live video scene. We compared spatial target registration error between the common OpenCV method and the fCalib method in a laboratory setting. In addition, we compared the calibration re-projection error between the EM tracking-based fCalib and the optical tracking-based fCalib in a clinical setting. Our results suggest that the proposed method is comparable to the OpenCV method. However, changing the environment, e.g., inserting or removing surgical tools, might affect re-projection accuracy for the EM tracking-based approach. Computational time of the fCalib method averaged 14.0 s (range 3.5 s–22.7 s). We developed and validated a prototype for fast calibration and evaluation of EM tracked conventional (forward viewing) laparoscopes. The calibration method achieved acceptable accuracy and was relatively fast and easy to be performed in the OR on demand.

This work proposes a novel algorithm to register cone-beam computed tomography (CBCT) volumes and 3D optical (RGBD) camera views. The co-registered real-time RGBD camera and CBCT imaging enable a novel augmented reality solution for orthopedic surgeries, which allows arbitrary views using digitally reconstructed radiographs overlaid on the reconstructed patient’s surface without the need to move the C-arm. An RGBD camera is rigidly mounted on the C-arm near the detector. We introduce a calibration method based on the simultaneous reconstruction of the surface and the CBCT scan of an object. The transformation between the two coordinate spaces is recovered using Fast Point Feature Histogram descriptors and the Iterative Closest Point algorithm. Several experiments are performed to assess the repeatability and the accuracy of this method. Target registration error is measured on multiple visual and radio-opaque landmarks to evaluate the accuracy of the registration. Mixed reality visualizations from arbitrary angles are also presented for simulated orthopedic surgeries. To the best of our knowledge, this is the first calibration method which uses only tomographic and RGBD reconstructions. This means that the method does not impose a particular shape of the phantom. We demonstrate a marker-less calibration of CBCT volumes and 3D depth cameras, achieving reasonable registration accuracy. This design requires a one-time factory calibration, is self-contained, and could be integrated into existing mobile C-arms to provide real-time augmented reality views from arbitrary angles.

In many orthopedic surgeries, there is a demand for correctly placing medical instruments (e.g., K-wire or drill) to perform bone fracture repairs. The main challenge is the mental alignment of X-ray images acquired using a C-arm, the medical instruments, and the patient, which dramatically increases in complexity during pelvic surgeries. Current solutions include the continuous acquisition of many intra-operative X-ray images from various views, which will result in high radiation exposure, long surgical durations, and significant effort and frustration for the surgical staff. This work conducts a preclinical usability study to test and evaluate mixed reality visualization techniques using intra-operative X-ray, optical, and RGBD imaging to augment the surgeon’s view to assist accurate placement of tools. We design and perform a usability study to compare the performance of surgeons and their task load using three different mixed reality systems during K-wire placements. The three systems are interventional X-ray imaging, X-ray augmentation on 2D video, and 3D surface reconstruction augmented by digitally reconstructed radiographs and live tool visualization. The evaluation criteria include duration, number of X-ray images acquired, placement accuracy, and the surgical task load, which are observed during 21 clinically relevant interventions performed by surgeons on phantoms. Finally, we test for statistically significant improvements and show that the mixed reality visualization leads to a significantly improved efficiency. The 3D visualization of patient, tool, and DRR shows clear advantages over the conventional X-ray imaging and provides intuitive feedback to place the medical tools correctly and efficiently.

Purpose Laparoscopic liver resection has significant advantages over open surgery due to less patient trauma and faster recovery times, yet it can be difficult due to the restricted field of view and lack of haptic feedback. Image guidance provides a potential solution but one current challenge is in accurate “hand–eye” calibration, which determines the position and orientation of the laparoscope camera relative to the tracking markers. Methods In this paper, we propose a simple and clinically feasible calibration method based on a single invariant point. The method requires no additional hardware, can be constructed by theatre staff during surgical setup, requires minimal image processing and can be visualised in real time. Real-time visualisation allows the surgical team to assess the calibration accuracy before use in surgery. In addition, in the laboratory, we have developed a laparoscope with an electromagnetic tracking sensor attached to the camera end and an optical tracking marker attached to the distal end. This enables a comparison of tracking performance. Results We have evaluated our method in the laboratory and compared it to two widely used methods, “Tsai’s method” and “direct” calibration. The new method is of comparable accuracy to existing methods, and we show RMS projected error due to calibration of 1.95 mm for optical tracking and 0.85 mm for EM tracking, versus 4.13 and 1.00 mm respectively, using existing methods. The new method has also been shown to be workable under sterile conditions in the operating room. Conclusion We have proposed a new method of hand–eye calibration, based on a single invariant point. Initial experience has shown that the method provides visual feedback, satisfactory accuracy and can be performed during surgery. We also show that an EM sensor placed near the camera would provide significantly improved image overlay accuracy.

Knowledge of the specific anatomical information of a patient is important when planning and undertaking laparoscopic surgery due to the restricted field of view and lack of tactile feedback compared to open surgery. To assist this type of surgery, we have developed a surgical navigation system that presents the patient’s anatomical information synchronized with the laparoscope position. This paper presents the surgical navigation system and its clinical application to laparoscopic gastrectomy for gastric cancer. The proposed surgical navigation system generates virtual laparoscopic views corresponding to the laparoscope position recorded with a three-dimensional (3D) positional tracker. The virtual laparoscopic views are generated from preoperative CT images. A point-based registration aligns coordinate systems between the patient’s anatomy and image coordinates. The proposed navigation system is able to display the virtual laparoscopic views using the registration result during surgery. We performed surgical navigation during laparoscopic gastrectomy in 23 cases. The navigation system was able to present the virtual laparoscopic views in synchronization with the laparoscopic position. The fiducial registration error was calculated in all 23 cases, and the average was 14.0 mm (range 6.1–29.8). The proposed surgical navigation system can provide CT-derived patient anatomy aligned to the laparoscopic view in real time during surgery. This system enables accurate identification of vascular anatomy as a guide to vessel clamping prior to total or partial gastrectomy.

Augmented reality for monocular laparoscopy from a preoperative volume such as CT is achieved in two steps. The first step is to segment the organ in the preoperative volume and reconstruct its 3D model. The second step is to register the preoperative 3D model to an initial intraoperative laparoscopy image. To date, there does not exist an automatic initial registration method to solve the second step for the liver in the de facto operating room conditions of monocular laparoscopy. Existing methods attempt to solve for both deformation and pose simultaneously, leading to nonconvex problems with no optimal solution algorithms. We propose in contrast to break the problem down into two parts, solving for (i) deformation and (ii) pose. Part (i) simulates biomechanical deformations from the preoperative to the intraoperative state to predict the liver’s unknown intraoperative shape by modeling gravity, the abdominopelvic cavity’s pressure and boundary conditions. Part (ii) rigidly registers the simulated shape to the laparoscopy image using contour cues. Our formulation leads to a well-posed problem, contrary to existing methods. This is because it exploits strong environment priors to complement the weak laparoscopic visual cues. Quantitative results with in silico and phantom experiments and qualitative results with laparosurgery images for two patients show that our method outperforms the state-of-the-art in accuracy and registration time.

In neurosurgery, an image-guided operation is performed to confirm that the surgical instruments reach the exact lesion position. Among the multiple imaging modalities, an X-ray fluoroscope mounted on C- or O-arm is widely used for monitoring the position of surgical instruments and the target position of the patient. However, frequently used fluoroscopy can result in relatively high radiation doses, particularly for complex interventional procedures. The proposed system can reduce radiation exposure and provide the accurate three-dimensional (3D) position information of surgical instruments and the target position. X-ray and optical stereo vision systems have been proposed for the C- or O-arm. Two subsystems have same optical axis and are calibrated simultaneously. This provides easy augmentation of the camera image and the X-ray image. Further, the 3D measurement of both systems can be defined in a common coordinate space. The proposed dual stereoscopic imaging system is designed and implemented for mounting on an O-arm. The calibration error of the 3D coordinates of the optical stereo and X-ray stereo is within 0.1 mm in terms of the mean and the standard deviation. Further, image augmentation with the camera image and the X-ray image using an artificial skull phantom is achieved. As the developed dual stereoscopic imaging system provides 3D coordinates of the point of interest in both optical images and fluoroscopic images, it can be used by surgeons to confirm the position of surgical instruments in a 3D space with minimum radiation exposure and to verify whether the instruments reach the surgical target observed in fluoroscopic images.

To design an augmented reality solution that assists surgeons during the distal locking of intramedullary nailing procedures.Traditionally, the procedure is performed under X-ray guidance and requires a significant amount of time and radiation exposure. To absolve these complications, we propose video guidance that allows surgeons to achieve both the down-the-beam position of the intramedullary nail and its subsequent locking. For the down-the-beam position, the IM nail pose in X-ray is calculated using a 2D/3D registration scheme and later related to the patient leg pose which is calculated using video-tracked AR markers. For the distal locking, surgeons use an augmented radiolucent drill in which its tip position is detected and tracked in real-time under video guidance.To evaluate the feasibility of our solution, we performed a preclinical study on dry bone phantom with the participation of four clinicians.Participants achieved 100 % success rate in the down-the beam positioning and 93 % success rate in distal locking using only two X-ray images in 100 s.We confirmed that intra-operative navigation using augmented reality provides an alternative way to perform distal locking in a safe and timely manner.

Surgical staff performing image-guided minimally invasive surgical procedures are chronically exposed to harmful ionizing radiation. Currently, no means exist to intraoperatively depict the 3D shape and intensity of scattered radiation fields or to assess the body-part exposure of clinicians. We propose a system for simulating and visualizing intraoperative scattered radiation using augmented reality.We use a multi-camera RGBD system to obtain a 3D point cloud reconstruction of the current room layout. The positions of the clinicians, patient, table and C-arm are used to build a radiation propagation simulation model and compute the deposited dose distribution in the room. We use wireless dosimeters to calibrate the simulation and to evaluate its accuracy at each time step. The computed 3D risk map is shown in an augmented reality manner by overlaying the simulation results onto the 3D model.Several 3D visualizations showing scattered radiation propagation, clinicians' body-part exposure and radiation risk maps under different irradiation conditions are proposed. The system is evaluated in an operating room equipped with a robotized X-ray imaging device by comparing the radiation simulation results to experimental measurements under several X-ray acquisition setups and room configurations.The proposed system is capable to display intraoperative scattered radiation intuitively in 3D by using augmented reality. This can have a strong impact on improving clinicians' awareness of their exposure to ionizing radiation and on reducing overexposure risks.

The aim of this report is to present a prototype augmented reality (AR) intra-operative brain imaging system. We present our experience of using this new neuronavigation system in neurovascular surgery and discuss the feasibility of this technology for aneurysms, arteriovenous malformations (AVMs), and arteriovenous fistulae (AVFs). We developed an augmented reality system that uses an external camera to capture the live view of the patient on the operating room table and to merge this view with pre-operative volume-rendered vessels. We have extensively tested the system in the laboratory and have used the system in four surgical cases: one aneurysm, two AVMs and one AVF case. The developed AR neuronavigation system allows for precise patient-to-image registration and calibration of the camera, resulting in a well-aligned augmented reality view. Initial results suggest that augmented reality is useful for tailoring craniotomies, localizing vessels of interest, and planning resection corridors. Augmented reality is a promising technology for neurovascular surgery. However, for more complex anomalies such as AVMs and AVFs, better visualization techniques that allow one to distinguish between arteries and veins and determine the absolute depth of a vessel of interest are needed.

Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves “blind” identification of the vertebral level through manual palpation of the spine, which has only 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion. A real-time system was developed to identify the vertebral level from a sequence of ultrasound images, following a clinical imaging protocol. The system uses a deep convolutional neural network (CNN) to classify transverse images of the lower spine. Several existing CNN architectures were implemented, utilizing transfer learning, and compared for adequacy in a real-time system. In the system, the CNN output is processed, using a novel state machine, to automatically identify vertebral levels as the transducer moves up the spine. Additionally, a graphical display was developed and integrated within 3D Slicer. Finally, an augmented reality display, projecting the level onto the patient’s back, was also designed. A small feasibility study $$(n=20)$$ evaluated performance. The proposed CNN successfully discriminates ultrasound images of the sacrum, intervertebral gaps, and vertebral bones, achieving 88% 20-fold cross-validation accuracy. Seventeen of 20 test ultrasound scans had successful identification of all vertebral levels, processed at real-time speed (40 frames/s). A machine learning system is presented that successfully identifies lumbar vertebral levels. The small study on human subjects demonstrated real-time performance. A projection-based augmented reality display was used to show the vertebral level directly on the subject adjacent to the puncture site.

Introducing computational methods to laser surgery are an emerging field. Focusing on endoscopic laser interventions, a novel approach is presented to enhance intraoperative incision planning and laser focusing by means of tissue surface information obtained by stereoscopic vision. Tissue surface is estimated with stereo-based methods using nonparametric image transforms. Subsequently, laser-to-camera registration is obtained by ablating a pattern on tissue substitutes and performing a principle component analysis for precise laser axis estimation. Furthermore, a virtual laser view is computed utilizing trifocal transfer. Depth-based laser focus adaptation is integrated into a custom experimental laser setup in order to achieve optimal ablation morphology. Experimental validation is conducted on tissue substitutes and ex vivo animal tissue. Laser-to-camera registration gives an error between planning and ablation of less than 0.2 mm. As a result, the laser workspace can accurately be highlighted within the live views and incision planning can directly be performed. Experiments related to laser focus adaptation demonstrate that ablation geometry can be kept almost uniform within a depth range of 7.9 mm, whereas cutting quality significantly decreases when the laser is defocused. An automatic laser focus adjustment on tissue surfaces based on stereoscopic scene information is feasible and has the potential to become an effective methodology for optimal ablation. Laser-to-camera registration facilitates advanced surgical planning for prospective user interfaces and augmented reality extensions.

Large volumes of information in the OR are ignored by surgeons when the amount outpaces human mental processing abilities. We developed an augmented reality (AR) system for dental implant surgery that acts as an automatic information filter, selectively displaying only relevant information. The purpose is to reduce information overflow and offer intuitive image guidance. The system was evaluated in a pig cadaver experiment. Information filtering is implemented via rule-based situation interpretation with description logics. The interpretation is based on intraoperative distances measurement between anatomical structures and the dental drill with optical tracking. For AR, a head-mounted display is used, which was calibrated with a novel method based on SPAAM. To adapt to surgeon specific preferences, we offer two alternative display formats: one with static and another with contact analog AR. The system made the surgery easier and showed ergonomical benefits, as assessed by a questionnaire. All relevant phases were recognized reliably. The new calibration showed significant improvements, while the deviation of the realized implants was $$

PurposeThe augmented reality (AR) fluoroscope augments an X-ray image by video and provides the surgeon with a real-time in situ overlay of the anatomy. The overlay alignment is crucial for diagnostic and intra-operative guidance, so precise calibration of the AR fluoroscope is required. The first and most complex step of the calibration procedure is the determination of the X-ray source position. Currently, this is achieved using a biplane phantom with movable metallic rings on its top layer and fixed X-ray opaque markers on its bottom layer. The metallic rings must be moved to positions where at least two pairs of rings and markers are isocentric in the X-ray image. The current “trial and error” calibration process currently requires acquisition of many X-ray images, a task that is both time consuming and radiation intensive. An improved process was developed and tested for C-arm calibration.MethodsVideo guidance was used to drive the calibration procedure to minimize both X-ray exposure and the time involved. For this, a homography between X-ray and video images is estimated. This homography is valid for the plane at which the metallic rings are positioned and is employed to guide the calibration procedure. Eight users having varying calibration experience (i.e., 2 experts, 2 semi-experts, 4 novices) were asked to participate in the evaluation.ResultsThe video-guided technique reduced the number of intra-operative X-ray calibration images by 89 % and decreased the total time required by 59 %.ConclusionA video-based C-arm calibration method has been developed that improves the usability of the AR fluoroscope with a friendlier interface, reduced calibration time and clinically acceptable radiation doses. published

The use of projector-based augmented reality (AR) in surgery may enable surgeons to directly view anatomical models and surgical data from the patient’s surface (skin). It has the advantages of a consistent viewing focus on the patient, an extended field of view and augmented interaction. This paper presents an AR guidance mechanism with a projector-camera system to provide the surgeon with direct visual feedback for supervision of robotic needle insertion in radiofrequency (RF) ablation treatment. The registration of target organ models to specific positions on the patient body is performed using a surface-matching algorithm and point-based registration. An algorithm based on the extended Kalman filter and spatial transformation is used to intraoperatively compute the virtual needle’s depth in the patient’s body for AR display. Experiments of this AR system on a mannequin were conducted to evaluate AR visualization and accuracy of virtual RF needle insertion. The average accuracy of 1.86 mm for virtual needle insertion met the clinical requirement of 2 mm or better. The feasibility of augmented interaction with a surgical robot using the proposed open AR interface with active visual feedback was demonstrated. The experimental results demonstrate that this guidance system is effective in assisting a surgeon to perform a robot-assisted radiofrequency ablation procedure. The novelty of the work lies in establishing a navigational procedure for percutaneous surgical augmented intervention integrating a projection-based AR guidance and robotic implementation for surgical needle insertion.

Percutaneous nephrolithotomy (PCNL) plays an integral role in treatment of renal stones. Creating percutaneous renal access is the most important and challenging step in the procedure. To facilitate this step, we evaluated our novel mobile augmented reality (AR) system for its feasibility of use for PCNL.A tablet computer, such as an iPad[Formula: see text], is positioned above the patient with its camera pointing toward the field of intervention. The images of the tablet camera are registered with the CT image by means of fiducial markers. Structures of interest can be superimposed semi-transparently on the video images. We present a systematic evaluation by means of a phantom study. An urological trainee and two experts conducted 53 punctures on kidney phantoms.The trainee performed best with the proposed AR system in terms of puncturing time (mean: 99 s), whereas the experts performed best with fluoroscopy (mean: 59 s). iPad assistance lowered radiation exposure by a factor of 3 for the inexperienced physician and by a factor of 1.8 for the experts in comparison with fluoroscopy usage. We achieve a mean visualization accuracy of 2.5 mm.The proposed tablet computer-based AR system has proven helpful in assisting percutaneous interventions such as PCNL and shows benefits compared to other state-of-the-art assistance systems. A drawback of the system in its current state is the lack of depth information. Despite that, the simple integration into the clinical workflow highlights the potential impact of this approach to such interventions.

Navigation systems commonly used in neurosurgery suffer from two main drawbacks: (1) their accuracy degrades over the course of the operation and (2) they require the surgeon to mentally map images from the monitor to the patient. In this paper, we introduce the Intraoperative Brain Imaging System (IBIS), an open-source image-guided neurosurgery research platform that implements a novel workflow where navigation accuracy is improved using tracked intraoperative ultrasound (iUS) and the visualization of navigation information is facilitated through the use of augmented reality (AR). The IBIS platform allows a surgeon to capture tracked iUS images and use them to automatically update preoperative patient models and plans through fast GPU-based reconstruction and registration methods. Navigation, resection and iUS-based brain shift correction can all be performed using an AR view. IBIS has an intuitive graphical user interface for the calibration of a US probe, a surgical pointer as well as video devices used for AR (e.g., a surgical microscope). The components of IBIS have been validated in the laboratory and evaluated in the operating room. Image-to-patient registration accuracy is on the order of $$3.72\pm 1.27\,\hbox {mm}$$ and can be improved with iUS to a median target registration error of 2.54 mm. The accuracy of the US probe calibration is between 0.49 and 0.82 mm. The average reprojection error of the AR system is $$0.37\pm 0.19\,\hbox {mm}$$ . The system has been used in the operating room for various types of surgery, including brain tumor resection, vascular neurosurgery, spine surgery and DBS electrode implantation. The IBIS platform is a validated system that allows researchers to quickly bring the results of their work into the operating room for evaluation. It is the first open-source navigation system to provide a complete solution for AR visualization.