Alignment between real and virtual objects is a challenging task required for the deployment of Mixed Reality (MR) into manufacturing, medical, and construction applications. To face this challenge, a series of methods have been proposed. While many approaches use dynamic augmentations such as animations, arrows, or text to assist users, they require tracking the position of real objects. In contrast, when tracking of the real objects is not available or desired, alternative approaches use virtual replicas of real objects to allow for interactive, perceptual virtual-to-real, and/or real-to-virtual alignment. In these cases, the accuracy achieved strongly depends on the quality of the perceptual information provided to the user. This paper proposes a novel set of perceptual alignment concepts that go beyond the use of traditional visualization of virtual replicas, introducing the concept of COMPLEMENTARY TEXTURES to improve interactive alignment in MR applications. To showcase the advantages of using COMPLEMENTARY TEXTURES, we describe three different implementations that provide highly salient visual cues when misalignment is observed; or present semantic augmentations that, when combined with a real object, provide contextual information that can be used during the alignment process. The authors aim to open new paths for the community to explore rather than describing end-to-end solutions. The objective is to show the multitude of opportunities such concepts could provide for further research and development.

The interactive alignment of real and virtual content in AR is often non-trivial. Positional errors along the users view direction frequently lead to the misjudgment of the objects depth. This work takes advantage of alternative users' viewpoints in collaborative settings to mitigate these errors. Furthermore, we systematically restrict the parameters used to control the virtual contents pose and investigate the impact of sharing and disentangling such parameters. Results from this work show that alignment schemes that disentangle the control parameters improve overall alignment accuracy with a similar workload for the users and no significant increase in execution time.

Image stitching is a prominent challenge in medical imaging, where the limited field-of-view captured by single images prohibits holistic analysis of patient anatomy. The barrier that prevents straight-forward mosaicing of 2D images is depth mismatch due to parallax. In this work, we leverage the Fourier slice theorem to aggregate information from multiple transmission images in parallax-free domains using fundamental principles of X-ray image formation. The details of the stitched image are subsequently restored using a novel deep learning strategy that exploits similarity measures designed around frequency, as well as dense and sparse spatial image content. Our work provides evidence that reconstruction of orthographic mosaics is possible with realistic motions of the C-arm involving both translation and rotation. We also show that these orthographic mosaics enable metric measurements of clinically relevant quantities directly on the 2D image plane.

Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. As a consequence, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. The main contribution of this paper is to reveal how exemplary workflows are redefined by taking full advantage of head-mounted displays when entirely co-registered with the imaging system at all times. The awareness of the system from the geometric and physical characteristics of X-ray imaging allows the exploration of different human-machine interfaces. Our system achieved an error of 4.76 ± 2.91mm for placing K-wire in a fracture management procedure, and yielded errors of 1.57 ± 1.16° and 1.46 ± 1.00° in the abduction and anteversion angles, respectively, for total hip arthroplasty (THA). We compared the results with the outcomes from baseline standard operative and non-immersive AR procedures, which had yielded errors of [4.61mm, 4.76°, 4.77°] and [5.13mm, 1.78°, 1.43°], respectively, for wire placement, and abduction and anteversion during THA. We hope that our holistic approach towards improving the interface of surgery not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications and provide novel approaches of documenting procedures for training purposes.

We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries. The mathematical basis of our solution is based on the incorporation of attributes and characteristics that satisfy the properties of intrinsic and extrinsic symmetry and are robust to outliers. In the first step, feature points that satisfy intrinsic symmetry are automatically detected in the Möbius space defined on the CT data. These features are then pruned via a two-stage RANSAC to attain correspondences that satisfy also the extrinsic symmetry. Then, a disparity function based on Tukey's biweight robust estimator is introduced and minimized to identify a symmetry plane parametrization that yields maximum contralateral similarity. Finally, a novel regularization term is introduced to enhance similarity between bone density histograms across the partial symmetry plane, relying on the important biological observation that, even if injured, the dislocated bone segments remain within the body. Our extensive evaluations on various cases of common fracture types demonstrate the validity of the novel concepts and the accuracy of the proposed method.

Virtual Reality (VR) is rapidly increasing in popularity as a teaching tool. It allows for the creation of a highly immersive, three-dimensional virtual environment intended to simulate real-life environments. With more robots saturating the industry - from manufacturing to healthcare, there is a need to train end-users on how to set up, operate, tear down, and troubleshoot the robot. Even though VR has become widely used in training surgeons on the psychomotor skills associated with operating the robot, little research has been done to see how the benefits of VR could translate to teaching the bedside staff, tasked with supporting the robot during the full end-to-end surgical procedure. We trained 30 participants on how to set up a robotic arm in an environment mimicking clinical setup. We divided these participants equally into 3 groups with one group trained with paper-based instructions, one with video-based instructions and one with VR-based instructions. We then compared and contrasted these three different training methods. VR and paper-based were highly favored training mediums over video-based. VR-trained participants achieved slightly higher fidelity of individual robotic joint angles, suggesting better comprehension of the spatial awareness skills necessary to achieve desired arm positioning. In addition, VR resulted in higher reproducibility of setup fidelity and more consistency in user confidence levels as compared to paper and video-based training.

With recent advances of Virtual Reality (VR) technology, the deployment of such will dramatically increase in non-entertainment environments, such as professional education and training, manufacturing, service, or low frequency/high-risk scenarios. Clinical education is an area that especially stands to benefit from VR technology due to the complexity, high cost, and difficult logistics. The effectiveness of the deployment of VR systems in healthcare environments is subject to factors that may not be necessarily considered for devices targeting the entertainment market. In this work, we systematically compare a wide range of VR head-mounted displays (HMDs) technologies and designs by defining a new set of metrics that are 1) relevant to medical VR solutions and 2) are of paramount importance for VR-based education and training. We evaluated 10 technologies based on various criteria, including neck strain, heat development, cleanability, and colour accuracy. Other metrics such as text readability, comfort, and contrast perception were evaluated in a multi-user study on three selected technologies, namely the ones offered by Oculus Rift S, HTC Vive Pro and Samsung Odyssey+. Results indicate that the HTC Vive Pro performs best with regards to comfort, display quality and compatibility with glasses.

Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies. Augmented reality (AR) has been introduced in the operating rooms in the last decade; however, in image-guided interventions, it has often only been considered as a visualization device improving traditional workflows. As a consequence, the technology is gaining minimum maturity that it requires to redefine new procedures, user interfaces, and interactions. The main contribution of this paper is to reveal how exemplary workflows are redefined by taking full advantage of head-mounted displays when entirely co-registered with the imaging system at all times. The proposed AR landscape is enabled by co-localizing the users and the imaging devices via the operating room environment and exploiting all involved frustums to move spatial information between different bodies. The awareness of the system from the geometric and physical characteristics of X-ray imaging allows the redefinition of different human-machine interfaces. We demonstrate that this AR paradigm is generic, and can benefit a wide variety of procedures. Our system achieved an error of $4.76\pm2.91$ mm for placing K-wire in a fracture management procedure, and yielded errors of $1.57\pm1.16^\circ$ and $1.46\pm1.00^\circ$ in the abduction and anteversion angles, respectively, for total hip arthroplasty. We hope that our holistic approach towards improving the interface of surgery not only augments the surgeon's capabilities but also augments the surgical team's experience in carrying out an effective intervention with reduced complications and provide novel approaches of documenting procedures for training purposes.

Object alignment is an activity frequently performed in several domains. Traditional AR/VR methods provide virtual guides such as text, arrows, or animations to assist users during alignment. This work explores the feasibility of using virtual cameras and mirrors instead. We conducted a study where participants aligned objects assisted by virtual cameras and mirrors in VR. Data regarding alignment, time to completion, users' distance traveled, average head velocity, usability, and mental effort was collected. Results show that virtual helpers reduce mental effort and distance traveled and increase acceptance without negatively affecting alignment accuracy.

Computer-assisted solutions are changing surgical practice continuously. One of the most disruptive technologies among the computer-integrated surgical techniques is Augmented Reality (AR). While Augmented Reality is increasingly used in several medical specialties, its potential benefit in orthopedic surgery is not yet clear. The purpose of this article is to provide a systematic review of the current state of knowledge and the applicability of AR in orthopedic surgery. A systematic review of the current literature was performed to find the state of knowledge and applicability of AR in Orthopedic surgery. A systematic search of the following three databases was performed: “PubMed”, “Cochrane Library” and “Web of Science”. The systematic review followed the Preferred Reporting Items on Systematic Reviews and Meta-analysis (PRISMA) guidelines and it has been published and registered in the international prospective register of systematic reviews (PROSPERO). 31 studies and reports are included and classified into the following categories: Instrument / Implant Placement, Osteotomies, Tumor Surgery, Trauma, and Surgical Training and Education. Quality assessment could be performed in 18 studies. Among the clinical studies, there were six case series with an average score of 90% and one case report, which scored 81% according to the Joanna Briggs Institute Critical Appraisal Checklist (JBI CAC). The 11 cadaveric studies scored 81% according to the QUACS scale (Quality Appraisal for Cadaveric Studies). This manuscript provides 1) a summary of the current state of knowledge and research of Augmented Reality in orthopedic surgery presented in the literature, and 2) a discussion by the authors presenting the key remarks required for seamless integration of Augmented Reality in the future surgical practice. PROSPERO registration number: CRD42019128569.

BACKGROUND:Malposition of the acetabular component of a hip prosthesis can lead to poor outcomes. Traditional placement with fluoroscopic guidance results in a 35% malpositioning rate. We compared the (1) accuracy and precision of component placement, (2) procedure time, (3) radiation dose, and (4) usability of a novel 3-dimensional augmented reality (AR) guidance system vs standard fluoroscopic guidance for acetabular component placement. METHODS:We simulated component placement using a radiopaque foam pelvis. Cone-beam computed tomographic data and optical data from a red-green-blue-depth camera were coregistered to create the AR environment. Eight orthopedic surgery trainees completed component placement using both methods. We measured component position (inclination, anteversion), procedure time, radiation dose, and usability (System Usability Scale score, Surgical Task Load Index value). Alpha = .05. RESULTS:Compared with fluoroscopic technique, AR technique was significantly more accurate for achieving target inclination (P = .01) and anteversion (P = .02) and more precise for achieving target anteversion (P < .01). AR technique was faster (mean ± standard deviation, 1.8 ± 0.25 vs 3.9 ± 1.6 minute; P < .01), and participants rated it as significantly easier to use according to both scales (P < .05). Radiation dose was not significantly different between techniques (P = .48). CONCLUSION:A novel 3-dimensional AR guidance system produced more accurate inclination and anteversion and more precise anteversion in the placement of the acetabular component of a hip prosthesis. AR guidance was faster and easier to use than standard fluoroscopic guidance and did not involve greater radiation dose.

Traditional intensity-based 2D/3D registration requires near-perfect initialization in order for image similarity metrics to yield meaningful updates of X-ray pose and reduce the likelihood of getting trapped in a local minimum. The conventional approaches strongly depend on image appearance rather than content, and therefore, fail in revealing large pose offsets that substantially alter the appearance of the same structure. We complement traditional similarity metrics with a convolutional neural network-based (CNN-based) registration solution that captures large-range pose relations by extracting both local and contextual information, yielding meaningful X-ray pose updates without the need for accurate initialization. To register a 2D X-ray image and a 3D CT scan, our CNN accepts a target X-ray image and a digitally reconstructed radiograph at the current pose estimate as input and iteratively outputs pose updates in the direction of the pose gradient on the Riemannian Manifold. Our approach integrates seamlessly with conventional image-based registration frameworks, where long-range relations are captured primarily by our CNN-based method while short-range offsets are recovered accurately with an image similarity-based method. On both synthetic and real X-ray images of the human pelvis, we demonstrate that the proposed method can successfully recover large rotational and translational offsets, irrespective of initialization.

Robot-assisted minimally invasive surgery has shown to improve patient outcomes, as well as reduce complications and recovery time for several clinical applications. While increasingly configurable robotic arms can maximize reach and avoid collisions in cluttered environments, positioning them appropriately during surgery is complicated because safety regulations prevent automatic driving. We propose a head-mounted display (HMD) based augmented reality (AR) system designed to guide optimal surgical arm set up. The staff equipped with HMD aligns the robot with its planned virtual counterpart. In this user-centric setting, the main challenge is the perspective ambiguities hindering such collaborative robotic solution. To overcome this challenge, we introduce a novel registration concept for intuitive alignment of AR content to its physical counterpart by providing a multi-view AR experience via reflective-AR displays that simultaneously show the augmentations from multiple viewpoints. Using this system, users can visualize different perspectives while actively adjusting the pose to determine the registration transformation that most closely superimposes the virtual onto the real. The experimental results demonstrate improvement in the interactive alignment of a virtual and real robot when using a reflective-AR display. We also present measurements from configuring a robotic manipulator in a simulated trocar placement surgery using the AR guidance methodology.

Stitching images acquired under perspective projective geometry is a relevant topic in computer vision with multiple applications ranging from smartphone panoramas to the construction of digital maps. Image stitching is an equally prominent challenge in medical imaging, where the limited field-of-view captured by single images prohibits holistic analysis of patient anatomy. The barrier that prevents straight-forward mosaicing of 2D images is depth mismatch due to parallax. In this work, we leverage the Fourier slice theorem to aggregate information from multiple transmission images in parallax-free domains using fundamental principles of X-ray image formation. The semantics of the stitched image are restored using a novel deep learning strategy that exploits similarity measures designed around frequency, as well as dense and sparse spatial image content. Our pipeline, not only stitches images, but also provides orthographic reconstruction that enables metric measurements of clinically relevant quantities directly on the 2D image plane.

Manufacturing, maintenance, assembly, and training tasks represent some of the human activities that have captured special interest for Mixed Reality (MR) applications. For most of these scenarios, accurate object alignment constitutes a requirement to ensure the desired outcome. This task has proved to be especially challenging in egocentric approaches, frequently leading to estimation errors in depth. While traditional MR methods provide virtual guides such as text, arrows, or animations to assist users during alignment, this work explores the feasibility of using additional views generated by virtual cameras and mirrors. Presenting additional views from different perspectives can help to mitigate the estimation errors and show information that is not directly visible to users. To explore the benefits of using additional views for alignment tasks, and to collect reliable data and diminish external factors, we conducted a user study in a controlled virtual environment where participants aligned objects supported by additional views from a top-down camera and virtual mirrors. Data regarding alignment error, time to completion, user's interaction and attention, distance traveled, average head velocity, usability, and mental effort were collected. Our results show that using additional views reduces the mental effort and distance traveled by users and increases acceptance without negatively affecting the alignment accuracy. Therefore, we believe that users will also benefit from integrating these techniques during alignment tasks in MR environments.

For a plane symmetric object we can find two views - mirrored at the plane of symmetry - that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we can automatically have a second, virtual image of that object if the 3D location of the symmetry plane is known. In this work, we show for the first time that the above concept naturally extends to transmission imaging and present an algorithm to estimate the 3D symmetry plane from a set of projection domain images based on Grangeat’s theorem.

Minimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy. Surgeons could highly benefit from additional information, such as the anatomical landmark locations in the projections, to support intra-operative decision making. However, detecting landmarks is challenging since the viewing direction changes substantially between views leading to varying appearance of the same landmark. Therefore, and to the best of our knowledge, view-independent anatomical landmark detection has not been investigated yet. In this work, we propose a novel approach to detect multiple anatomical landmarks in X-ray images from arbitrary viewing directions. To this end, a sequential prediction framework based on convolutional neural networks is employed to simultaneously regress all landmark locations. For training, synthetic X-rays are generated with a physically accurate forward model that allows direct application of the trained model to real X-ray images of the pelvis. View invariance is achieved via data augmentation by sampling viewing angles on a spherical segment of $$120^\circ \times 90^\circ $$ . On synthetic data, a mean prediction error of 5.6 ± 4.5 mm is achieved. Further, we demonstrate that the trained model can be directly applied to real X-rays and show that these detections define correspondences to a respective CT volume, which allows for analytic estimation of the 11 degree of freedom projective mapping. We present the first tool to detect anatomical landmarks in X-ray images independent of their viewing direction. Access to this information during surgery may benefit decision making and constitutes a first step toward global initialization of 2D/3D registration without the need of calibration. As such, the proposed concept has a strong prospect to facilitate and enhance applications and methods in the realm of image-guided surgery.

Purpose For a perfectly plane symmetric object, we can find two views—mirrored at the plane of symmetry—that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we automatically have a second, virtual image of that object if the 3-D location of the symmetry plane is known. Methods We propose a method for estimating the symmetry plane from a set of projection images as the solution of a consistency maximization based on epipolar consistency. With the known symmetry plane, we can exploit symmetry to estimate in-plane motion by introducing the X-trajectory that can be acquired with a conventional short-scan trajectory by simply tilting the acquisition plane relative to the plane of symmetry. Results We inspect the symmetry plane estimation on a real scan of an anthropomorphic human head phantom and show the robustness using a synthetic dataset. Further, we demonstrate the advantage of the proposed method for estimating in-plane motion using the acquired projection data. Conclusion Symmetry breakers in the human body are widely used for the detection of tumors or strokes. We provide a fast estimation of the symmetry plane, robust to outliers, by computing it directly from a set of projections. Further, by coupling the symmetry prior with epipolar consistency, we overcome inherent limitations in the estimation of in-plane motion.

Purpose For orthopedic procedures, surgeons utilize intra-operative medical images such as fluoroscopy to plan screw placement and accurately position the guide wire with the intended trajectory. The number of fluoroscopic images needed depends on the complexity of the case and skill of the surgeon. Since more fluoroscopic images lead to more exposure and higher radiation dose for both surgeon and patient, a solution that decreases the number of fluoroscopic images would be an improvement in clinical care. Methods This article describes and compares three different novel navigation methods and techniques for screw placement using an attachable Inertial Measurement Unit device or a robotic arm. These methods provide projection and visualization of the surgical tool trajectory during the slipped capital femoral epiphysis procedure. Results These techniques resulted in faster and more efficient preoperative calibration and set up times compared to other intra-operative navigation systems in our phantom study. We conducted an experiment using 120 model bones to measure the accuracy of the methods. Conclusion As conclusion, these approaches have the potential to improve accuracy of surgical tool navigation and decrease the number of required X-ray images without any change in the clinical workflow. The results also show 65% decrease in total error compared to the conventional manual approach.

With recent advances of Virtual Reality (VR) technology, the deployment of such will dramatically increase in non-entertainment environments, such as professional education and training, manufacturing, service, or low frequency/high risk scenarios. Clinical education is an area that especially stands to benefit from VR technology due to the complexity, high cost, and difficult logistics. The effectiveness of the deployment of VR systems, is subject to factors that may not be necessarily considered for devices targeting the entertainment market. In this work, we systematically compare a wide range of VR Head-Mounted Displays (HMDs) technologies and designs by defining a new set of metrics that are 1) relevant to most generic VR solutions and 2) are of paramount importance for VR-based education and training. We evaluated ten HMDs based on various criteria, including neck strain, heat development, and color accuracy. Other metrics such as text readability, comfort, and contrast perception were evaluated in a multi-user study on three selected HMDs, namely Oculus Rift S, HTC Vive Pro and Samsung Odyssey+. Results indicate that the HTC Vive Pro performs best with regards to comfort, display quality and compatibility with glasses.