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Register a new userImage-based marker tracking and registration for intraoperative 3D image-guided interventions using augmented reality
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Added by
Andong Cao
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Augmented reality has the potential to improve operating room workflow by allowing physicians to see inside a patient through the projection of imaging directly onto the surgical field. For this to be useful the acquired imaging must be quickly and accurately registered with patient and the registration must be maintained. Here we describe a method for projecting a CT scan with Microsoft Hololens and then aligning that projection to a set of fiduciary markers. Radio-opaque stickers with unique QR-codes are placed on an object prior to acquiring a CT scan. The location of the markers in the CT scan are extracted and the CT scan is converted into a 3D surface object. The 3D object is then projected using the Hololens onto a table on which the same markers are placed. We designed an algorithm that aligns the markers on the 3D object with the markers on the table. To extract the markers and convert the CT into a 3D object took less than 5 seconds. To align three markers, it took $0.9 pm 0.2$ seconds to achieve an accuracy of $5 pm 2$ mm. These findings show that it is feasible to use a combined radio-opaque optical marker, placed on a patient prior to a CT scan, to subsequently align the acquired CT scan with the patient.
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Periacetabular osteotomy is a challenging surgical procedure for treating developmental hip dysplasia, providing greater coverage of the femoral head via relocation of a patients acetabulum. Since fluoroscopic imaging is frequently used in the surgical workflow, computer-assisted X-Ray navigation of osteotomes and the relocated acetabular fragment should be feasible. We use intensity-based 2D/3D registration to estimate the pelvis pose with respect to fluoroscopic images, recover relative poses of multiple views, and triangulate landmarks which may be used for navigation. Existing similarity metrics are unable to consistently account for the inherent mismatch between the preoperative intact pelvis, and the intraoperative reality of a fractured pelvis. To mitigate the effect of this mismatch, we continuously estimate the relevance of each pixel to solving the registration and use these values as weightings in a patch-based similarity metric. Limiting computation to randomly selected subsets of patches results in faster runtimes than existing patch-based methods. A simulation study was conducted with random fragment shapes, relocations, and fluoroscopic views, and the proposed method achieved a 1.7 mm mean triangulation error over all landmarks, compared to mean errors of 3 mm and 2.8 mm for the non-patched and image-intensity-variance-weighted patch similarity metrics, respectively.
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 $4.76pm2.91$ mm for placing K-wire in a fracture management procedure, and yielded errors of $1.57pm1.16^circ$ and $1.46pm1.00^circ$ 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 surgeons capabilities but also augments the surgical teams experience in carrying out an effective intervention with reduced complications and provide novel approaches of documenting procedures for training purposes.
Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows. Further, it is expected that image-based navigation will play a major role in enabling mixed reality environments and autonomous, robotic workflows. A critical component of image guidance is 2D/3D registration, a technique to estimate the spatial relationships between 3D structures, e.g., volumetric imagery or tool models, and 2D images thereof, such as fluoroscopy or endoscopy. While image-based 2D/3D registration is a mature technique, its transition from the bench to the bedside has been restrained by well-known challenges, including brittleness of the optimization objective, hyperparameter selection, and initialization, difficulties around inconsistencies or multiple objects, and limited single-view performance. One reason these challenges persist today is that analytical solutions are likely inadequate considering the complexity, variability, and high-dimensionality of generic 2D/3D registration problems. The recent advent of machine learning-based approaches to imaging problems that, rather than specifying the desired functional mapping, approximate it using highly expressive parametric models holds promise for solving some of the notorious challenges in 2D/3D registration. In this manuscript, we review the impact of machine learning on 2D/3D registration to systematically summarize the recent advances made by introduction of this novel technology. Grounded in these insights, we then offer our perspective on the most pressing needs, significant open problems, and possible next steps.
Image registration plays an important role in medical image analysis. Conventional optimization based methods provide an accurate estimation due to the iterative process at the cost of expensive computation. Deep learning methods such as learn-to-map are much faster but either iterative or coarse-to-fine approach is required to improve accuracy for handling large motions. In this work, we proposed to learn a registration optimizer via a multi-scale neural ODE model. The inference consists of iterative gradient updates similar to a conventional gradient descent optimizer but in a much faster way, because the neural ODE learns from the training data to adapt the gradient efficiently at each iteration. Furthermore, we proposed to learn a modal-independent similarity metric to address image appearance variations across different image contrasts. We performed evaluations through extensive experiments in the context of multi-contrast 3D MR images from both public and private data sources and demonstrate the superior performance of our proposed methods.
Deformable image registration, aiming to find spatial correspondence between a given image pair, is one of the most critical problems in the domain of medical image analysis. In this paper, we present a generic, fast, and accurate diffeomorphic image registration framework that leverages neural ordinary differential equations (NODEs). We model each voxel as a moving particle and consider the set of all voxels in a 3D image as a high-dimensional dynamical system whose trajectory determines the targeted deformation field. Compared with traditional optimization-based methods, our framework reduces the running time from tens of minutes to tens of seconds. Compared with recent data-driven deep learning methods, our framework is more accessible since it does not require large amounts of training data. Our experiments show that the registration results of our method outperform state-of-the-arts under various metrics, indicating that our modeling approach is well fitted for the task of deformable image registration.
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