Markerless Registration Research Articles

An Automated Marker-Less Registration Approach Using Neural Radiance Fields for Potential Use in Mixed Reality-Based Computer-Aided Surgical Navigation of Paranasal Sinus

Paranasal sinus surgery, a common treatment for chronic rhinosinusitis, requires exceptional precision due to the proximity of critical anatomical structures. To ensure accurate instrument control and clear visualization of the surgical site, surgeons utilize computer-aided surgical navigation (CSN). A key component of CSN is the registration process, which is traditionally reliant on manual or marker-based techniques. However, there is a growing shift toward marker-less registration methods. In previous work, we investigated a mesh-based registration approach using a Mixed Reality Head-Mounted Display (MR-HMD), specifically the Microsoft HoloLens 2. However, this method faced limitations, including depth holes and invalid values. These issues stemmed from the device’s low-resolution camera specifications and the 3D projection steps required to upscale RGB camera spaces. In this study, we propose a novel automated marker-less registration method leveraging Neural Radiance Field (NeRF) technology with an MR-HMD. To address insufficient depth information in the previous approach, we utilize rendered-depth images generated by the trained NeRF model. We evaluated our method against two other techniques, including prior mesh-based registration, using a facial phantom and three participants. The results demonstrate our proposed method achieves at least a 0.873 mm (12%) improvement in registration accuracy compared to others.

Markerless Registration Method for Augmented Reality Navigation in Head Surgery

Markerless Registration Method for Augmented Reality Navigation in Head Surgery

Application of augmented reality using automatic markerless registration for facial plastic and reconstructive surgery

This study aimed to present a novel markerless augmented reality (AR) system using automatic registration based on machine-learning algorithms that visualize the facial region and provide an intraoperative guide for facial plastic and reconstructive surgeries.This study prospectively enrolled 20 patients scheduled for facial plastic and reconstructive surgeries. The AR system visualizes computed tomographic data in three-dimensional (3D) space by aligning with the point clouds captured by a 3D camera. Point cloud registration consists of two stages: the preliminary registration gives an initial estimate of the transformation using landmark detection, followed by the precise registration using Iterative Closest Point algorithms. Computed Tomography (CT) data can be visualized as two-dimensional slice images or 3D images by the AR system. The AR registration error was defined as the cloud-to-cloud distance between the surface data obtained from the CT and 3D camera. The error was calculated in each facial territory, including the upper, middle, and lower face, while patients were awake and orally intubated, respectively.The mean registration errors were 1.490 ± 0.384 mm and 1.948 ± 0.638 mm while patients were awake and orally intubated, respectively. There was a significant difference in the errors in the lower face between patients while they were awake (1.502 ± 0.480 mm) and orally intubated (2.325 ± 0.971 mm) when stratified by facial territories (p = 0.006).The markerless AR can accurately visualize the facial region with a mean overall registration error of 1–2 mm, with a slight increase in the lower face due to errors arising from tube intubation.

SkyroadAR: An Augmented Reality System for UAVs Low-Altitude Public Air Route Visualization

Augmented Reality (AR) technology visualizes virtual objects in the real environment, offering users an immersive experience that enhances their spatial perception of virtual objects. This makes AR an important tool for visualization in engineering, education, and gaming. The Unmanned Aerial Vehicles’ (UAVs’) low-altitude public air route (Skyroad) is a forward-looking virtual transportation infrastructure flying over complex terrain, presenting challenges for user perception due to its invisibility. In order to achieve a 3D and intuitive visualization of Skyroad, this paper proposes an AR visualization framework based on a physical sandbox. The framework consists of four processes: reconstructing and 3D-printing a sandbox model, producing virtual scenes for UAVs Skyroad, implementing a markerless registration and tracking method, and displaying Skyroad scenes on the sandbox with GPU-based occlusion handling. With the support of the framework, a mobile application called SkyroadAR was developed. System performance tests and user questionnaires were conducted on SkyroadAR; the results showed that our approachs to tracking and occlusion provided an efficient and stable AR effect for Skyroad. This intuitive visualization is recognized by both professional and non-professional users.

The in-vitro accuracy of fiducial marker-based versus markerless registration of an intraoral scan with a cone-beam computed tomography scan in the presence of restoration artifact.

To determine the effect of restoration artifact ('metal artifact') on registration accuracy of an intraoral scan and cone-beam computed tomography (CBCT) scan, comparing fiducial marker-based registration with markerless registration. A maxillary model was fitted with multiple configurations of zirconia crowns to simulate various states of oral rehabilitation. Intraoral scans and CBCT scans (half and full rotation) were acquired. Registration was performed using markerless (point-based registration with surface-based refinement) and fiducial marker-based registration. Each experimental condition was repeated 10 times (n = 320). The absolute deviation was measured at the canines and first molars, and the average and maximum values were analysed using multiple linear regression. R2 was 0.874 for average error and 0.858 for maximum error. For markerless registration, there were 0.041 mm (p < .001) and 0.045 mm (p < .001) increases in average and maximum error per crown, respectively. For fiducial marker-based registration, the effect of additional crowns was not statistically significant for average (p = .067) or maximum (p = .438) error. For a full arch of crowns, the regression model predicted average and maximum errors of 0.581 and 0.697 mm for the markerless technique, and 0.185 and 0.210 mm for the fiducial marker-based technique. Overall, the fiducial marker-based technique was more accurate for four or more crowns. The half rotation scan increased average error by 0.021 mm (p = .001) and maximum error by 0.029 mm (p < .001). Under the present study's experimental conditions, the fiducial marker-based technique should be considered if four or more full-coverage highly radiopaque restorations are present.

Exploring the Performance of Geometry-Based Markerless Registration in a Simulated Surgical Environment: A Comparative Study of Registration Algorithms in Medical Augmented Reality.

Augmented Reality (AR) has been utilized in multiple applications in the medical field, such as augmenting Computed Tomography (CT) images onto the patient's body during surgery. However, one of the challenges in its utilization is to register the pre-operative CT images to the patient's body accurately. The current registration process requires prior attachment of tracking markers, and their localization within the body and CT images. This process can be cumbersome, error-prone, and dependent on the surgeon's experience. Moreover, there are cases where medical instruments, drapes, or the body may occlude the markers. In light of these limitations, markerless registration algorithms have the potential to aid the registration process in the clinical setting. While those algorithms have been successfully used in other sectors, such as multimedia, they have not yet been thoroughly investigated in a clinical setting, especially in surgery, where there are more challenging cases with different positions of the patients in the image and the surgical environment. In this paper, we benchmarked and evaluated the performance of 6 state-of-the-art markerless registration algorithms from the multimedia sector by registering a CT image onto the whole-body phantom dataset acquired from a simulated surgical environment. We also analyzed the suitability of these algorithms for use in the surgical setting and discussed their potential for the advancement of AR-assisted surgery.Clinical Relevance-Our study provides insight into the potential of AR-assisted surgery and helps practitioners in choosing the most suitable registration algorithm for their needs to improve patient outcomes, reduce the risk of surgical errors and shorten the time of preoperative planning.

Accuracy of markerless registration methods of DICOM and STL files used for computerized surgical guides in mandibles with metal restorations: An in vitro study

Statement of problemDigital imaging and communications in medicine (DICOM) files together with surface scans must be accurately registered in virtual implant planning software programs to match real-life dimensions and ensure correct plan transfer through computer-aided manufactured surgical guides. PurposeThe purpose of this in vitro study was to evaluate the accuracy of 3 different registration methods of DICOM data with and without metal restorations and a metal artifact reduction (MAR) tool for surface scans. Material and methodsThirteen dentate mandibles were assigned to each group of this study (n=39). Baseplate wax was adapted to the bone surfaces of each mandible, and 5 radiopaque markers were attached. A desktop scanner was used to obtain control scans. The groups of metal-free mandibles (MFM) and mandibles with metal restorations (MRM) were scanned to obtain DICOM data without a MAR tool. Additional DICOM data for the MRM were obtained with the MAR tool (MRM-MAR). Point-based registration (PBR), best-fit registration (BFR), and automatic registration (AR) were used to align standard tessellation language (STL) and DICOM data, and 3 data sets were exported. Radiographic markers on each data set were compared with those on the control scan, and positional deviations were calculated and statistically evaluated with 1-way ANOVA followed by multiple pairwise comparisons, independent samples t test, and 2-way ANOVA (α=.05). ResultsWithin each group, PBR had the lowest deviation values with statistical significance in the MFM and the MRM-MAR groups (P<.001). AR showed failure in the MRM and the MRM-MAR groups. Statistically significant differences were found on comparing the average deviations among the 3 groups for PBR only (P<.001). No association was found between deviation values and the presence or absence of metal restoration, while a positive association was found with the type of registration method (P<.001). ConclusionsPBR had the highest accuracy level compared with AR and BFR methods. An increase in the number of calculations resulted in more deviation values. The MAR tool had a positive effect on PBR in mandibles with metal restorations.

A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image.

Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6–0.8 mm and 0.5–1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be and . The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.

Reattachable fiducial skin marker for automatic multimodality registration

PurposeFusing image information has become increasingly important for optimal diagnosis and treatment of the patient. Despite intensive research towards markerless registration approaches, fiducial marker-based methods remain the default choice for a wide range of applications in clinical practice. However, as especially non-invasive markers cannot be positioned reproducibly in the same pose on the patient, pre-interventional imaging has to be performed immediately before the intervention for fiducial marker-based registrations.MethodsWe propose a new non-invasive, reattachable fiducial skin marker concept for multi-modal registration approaches including the use of electromagnetic or optical tracking technologies. We furthermore describe a robust, automatic fiducial marker localization algorithm for computed tomography (CT) and magnetic resonance imaging (MRI) images. Localization of the new fiducial marker has been assessed for different marker configurations using both CT and MRI. Furthermore, we applied the marker in an abdominal phantom study. For this, we attached the marker at three poses to the phantom, registered ten segmented targets of the phantom’s CT image to live ultrasound images and determined the target registration error (TRE) for each target and each marker pose.ResultsReattachment of the marker was possible with a mean precision of 0.02 mm ± 0.01 mm. Our algorithm successfully localized the marker automatically in all (n=201) evaluated CT/MRI images. Depending on the marker pose, the mean (n=10) TRE of the abdominal phantom study ranged from 1.51 ± 0.75 mm to 4.65 ± 1.22 mm.ConclusionsThe non-invasive, reattachable skin marker concept allows reproducible positioning of the marker and automatic localization in different imaging modalities. The low TREs indicate the potential applicability of the marker concept for clinical interventions, such as the puncture of abdominal lesions, where current image-based registration approaches still lack robustness and existing marker-based methods are often impractical.

Automatic Bone Surface Restoration for Markerless Computer-Assisted Orthopaedic Surgery

An automatic markerless knee tracking and registration algorithm has been proposed in the literature to avoid the marker insertion required by conventional computer-assisted knee surgery, resulting in a shorter and less invasive surgical workflow. However, such an algorithm considers intact femur geometry only. The bone surface modification is inevitable due to intra-operative intervention. The mismatched correspondences will degrade the reliability of registered target pose. To solve this problem, this work proposed a supervised deep neural network to automatically restore the surface of processed bone. The network was trained on a synthetic dataset that consists of real depth captures of a model leg and simulated realistic femur cutting. According to the evaluation on both synthetic data and real-time captures, the registration quality can be effectively improved by surface reconstruction. The improvement in tracking accuracy is only evident over test data, indicating the need for future enhancement of the dataset and network.

Markerless registration approach using dynamic touchable region model.

Markerless registration is required for image-guided surgery; it has limited accuracy due to the ambiguity of the correspondence point set. In this study, we proposed a registration framework to improve registration accuracy for markerless registration using a dynamic touchable region model (DTRM). The DTRM is defined using the geometric characteristics of the surface around the fiducial area using Intrinsic Shape Signature keypoints. The new registration procedure, which combines the DTRM with iterative closest point-based registration (ICDTP) was implemented and verified with phantom experiments for a single-user and multi-user study. The ICDTP registration framework provides improved performance over the paired-point registration for surgeries, where it is inappropriate to construct the corresponding positions with adhesive fiducial markers. The proposed method can be an alternative approach for image-guided surgery in operations for which itis not appropriate to set up markers for registration.

증강현실 수술 내비게이션 시스템을 위한 단일 카메라 깊이 추정 기반 마커리스 정합

In augmented reality(AR) surgical navigation system, the depth estimation using RGBD camera has limitation in obtaining the dense depth necessary to increase the registration accuracy. Recently, deep learning based monocular depth estimation has showed remarkable performance. In this study, we developed a markerless registration method using the monocular depth estimation, and applied it to AR surgical navigation system. The accuracy of our method of monocular depth estimation was 2.47 ± 1.15mm, while that of the method of the RGBD camera was 2.33 ± 1.24mm. There was no significant difference by paired T-test. Furthermore, the monocular depth estimation was able to acquire denser depth than the RGBD camera.

A novel augmented reality visualization in jaw surgery: enhanced ICP based modified rotation invariant and modified correntropy

Image registration, accuracy, processing time and occlusions are the main limitations of augmented reality (AR) based jaw surgery. Therefore, the main aim of this paper is to reduce the registration error, which will help in improving the accuracy and reducing the processing time. Also, it aims to remove outliers and remove the registration outcomes trapped in local minima to improve the alignment problems and remove the occlusion caused by surgery instrument. The enhanced Iterative Closest Point (ICP) algorithm with rotation invariant and correntropy was used for the proposed system. Markerless image registration technique was used for AR-based jaw surgery. The problem of occlusion caused by surgical tools and blood is solved by using stereo based tracing with occlusion handling techniques. This research reduced alignment error 0.59 mm ~ 0.62 mm against 0.69 ~ 0.72 mm of state-of-the-art solution. The processing time of video frames was enhanced to 11.9 ~ 12.8 fps against 8 ~ 9.15 fps in state-of-the-art solution. This paper is focused on providing fast and accurate AR-based system for jaw surgery. The proposed system helps in improving the AR visualization during jaw surgery. The combination of methods and technology helped in improving AR visualization for jaw surgery and to overcome the failure caused by a large rotation angle and provides an initial parameter for better image registration. It also enhances performance by removing outliers and noises. The pose refinement stage provides a better result in terms of processing time and accuracy.

Applicability of a single camera-based catheter navigation system using teeth arch as an anatomical landmark for superselective intraarterial infusion in advanced oral cancer treatment

Superselective intraarterial infusion chemoradiotherapy is a modality of oral cancer therapy in which the artery feeding the tumor is catheterized. 3D information about the carotid artery is required to enable the surgeon to judge whether to advance, retract, or rotate the catheter. For this purpose, we proposed and conducted a model experiment to assess a new method of catheterization that applies a tracking system using registration with a monocular camera using the maxillary arch as the anatomical landmark. In this method, the preoperative 3D computer tomography angiographic image of the carotid artery that the catheter will be passed through is overlaid on the 2D video image. The mean TRE was 0.96 ± 0.36 mm and 0.88 ± 0.31 mm and 1.12 ± 0.46 mm when images were registered with the anterior and posterior teeth as the landmarks, respectively; the difference was not significant (p = 0.21). This tracking system that enables markerless registration simply by taking images of the maxillary anterior teeth with a single camera was convenient and effective for catheterization. In this study, we propose the new application of this tracking system and a novel method of catheterization for superselective intraarterial infusion chemoradiotherapy for oral cancer.Graphical abstractIn retrograde superselective intraarterial catheterization, a catheter is inserted into a tumor-feeding artery originating from the external carotid artery (ECA) (the lingual artery [LA], facial artery [FA], or maxillary artery [MA]). Because the maxillary dentition is located near the external carotid artery, we focused on real-time markerless registration using maxillary dentition fixed to the skull.

Robust Markerless Registration of Point Clouds for Terrestrial Laser Scanning-Based Measurement of Bulk Grains Stockpiled in Storehouses

HighlightsFully automated registration free from artificial markers for multi-scan point clouds aimed for TLS-based measurement of bulk grains in large storehouses.The geometric structure of the large grain storehouse is explored to derive geometrical features as the structurally semantic information for scene understanding.The geometrical features are modeled as a small ordered set and correspondences are established by performing trials for all possible matching pairs of two sets extracted from two different scans.Significant improvements have been achieved in registration accuracy, computational efficiency, and robustness against scenes with symmetric structures as well as the immunity to noises and varying point density.Abstract. Point clouds collected by terrestrial laser scanning (TLS) in the application of bulk grain measurement and quantification contain a vast amount of data, relatively low-textured surfaces and highly symmetric structures. All of these challenges make it a difficult task to automatically register multiple scans from different viewpoints needed to fully cover a large-scale scene. To address the challenges, this article presents a robust automatic marker-free registration method dedicated for multi-scan TLS point cloud data captured in large grain storehouses. The framework of the dedicated method follows the common procedure to split the entire registration into coarse alignment and fine registration, and uses the iterative closest point (ICP) algorithm for the latter. The main contribution of the proposed dedicated method is an efficient way to find a global coarse alignment that is robust across individual scans in a TLS-based bulk grain measurement project. To tackle the correspondence problem, which is at the core of a registration task, the geometric information inherent in grain storehouses is explored in the stage of global coarse alignment. The derived semantic feature points are modeled as a small ordered set and reliable correspondences are established by performing trials for all possible matching pairs of two sets extracted from two different scans. Experimental results show the dedicated method outperforms the existing generic markless registration approaches in terms of accuracy, robustness and computational efficiency. With robustness, efficiency and accuracy, the proposed markless point cloud registration method dedicated for bulk grain measurement can cover a gap between the TLS technology and various granary field applications. Especially, its applicability to the dominant storage structure in Chinese huge grain reserve system implies remarkable efficiency improvements and will facilitate the application of TLS-based measurement in the national grain inventory of China. Keywords: Bulk grain measurement, Feature extraction, Grain storehouse, Markerless registration, Point cloud, Terrestrial laser scanning.

Markerless Navigation System for Orthopaedic Knee Surgery: A Proof of Concept Study

Current computer-assisted surgical navigation systems mainly rely on optical markers screwed into the bone for anatomy tracking. The insertion of these percutaneous markers increases operating complexity and causes additional harm to the patient. A markerless tracking and registration algorithm has recently been proposed to avoid anatomical markers for knee surgery. The femur points were directly segmented from the recorded RGBD scene by a neural network and then registered to a pre-scanned femur model for the real-time pose. However, in a practical setup such a method can produce unreliable registration results, especially in rotation. Furthermore, its potential application in surgical navigation has not been demonstrated. In this paper, we first improved markerless registration accuracy by adopting a bounded-ICP (BICP) technique, where an estimate of the remote hip centre, acquired also in a markerless way, was employed to constrain distal femur alignment. Then, a proof-of-concept markerless navigation system was proposed to assist in typical knee drilling tasks. Two example setups for global anchoring were proposed and tested on a phantom leg. Our BICP-based markerless tracking and registration method has better angular accuracy and stability than the original method, bringing our straightforward, less invasive markerless navigation approach one step closer to clinical application. According to user tests, our proposed optically anchored navigation system achieves comparable accuracy with the state-of-the-art (3.64± 1.49 mm in position and 2.13±0.81° in orientation). Conversely, our visually anchored, optical tracker-free setup has a lower accuracy (5.86± 1.63 mm in position and 4.18±1.44° in orientation), but is more cost-effective and flexible in the operating room.

Towards markerless computer-aided surgery combining deep segmentation and geometric pose estimation: application in total knee arthroplasty

ABSTRACT Total knee arthroplasty (TKA) is a surgical procedure performed in patients suffering from knee arthritis. The correct positioning of the implants is strongly related to multiple surgical variables that have a tremendous impact on the success of the surgery. Computer-based navigation systems have been investigated and developed in order to assist the surgeon in accurately controlling those surgical variables. The existing technologies are very costly, require additional bone incisions for fixing markers to be tracked, and these markers are usually bulky, interfering with the standard surgical flow. This work presents a markerless navigation system that supports the surgeon in accurately performing the TKA procedure. The proposed system uses a mobile RGB-D camera for replacing the existing optical tracking systems and does not require markers to be tracked. We combine an effective deep learning-based approach for accurately segmenting the bone surface with a robust geometry-based algorithm for registering the bones with pre-operative models. The favourable performance of our pipeline is achieved by (1) employing a semi-supervised labelling approach for generating training data from real TKA surgery data, (2) using effective data augmentation techniques for improving the generalisation capability and (3) using appropriate depth data cleaning strategies. The construction of this complete markerless registration prototype that generalises for unseen intra-operative data is non-obvious, and relevant insights and future research directions can be derived. The experimental results show encouraging performance for video-based TKA.

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems.

Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

Assessing walking adaptability in stroke patients

Purpose The ability to adapt walking is important for safe ambulation. Assessments of impairments in walking adaptability with the Interactive Walkway may aid in the development of individualized therapy strategies of stroke patients. The Interactive Walkway is an overground walkway with Kinect v2 sensors for a markerless registration of full-body kinematics, which can be augmented with (gait-dependent) visual context to assess walking adaptability. This study aims to evaluate the potential of the Interactive Walkway as a new technology for assessing walking adaptability in stroke patients. Materials and methods: 30 stroke patients and 30 controls performed clinical tests, quantitative gait assessments and various walking-adaptability tasks on the Interactive Walkway. Outcome measures were compared between stroke patients and controls to examine known-groups validity. Pearson’s correlation coefficients were calculated to assess the relationship between walking-adaptability outcomes and commonly used clinical test scores of walking ability and spatiotemporal gait parameters of unconstrained walking. Results: Good known-groups validity for walking-adaptability outcomes was demonstrated. In addition, the vast majority of walking-adaptability outcomes did not or only moderately correlate with clinical test scores of walking ability and unconstrained walking parameters. Conclusion: Interactive Walkway walking-adaptability outcomes have good known-groups validity and complement standard clinical tests and spatiotemporal gait parameters. IMPLICATIONS FOR REHABILITATION The Interactive Walkway allows for a comprehensive walking-adaptability assessment. Good known-groups validity for walking-adaptability tasks was demonstrated and walking-adaptability tasks complemented clinical tests and gait parameters. The Interactive Walkway has potential for monitoring recovery of walking after stroke. Assessments of walking adaptability may contribute to individualized interventions.

The Reliability of Foot and Ankle Bone and Joint Kinematics Measured With Biplanar Videoradiography and Manual Scientific Rotoscoping.

The intricate motion of the small bones of the feet are critical for its diverse function. Accurately measuring the 3-dimensional (3D) motion of these bones has attracted much attention over the years and until recently, was limited to invasive techniques or quantification of functional segments using multi-segment foot models. Biplanar videoradiography and model-based scientific rotoscoping offers an exciting alternative that allows us to focus on the intricate motion of individual bones in the foot. However, scientific rotoscoping, the process of rotating and translating a 3D bone model so that it aligns with the captured x-ray images, is either semi- or completely manual and it is unknown how much human error affects tracking results. Thus, the aim of this study was to quantify the inter- and intra-operator reliability of manually rotoscoping in vivo bone motion of the tibia, talus, and calcaneus during running. Three-dimensional CT bone volumes and high-speed biplanar videoradiography images of the foot were acquired on six participants. The six-degree-of-freedom motions of the tibia, talus, and calcaneus were determined using a manual markerless registration algorithm. Two operators performed the tracking, and additionally, the first operator re-tracked all bones, to test for intra-operator effects. Mean RMS errors were 1.86 mm and 1.90° for intra-operator comparisons and 2.30 mm and 2.60° for inter-operator comparisons across all bones and planes. The moderate to strong similarity values indicate that tracking bones and joint kinematics between sessions and operators is reliable for running. These errors are likely acceptable for defining gross joint angles. However, this magnitude of error may limit the capacity to perform advanced analyses of joint interactions, particularly those that require precise (sub-millimeter) estimates of bone position and orientation. Optimizing the view and image quality of the biplanar videoradiography system as well as the automated tracking algorithms for rotoscoping bones in the foot are required to reduce these errors and the time burden associated with the manual processing.