The Global Track Concept for Assessment of Engaging Hill-Sachs Defects in Anterior Shoulder Instability.

BACKGROUND: The glenoid track concept is used to determine preoperatively whether a Hill-Sachs defect is engaging or not. Currently, the glenoid track concept relies on measurements of bony structures as well as on the confines and elasticity of the rotator cuff as a reference point, which varies extensively among individuals and therefore limits the reliability and accuracy of this concept. PURPOSE: To evaluate the reliability of the global track concept, which determines the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head as a new reference point with the help of an automated image analysis software and 3-dimensional analysis of the humeral head. STUDY DESIGN: Controlled laboratory study. METHODS: Computed tomography scans of 100 patients treated for anterior shoulder instability with different sizes of Hill-Sachs defects were evaluated manually by 2 orthopaedic surgeons independently using the software OsiriX as well as automatically by using a dedicated prototype software (ImFusion). Obtained manual and automated measurements included the Hill-Sachs length, Hill-Sachs width, and Hill-Sachs depth of the defect; the Hill-Sachs interval (HSI); and the glenoid width for the glenoid track concept, as well as the angular distance of the Hill-Sachs defect from the center of the articular surface of the humeral head (global track concept). The reliability of the different measurement techniques was compared by calculating intraclass correlation coefficients (ICCs). RESULTS: There was a significant difference for all obtained parameters comparing manual and automatic measurements. For manually obtained parameters, measurements referring to bony boundaries (glenoid width, Hill-Sachs length, and Hill-Sachs width) showed good to excellent agreement (ICC, 0.86, 0.82, and 0.62, respectively), while measurements referring to soft tissue boundaries (HSI and glenoid track; ICC, 0.56 and 0.53, respectively) or not directly identifiable reference points (center of articular surface and global track) only showed fair reliability (ICC middle excursion, 0.42). When the same parameters were measured with the help of an automated software, good reliability for the glenoid track concept and excellent reliability for the global track concept in the middle excursion were achieved. CONCLUSION: The present study showed that the more complex global track measurements of humeral defects are more reliable than the current standard HSI and glenoid track measurements. However, this is only true when automated software is used to perform the measurements. CLINICAL RELEVANCE: Future studies using the new proposed method in combination with an automated software need to be conducted to determine critical threshold values for defects prone to engagement.

Reliability of Manual Measurements Versus Semiautomated Software for Glenoid Bone Loss Quantification in Patients With Anterior Shoulder Instability.

Background: The presence of glenoid bone defects is indicative in the choice of treatment for patients with anterior shoulder instability. In contrast to traditional linear- and area-based measurements, techniques such as the consideration of glenoid concavity have been proposed and validated. Purpose: To compare the reliability of linear (1-dimensional [1D]), area (2-dimensional [2D]), and concavity (3-dimensional [3D]) measurements to quantify glenoid bone loss performed manually and to analyze how automated measurements affect reliability. Study Design: Cohort study (diagnosis); Level of evidence, 3. Methods: Computed tomography images of 100 patients treated for anterior shoulder instability with differently sized glenoid defects were evaluated independently by 2 orthopaedic surgeons manually using conventional software (OsiriX; Pixmeo) as well as automatically with a dedicated prototype software program (ImFusion Suite; ImFusion). Parameters obtained included 1D (defect diameter, best-fit circle diameter), 2D (defect area, best-fit circle area), and 3D (bony shoulder stability ratio) measurements. Mean values and reliability as expressed by the intraclass correlation coefficient [ICC]) were compared between the manual and automated measurements. Results: When manually obtained, the measurements showed almost perfect agreement for 1D parameters (ICC = 0.83), substantial agreement for 2D parameters (ICC = 0.79), and moderate agreement for the 3D parameter (ICC = 0.48). When measurements were aided by automated software, the agreement between raters was almost perfect for all parameters (ICC = 0.90 for 1D, 2D, and 3D). There was a significant difference in mean values between manually versus automatically obtained measurements for 1D, 2D, and 3D parameters (P < .001 for all). Conclusion: While more advanced measurement techniques that take glenoid concavity into account are more accurate in determining the biomechanical relevance of glenoid bone loss, our study showed that the reliability of manually performed, more complex measurements was moderate.

Femur reconstruction in 3D ultrasound for orthopedic surgery planning.

PURPOSE: Derotation varisation osteotomy of the proximal femur in pediatric patients usually relies on 2-dimensional X-ray imaging, as CT and MRI still are disadvantageous when applied in small children either due to a high radiation exposure or the need of anesthesia. This work presents a radiation-free non-invasive tool to 3D-reconstruct the femur surface and measure relevant angles for orthopedic diagnosis and surgery planning from 3D ultrasound scans instead. METHODS: Multiple tracked ultrasound recordings are segmented, registered and reconstructed to a 3D femur model allowing for manual measurements of caput-collum-diaphyseal (CCD) and femoral anteversion (FA) angles. Novel contributions include the design of a dedicated phantom model to mimic the application ex vivo, an iterative registration scheme to overcome movements of a relative tracker only attached to the skin, and a technique to obtain the angle measurements. RESULTS: We obtained sub-millimetric surface reconstruction accuracy from 3D ultrasound on a custom 3D-printed phantom model. On a pre-clinical pediatric patient cohort, angular measurement errors were [Formula: see text] and eventually [Formula: see text] for CCD and FA angles, respectively, both within the clinically acceptable range. To obtain these results, multiple refinements of the acquisition protocol were necessary, ultimately reaching success rates of up to 67% for achieving sufficient surface coverage and femur reconstructions that allow for geometric measurements. CONCLUSION: Given sufficient surface coverage of the femur, clinically acceptable characterization of femoral anatomy is feasible from non-invasive 3D ultrasound. The acquisition protocol requires leg repositioning, which can be overcome using the presented algorithm. In the future, improvements of the image processing pipeline and more extensive surface reconstruction error assessments could enable more personalized orthopedic surgery planning using cutting templates.

Correction to: On the reproducibility of expert-operated and robotic ultrasound acquisitions.

The original version of this article unfortunately contained a mistake.

3D freehand ultrasound without external tracking using deep learning.

This work aims at creating 3D freehand ultrasound reconstructions from 2D probes with image-based tracking, therefore not requiring expensive or cumbersome external tracking hardware. Existing model-based approaches such as speckle decorrelation only partially capture the underlying complexity of ultrasound image formation, thus producing reconstruction accuracies incompatible with current clinical requirements. Here, we introduce an alternative approach that relies on a statistical analysis rather than physical models, and use a convolutional neural network (CNN) to directly estimate the motion of successive ultrasound frames in an end-to-end fashion. We demonstrate how this technique is related to prior approaches, and derive how to further improve its predictive capabilities by incorporating additional information such as data from inertial measurement units (IMU). This novel method is thoroughly evaluated and analyzed on a dataset of 800 in vivo ultrasound sweeps, yielding unprecedentedly accurate reconstructions with a median normalized drift of 5.2%. Even on long sweeps exceeding 20 cm with complex trajectories, this allows to obtain length measurements with median errors of 3.4%, hence paving the way toward translation into clinical routine.

Robotic ultrasound-guided facet joint insertion.

PURPOSE: Facet joint insertion is a common treatment of chronic pain in the back and spine. This procedure is often performed under fluoroscopic guidance, where the staff's repetitive radiation exposure remains an unsolved problem. Robotic ultrasound (rUS) has the potential to reduce or even eliminate the use of radiation by using ultrasound with a robotic-guided needle insertion. This work presents first clinical data of rUS-based needle insertions extending previous work of our group. METHODS: Our system implements an automatic US acquisition protocol combined with a calibrated needle targeting system. This approach assists the physician by positioning the needle holder on a trajectory selected in a 3D US volume of the spine. RESULTS: By the time of submission, nine facets were treated with our approach as first data from an ongoing clinical study. The insertion success rate was shown to be comparable to current clinical practice. Furthermore, US imaging offers additional anatomical context for needle trajectory planning. CONCLUSION: This work shows first clinical data for robotic ultrasound-assisted facet joint insertion as a promising solution that can easily be incorporated into the clinical workflow. Presented results show the clinical value of such a system.

On the reproducibility of expert-operated and robotic ultrasound acquisitions.

PURPOSE: We present the evaluation of the reproducibility of measurements performed using robotic ultrasound imaging in comparison with expert-operated sonography. Robotic imaging for interventional procedures may be a valuable contribution, but requires reproducibility for its acceptance in clinical routine. We study this by comparing repeated measurements based on robotic and expert-operated ultrasound imaging. METHODS: Robotic ultrasound acquisition is performed in three steps under user guidance: First, the patient is observed using a 3D camera on the robot end effector, and the user selects the region of interest. This allows for automatic planning of the robot trajectory. Next, the robot executes a sweeping motion following the planned trajectory, during which the ultrasound images and tracking data are recorded. As the robot is compliant, deviations from the path are possible, for instance due to patient motion. Finally, the ultrasound slices are compounded to create a volume. Repeated acquisitions can be performed automatically by comparing the previous and current patient surface. RESULTS: After repeated image acquisitions, the measurements based on acquisitions performed by the robotic system and expert are compared. Within our case series, the expert measured the anterior-posterior, longitudinal, transversal lengths of both of the left and right thyroid lobes on each of the 4 healthy volunteers 3 times, providing 72 measurements. Subsequently, the same procedure was performed using the robotic system resulting in a cumulative total of 144 clinically relevant measurements. Our results clearly indicated that robotic ultrasound enables more repeatable measurements. CONCLUSIONS: A robotic ultrasound platform leads to more reproducible data, which is of crucial importance for planning and executing interventions.

3D ultrasound registration-based visual servoing for neurosurgical navigation.

PURPOSE: We present a fully image-based visual servoing framework for neurosurgical navigation and needle guidance. The proposed servo-control scheme allows for compensation of target anatomy movements, maintaining high navigational accuracy over time, and automatic needle guide alignment for accurate manual insertions. METHOD: Our system comprises a motorized 3D ultrasound (US) transducer mounted on a robotic arm and equipped with a needle guide. It continuously registers US sweeps in real time with a pre-interventional plan based on CT or MR images and annotations. While a visual control law maintains anatomy visibility and alignment of the needle guide, a force controller is employed for acoustic coupling and tissue pressure. We validate the servoing capabilities of our method on a geometric gel phantom and real human anatomy, and the needle targeting accuracy using CT images on a lumbar spine gel phantom under neurosurgery conditions. RESULTS: Despite the varying resolution of the acquired 3D sweeps, we achieved direction-independent positioning errors of [Formula: see text] mm and [Formula: see text], respectively. Our method is capable of compensating movements of around 25 mm/s and works reliably on human anatomy with errors of [Formula: see text] mm. In all four manual insertions by an expert surgeon, a needle could be successfully inserted into the facet joint, with an estimated targeting accuracy of [Formula: see text] mm, superior to the gold standard. CONCLUSION: The experiments demonstrated the feasibility of robotic ultrasound-based navigation and needle guidance for neurosurgical applications such as lumbar spine injections.

Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study.

Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn is used to plan 3D ultrasound trajectories. These MRI-based trajectories are followed autonomously by the robot and are further refined online using automatic MRI/US registration. Despite the low spatial resolution of structured light scanners, the initial planned acquisition path can be followed with an accuracy of 2.46 ± 0.96 mm. This leads to a good initialization of the MRI/US registration: the 3D-scan-based alignment for planning and acquisition shows an accuracy (distance between planned ultrasound and MRI) of 4.47 mm, and 0.97 mm after an online-update of the calibration based on a closed loop registration.

Dual-robot ultrasound-guided needle placement: closing the planning-imaging-action loop.

PURPOSE: Precise needle placement is an important task during several medical procedures. Ultrasound imaging is often used to guide the needle toward the target region in soft tissue. This task remains challenging due to the user's dependence on image quality, limited field of view, moving target, and moving needle. In this paper, we present a novel dual-robot framework for robotic needle insertions under robotic ultrasound guidance. METHOD: We integrated force-controlled ultrasound image acquisition, registration of preoperative and intraoperative images, vision-based robot control, and target localization, in combination with a novel needle tracking algorithm. The framework allows robotic needle insertion to target a preoperatively defined region of interest while enabling real-time visualization and adaptive trajectory planning to provide safe and quick interactions. We assessed the framework by considering both static and moving targets embedded in water and tissue-mimicking gelatin. RESULTS: The presented dual-robot tracking algorithms allow for accurate needle placement, namely to target the region of interest with an error around 1 mm. CONCLUSION: To the best of our knowledge, we show the first use of two independent robots, one for imaging, the other for needle insertion, that are simultaneously controlled using image processing algorithms. Experimental results show the feasibility and demonstrate the accuracy and robustness of the process.

Multimodal image-guided prostate fusion biopsy based on automatic deformable registration.

PURPOSE: Transrectal ultrasound (TRUS)-guided random prostate biopsy is, in spite of its low sensitivity, the gold standard for the diagnosis of prostate cancer. The recent advent of PET imaging using a novel dedicated radiotracer, [Formula: see text]-labeled prostate-specific membrane antigen (PSMA), combined with MRI provides improved pre-interventional identification of suspicious areas. This work proposes a multimodal fusion image-guided biopsy framework that combines PET-MRI images with TRUS, using automatic segmentation and registration, and offering real-time guidance. METHODS: The prostate TRUS images are automatically segmented with a Hough transform-based random forest approach. The registration is based on the Coherent Point Drift algorithm to align surfaces elastically and to propagate the deformation field calculated from thin-plate splines to the whole gland. RESULTS: The method, which has minimal requirements and temporal overhead in the existing clinical workflow, is evaluated in terms of surface distance and landmark registration error with respect to the clinical ground truth. Evaluations on agar-gelatin phantoms and clinical data of 13 patients confirm the validity of this approach. CONCLUSION: The system is able to successfully map suspicious regions from PET/MRI to the interventional TRUS image.

3D velocity field and flow profile reconstruction from arbitrarily sampled Doppler ultrasound data.

With the need for adequate analysis of blood flow dynamics, different maging modalities have been developed to measure varying blood velocities over time. Due to its numerous advantages, Doppler ultrasound sonography remains one of the most widely used techniques in clinical routine, but requires additional preprocessing to recover 3D velocity information. Despite great progress in the last years, recent approaches do not jointly consider spatial and temporal variation in blood flow. In this work, we present a novel gating- and compounding-free method to simultaneously reconstruct a 3D velocity field and a temporal flow profile from arbitrarily sampled Doppler ultrasound measurements obtained from multiple directions. Based on a laminar flow assumption, a patch-wise B-spline formulation of blood velocity is coupled for the first time with a global waveform model acting as temporal regularization. We evaluated our method on three virtual phantom datasets, demonstrating robustness in terms of noise, angle between measurements and data sparsity, and applied it successfully to five real case datasets of carotid artery examination.

Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals.

Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7ms for the QRS duration and 6.5° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP.

Fast data-driven calibration of a cardiac electrophysiology model from images and ECG.

Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in aproximately 3 s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5 ms for QRS duration and 2 degree for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization.