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In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.

Purpose: Ultrasound imaging provides a low-cost, real-time modality to guide needle insertion procedures, but localizing the needle using conventional ultrasound images is often challenging. Estimating the needle trajectory can increase the success rate of ultrasound-guided needle interventions and improve patient comfort. In this study, a novel method is introduced to localize the needle trajectory in curvilinear ultrasound images based on the needle reflection pattern of circular ultrasound waves. Methods: A circular ultrasound wave was synthesized by sequentially firing the elements of a curvilinear transducer and recording the radio-frequency signals received by each element. Two features, namely, the large amplitude and repetitive reflection pattern, were used to identify the needle echoes in the received signals. The trajectory of the needle was estimated by fitting the arrival times of needle echoes to an equation that describes needle reflection of circular waves. The method was employed to estimate the trajectories of needles inserted in agar phantom, beef muscle, and porcine tissue specimens. Results: The maximum error rates of estimating the needle trajectories were on the order of 1 mm and 3° for the radial and azimuth coordinates, respectively. Conclusions: These results suggest that the proposed method can improve the robustness and accuracy of needle segmentation methods by adding signature-based detection of the needle trajectory in curvilinear ultrasound images. The method can be implemented on conventional ultrasound imaging systems.

Purpose: In image-guided therapy, real-time visualization of the anatomy and adjustments in the therapy plan due to anatomical motions during the procedure is of outmost importance. 3D ultrasound has the potential to enable this real-time monitoring; however, nonrigid registration of a sequence of 3D ultrasound volumes remains to be a challenging problem. The authors present our recent results on the development of a computationally inexpensive feature-based registration algorithm for elastic alignment of dynamic-3D ultrasoundimages. Methods: Our algorithm uses attribute vectors, based on the image intensity and gradient information, to perform feature-based matching in a sequence of 3D ultrasoundimages. Prior information from both the fixed and previous moving images is utilized to track features throughout the 3D image series. The algorithm has been compared to various publicly available registration techniques, i.e., the B-splines deformable registration, the symmetric forces Demons, and the fast free-form deformable registration method. Results: Using a series of validation experiments on datasets collected from carotid artery,liver, and kidney of 20 subjects, the authors demonstrate that the feature-based, B-splines, Demons, and fast free-form deformable registration techniques can all recover volume deformations in a 3D ultrasoundimage series with reasonable accuracy; however, the proposed feature-based registration technique has substantial computational advantage over the other approaches. Conclusions: The proposed feature-based registration technique has the potential for real-time implementation on a computationally inexpensive platform and has the capability of recovering nonrigid deformations in tissue with reasonable accuracy.

Purpose: Fusion of intraprocedure ultrasound and preprocedure CT data is proposed for guidance in percutaneous spinal injections, a common procedure for pain management. CT scan of the lumbar spine is usually collected in a supine position, whereas spinal injections are performed in prone or sitting positions. This leads to a difference in the spine curvature between CT and ultrasoundimages; as such, a single-body rigid registration approach cannot be used for the whole lumbar vertebrae. Methods: To compensate for the difference in the spinal curvature between the two imaging modalities, a multibody rigid registration algorithm is presented. The approach utilizes a point-based registration technique based on the unscented Kalman filter (UKF), taking as input segmented vertebrae surfaces in both CT and ultrasound data. Ultrasoundimages are automatically segmented using a dynamic programming method, while the CTimages are semiautomatically segmented using thresholding. The registration approach is designed to simultaneously align individual groups of points segmented from each vertebra in the two imaging modalities. A biomechanical model is developed to constrain the vertebrae transformation parameters during the registration and to ensure convergence. Results: The proposed methodology is evaluated on human spine phantoms and a sheep cadaver. Registrations on phantom data have a mean target registration error (TRE) of 1.99 mm in the region of interest and converged in 87% of cases. Registrations on sheep cadaver have a mean target registration error of 2.2 mm and converged in 82% of cases. Conclusions: The proposed technique can robustly and simultaneously register several vertebrae extracted from CTimages to the ultrasound volumes. The registration error below 2.2 mm is sufficient for most spinal injections.

Purpose: Prostate cancer brachytherapy relies on an accurate spatial registration between the implant needles and the TRUS image, called ''calibration''. The authors propose a new device and a fast, automatic method to calibrate the brachytherapy system in the operating room, with instant error feedback. Methods: A device was CAD-designed and precision-engineered, which mechanically couples a calibration phantom with an exact replica of the standard brachytherapy template. From real-time TRUS images acquired from the calibration device and processed by the calibration system, the coordinate transformation between the brachytherapy template and the TRUS images was computed automatically. The system instantly generated a report of the target reconstruction accuracy based on the current calibration outcome. Results: Four types of validation tests were conducted. First, 50 independent, real-time calibration trials yielded an average of 0.57 {+-} 0.13 mm line reconstruction error (LRE) relative to ground truth. Second, the averaged LRE was 0.37 {+-} 0.25 mm relative to ground truth in tests with six different commercial TRUS scanners operating at similar imaging settings. Furthermore, testing with five different commercial stepper systems yielded an average of 0.29 {+-} 0.16 mm LRE relative to ground truth. Finally, the system achieved an average of 0.56 {+-} 0.27 mmmore » target registration error (TRE) relative to ground truth in needle insertion tests through the template in a water tank. Conclusions: The proposed automatic, intraoperative calibration system for prostate cancer brachytherapy has achieved high accuracy, precision, and robustness.« less

Purpose: C-arm fluoroscopy reconstruction, such as that used in prostate brachytherapy, requires that the relative poses of the individual C-arm fluoroscopy images must be known prior to reconstruction. Radiographic fiducials can provide excellent C-arm pose tracking, but they need to be segmented in the image. The authors report an automated and unsupervised method that does not require prior segmentation of the fiducial. Methods: The authors compute the individual C-arm poses relative to a stationary radiographic fiducial of known geometry. The authors register a filtered 2D fluoroscopy image of the fiducial to its 3D model by using image intensity alone without prior segmentation. To enhance the C-arm images, the authors investigated a three-step cascade filter and a line enhancement filter. The authors tested the method on a composite fiducial containing beads, straight lines, and ellipses. Ground-truth C-arm pose was provided by a clinically proven method. Results: Using 111 clinical C-arm images and {+-}10 deg. and {+-}10 mm random perturbation around the ground-truth pose, a total of 2775 cases were evaluated. The average rotation and translation errors were 0.62 deg. (STD=0.31 deg.) and 0.72 mm (STD=0.55 mm) for the three-step filter and 0.67 deg. (STD=0.40 deg.) and 0.87 mm (STD=0.27 mm) usingmore » the line enhancement filter. Conclusions: The C-arm pose tracking method was sufficiently accurate and robust on human patient data for subsequent 3D implant reconstruction.« less

Purpose: In prostate brachytherapy, transrectal ultrasound(TRUS) is used to visualize the anatomy, while implanted seeds can be visualized by fluoroscopy. Intraoperative dosimetry optimization is possible using a combination of TRUS and fluoroscopy, but requires localization of the fluoroscopy-derived seed cloud, relative to the anatomy as seen on TRUS. The authors propose to develop a method of registration of TRUSimages and the implants reconstructed from fluoroscopy. Methods: A phantom was implanted with 48 seeds then imaged with TRUS and CT. Seeds were reconstructed from CT yielding a cloud of seeds. Fiducial-based ground-truth registration was established between the TRUS and CT.TRUSimages are filtered, compounded, and registered to the reconstructed implants by using an intensity-based metric. The authors evaluated a volume-to-volume and point-to-volume registration scheme. In total, seven TRUS filtering techniques and three image similarity metrics were analyzed. The method was also tested on human subject data captured from a brachytherapy procedure. Results: For volume-to-volume registration, noise reduction filter and normalized correlation metrics yielded the best result: An average of 0.54 ± 0.11 mm seed localization error relative to ground truth. For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: An average of 0.38 ± 0.19 mm seed localization error relative to the ground truth. In human patient data, C-arm fluoroscopyimages showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intraoperative fluoroscopyimaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86 ± 1.26 mm . Conclusions: Fully automated registration between TRUS and the reconstructed seeds performed well in ground-truth phantom experiments and qualitative observation showed adequate performance on early clinical patient data.