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We report a rare case of middle cerebral artery (MCA) stroke in a teenage girl with initial improvement, followed by progression to malignant MCA infarction, requiring an urgent decompressive hemicraniectomy (DHC). Additionally, we report improvement in all areas, including language, comprehension, and motor skills at discharge and the 4-month follow-up. This rare presentation highlights the importance of monitoring the neurological status of a patient with an MCA infarct for progression to a life-threatening malignant MCA infarct. This case report also highlights the importance of consideration of DHC for a favorable outcome of the MMCA infarction.

Although musculoskeletal magnetic resonance imaging (MRI) plays a dominant role in characterizing abnormalities, novel computed tomography (CT) techniques have found an emerging niche in several scenarios such as trauma, gout, and the characterization of pathologic biomechanical states during motion and weight-bearing. Recent developments and advancements in the field of musculoskeletal CT include 4-dimensional, cone-beam (CB), and dual-energy (DE) CT. Four-dimensional CT has the potential to quantify biomechanical derangements of peripheral joints in different joint positions to diagnose and characterize patellofemoral instability, scapholunate ligamentous injuries, and syndesmotic injuries. Cone-beam CT provides an opportunity to image peripheral joints during weight-bearing, augmenting the diagnosis and characterization of disease processes. Emerging CBCT technologies improved spatial resolution for osseous microstructures in the quantitative analysis of osteoarthritis-related subchondral bone changes, trauma, and fracture healing. Dual-energy CT-based material decomposition visualizes and quantifies monosodium urate crystals in gout, bone marrow edema in traumatic and nontraumatic fractures, and neoplastic disease. Recently, DE techniques have been applied to CBCT, contributing to increased image quality in contrast-enhanced arthrography, bone densitometry, and bone marrow imaging. This review describes 4-dimensional CT, CBCT, and DECT advances, current logistical limitations, and prospects for each technique.

Cone-beam CT (CBCT) is widely used for guidance in interventional radiology but it is susceptible to motion artifacts. Motion in interventional CBCT features a complex combination of diverse sources including quasi-periodic, consistent motion patterns such as respiratory motion, and aperiodic, quasi-random, motion such as peristalsis. Recent developments in image-based motion compensation methods include approaches that combine autofocus techniques with deep learning models for extraction of image features pertinent to CBCT motion. Training of such deep autofocus models requires the generation of large amounts of realistic, motion-corrupted CBCT. Previous works on motion simulation were mostly focused on quasi-periodic motion patterns, and reliable simulation of complex combined motion with quasi-random components remains an unaddressed challenge. This work presents a framework aimed at synthesis of realistic motion trajectories for simulation of deformable motion in soft-tissue CBCT. The approach leveraged the capability of conditional generative adversarial network (GAN) models to learn the complex underlying motion present in unlabeled, motion-corrupted, CBCT volumes. The approach is designed for training with unpaired clinical CBCT in an unsupervised fashion. This work presents a first feasibility study, in which the model was trained with simulated data featuring known motion, providing a controlled scenario for validation of the proposed approach prior to extension to clinical data. Our proof-of-concept study illustrated the potential of the model to generate realistic, variable simulation of CBCT deformable motion fields, consistent with three trends underlying the designed training data: i) the synthetic motion induced only diffeomorphic deformations – with Jacobian Determinant larger than zero; ii) the synthetic motion showed median displacement of 0. 5 mm in regions predominantly static in the training (e.g., the posterior aspect of the patient laying supine), compared to a median displacement of 3.8 mm in regions more prone to motion in the training; and iii) the synthetic motion exhibited predominant directionality consistent with the training set, resulting in larger motion in the superior-inferior direction (median and maximum amplitude of 4.58 mm and 20 mm, > 2x larger than the two remaining direction). Together, the proposed framework shows the feasibility for realistic motion simulation and synthesis of variable CBCT data.

PURPOSE: Cone-beam CT has become commonplace for 3D guidance in interventional radiology (IR), especially for vascular procedures in which identification of small vascular structures is crucial. However, its long image acquisition time poses a limit to image quality due to soft-tissue deformable motion that hampers visibility of small vessels. Autofocus motion compensation has shown promising potential for soft-tissue deformable motion compensation, but it lacks specific target to the imaging task. This work presents an approach for deformable motion compensation targeted at imaging of vascular structures. METHODS: The proposed method consists on a two-stage framework for: i) identification of contrast-enhanced blood vessels in 2D projection data and delineation of an approximate region covering the vascular target in the volume space, and, ii) a novel autofocus approach including a metric designed to promote the presence of vascular structures acting solely in the region of interest. The vesselness of the image is quantified via evaluation of the properties of the 3D image Hessian, yielding a vesselness filter that gives larger values to voxels candidate to be part of a tubular structure. A cost metric is designed to promote large vesselness values and spatial sparsity, as expected in regions of fine vascularity. A targeted autofocus method was designed by combining the presented metric with a conventional autofocus term acting outside of the region of interest. The resulting method was evaluated on simulated data including synthetic vascularity merged with real anatomical features obtained from MDCT data. Further evaluation was obtained in two clinical datasets obtained during TACE procedures with a robotic C-arm (Artis Zeego, Siemens Healthineers). RESULTS: The targeted vascular autofocus effectively restored the shape and contrast of the contrast-enhanced vascularity in the simulation cases, resulting in improved visibility and reduced artifacts. Segmentations performed with a single threshold value on the target vascular regions yielded a net increase of up to 42% in DICE coefficient computed against the static reference. Motion compensation in clinical datasets resulted in improved visibility of vascular structures, observed in maximum intensity projections of the contrast-enhanced liver vessel tree. CONCLUSION: Targeted motion compensation for vascular imaging showed promising performance for increased identification of small vascular structures in presence of motion. The development of autofocus metrics and methods tailored to vascular imaging opens the way for reliable compensation of deformable motion while preserving the integrity of anatomical structures in the image.

Purpose. Target localization in pulmonary interventions (e.g. transbronchial biopsy of a lung nodule) is challenged by deformable motion and may benefit from fluoroscopic overlay of the target to provide accurate guidance. We present and evaluate a 3D–2D image registration method for fluoroscopic overlay in the presence of tissue deformation using a multi-resolution/multi-scale (MRMS) framework with an objective function that drives registration primarily by soft-tissue image gradients. Methods. The MRMS method registers 3D cone-beam CT to 2D fluoroscopy without gating of respiratory phase by coarse-to-fine resampling and global-to-local rescaling about target regions-of-interest. A variation of the gradient orientation ( GO ) similarity metric (denoted G O ′ ) was developed to downweight bone gradients and drive registration via soft-tissue gradients. Performance was evaluated in terms of projection distance error at isocenter (PDEiso). Phantom studies determined nominal algorithm parameters and capture range. Preclinical studies used a freshly deceased, ventilated porcine specimen to evaluate performance in the presence of real tissue deformation and a broad range of 3D–2D image mismatch. Results. Nominal algorithm parameters were identified that provided robust performance over a broad range of motion (0–20 mm), including an adaptive parameter selection technique to accommodate unknown mismatch in respiratory phase. The G O ′ metric yielded median PDEiso = 1.2 mm, compared to 6.2 mm for conventional GO . Preclinical studies with real lung deformation demonstrated median PDEiso = 1.3 mm with MRMS + G O ′ registration, compared to 2.2 mm with a conventional transform. Runtime was 26 s and can be reduced to 2.5 s given a prior registration within ∼5 mm as initialization. Conclusions. MRMS registration via soft-tissue gradients achieved accurate fluoroscopic overlay in the presence of deformable lung motion. By driving registration via soft-tissue image gradients, the method avoided false local minima presented by bones and was robust to a wide range of motion magnitude.

Purpose Our aim was to develop a high-quality, mobile cone-beam computed tomography (CBCT) scanner for point-of-care detection and monitoring of low-contrast, soft-tissue abnormalities in the head/brain, such as acute intracranial hemorrhage (ICH). This work presents an integrated framework of hardware and algorithmic advances for improving soft-tissue contrast resolution and evaluation of its technical performance with human subjects. Methods Four configurations of a CBCT scanner prototype were designed and implemented to investigate key aspects of hardware (including system geometry, antiscatter grid, bowtie filter) and technique protocols. An integrated software pipeline (c.f., a serial cascade of algorithms) was developed for artifact correction (image lag, glare, beam hardening and x-ray scatter), motion compensation, and three-dimensional image (3D) reconstruction [penalized weighted least squares (PWLS), with a hardware-specific statistical noise model]. The PWLS method was extended in this work to accommodate multiple, independently moving regions with different resolution (to address both motion compensation and image truncation). Imaging performance was evaluated quantitatively and qualitatively with 41 human subjects in the neurosciences critical care unit (NCCU) at our institution. Results The progression of four scanner configurations exhibited systematic improvement in the quality of raw data by variations in system geometry (source-detector distance), antiscatter grid, and bowtie filter. Quantitative assessment of CBCT images in 41 subjects demonstrated: ~70% reduction in image nonuniformity with artifact correction methods (lag, glare, beam hardening, and scatter); ~40% reduction in motion-induced streak artifacts via the multi-motion compensation method; and ~15% improvement in soft-tissue contrast-to-noise ratio (CNR) for PWLS compared to filtered backprojection (FBP) at matched resolution. Each of these components was important to improve contrast resolution for point-of-care cranial imaging. Conclusions This work presents the first application of a high-quality, point-of-care CBCT system for imaging of the head/ brain in a neurological critical care setting. Hardware configuration iterations and an integrated software pipeline for artifacts correction and PWLS reconstruction mitigated artifacts and noise to achieve image quality that could be valuable for point-of-care detection and monitoring of a variety of intracranial abnormalities, including ICH and hydrocephalus.

Frouros is an open-source Python library capable of detecting drift in machine learning systems. It provides a combination of classical and more recent algorithms for drift detection: both concept and data drift. We have designed it with the objective of making it compatible with any machine learning framework and easily adaptable to real-world use cases. The library is developed following a set of best development and continuous integration practices to ensure ease of maintenance and extensibility. The source code is available at https://github.com/IFCA/frouros., Comment: 11 pages, 1 table

Background Most pleural procedures need the presence of a moderate effusion to allow safe access to the pleural space. We propose a technique to allow safe access in patients with a drained pleural space who require further pleural evaluation or treatment during the same hospital stay. Methods This was a retrospective study. All patients who underwent any pleural intervention with a prior infusion of fluid in the pleural space using a pre-existing chest tube (≤14 Fr) were included. Before the pleural intervention, warm saline was infused into the pleural space through the small-bore chest tube until enough fluid was detected on thoracic ultrasound to allow pleural access. Data on patient demographics, indication for the pleural procedure, and patient outcome was analyzed. Results A total of 22 patients with pleural disease underwent definitive pleural procedure facilitated by fluid infusion. Median volume of fluid infused was 1000 mL (850, 1500 mL). The median time between the initial chest tube insertion and the subsequent definitive pleural procedure was 3 days (2, 7 d). All procedures were completed successfully. One patient had a hemothorax secondary to fluid infusion. Conclusion Fluid infusion through a chest tube is a feasible technique for patients that require a pleural procedure and have minimal fluid after initial pleural drainage. This approach may facilitate pleural procedures, reduce incidence of complications, and expedite the diagnosis and treatment of patients with pleural diseases. Pressure infusers should not be used during this procedure as there is a theoretical increased risk of complications.

Magic angle field spinning (MAFS) is a Nuclear Magnetic Resonance (NMR) spectroscopy technique used in NMR Compact Devices to enhance the spectral resolution. In this technique, the sample is placed inside a magnet which generates a magnetic flux density oriented at 54.74° with respect to the sample main axis and spun at high frequencies. We present here, the design and construction of a novel magnet that generates a magnetic field according to the MAFS technique. The prototype generates two radio frequency and one dc magnetic fields by the combined action of three electromagnets. This combination results in a magnetic flux density of ~10 mT, with field deviations ≤1% inside a cylindrical volume of $30\times 30$ mm, an orientation easily controlled by field amplitudes and precessing at frequencies up to 12 kHz. The magnet has been characterized by using a novel Flux+Gauss meter (FGM) built and calibrated in-house . The FGM is able to measure ac and dc magnetic fields along seven different longitudinal axes, one of them corresponds to the longitudinal axis of the prototype. The other six axes are placed at six angular orientations on a 30-mm-diameter circumference. The characterization of the magnet includes field profiles of the RF and dc fields along the FGM axes and deviations of the field in the $30\times 30$ mm cylinder. The homogeneity is evaluated as the relative differences between the field measured at the centered axis and the field measured along the six extra axes for both RF and dc fields.

OBJECTIVES: To evaluate the improvement in extremity Cone-Beam Computed Tomography (CBCT) image quality in datasets with motion artifact using a motion compensation method based on maximizing image sharpness. METHODS: Following IRB approval, retrospective analysis of 308 CBCT scans of lower extremities was performed by a fellowship-trained musculoskeletal radiologist to identify images with moderate to severe motion artifact. Twenty-four scans of 22 patients (18 male, 4 female; mean 32 yo, ranging 21-74 yo) were chosen for inclusion. Sharp (bone) and smooth (soft-tissue) reconstructions were processed using the motion compensation algorithm. Two experts rated visualization of trabecular bone, cortical bone, joint spaces, and tendon on a 9-level Likert scale with and without motion compensation (a total of 96 datasets). Visual Grading Characteristics (VGC) was used to quantitatively determine the difference in image quality following motion compensation. Intra-class correlation coefficient (ICC) was obtained to assess inter-observer agreement. RESULTS: Motion-compensated images exhibited appreciable reduction in artifacts. The observer study demonstrated the associated improvement in diagnostic quality. The fraction of cases receiving scores better than “Fair” increased from less than 10% without compensation to 40% - 70% following compensation, depending on the task. The area under the VGC curve was 0.75 (tendon) to 0.85 (cortical bone), confirming preference for motion compensated images. ICC values showed excellent agreement between readers before (ICC range: 0.8-0.91) and after motion compensation (ICC range: 0.92-0.97). CONCLUSION: The motion compensation algorithm significantly improved the visualization of bone and soft-tissue structures in extremity CBCT for cases exhibiting patient motion.

Purpose of review The purpose of this review is to demonstrate advances in the medical treatment of pulmonary arterial hypertension (PAH). Reviewed will be the evidence that favors the use of risk assessment in the treatment of PAH. Optimization of combination therapy depending on the risk or worsening will be reviewed. Finally, recent advances in new treatment strategies will be mentioned. Recent findings The use of therapies in sequence or in combination for the treatment of PAH has been shown to decrease morbidity and mortality. Tailoring these treatment strategies to a risk of worsening has been shown to decrease mortality and time to clinical worsening because of PAH. In addition, there have been several advances in the development of other medications separate from the three known pathogenic pathways in PAH. Summary In the last 15 years, 12 specific therapies have been approved for PAH. These therapies target three separate pathogenic pathways [the endothelin (ET), nitric oxide (NO) and prostacyclin (PGI2)]. As a result, treatment guidelines have tailored the treatment of PAH with these medications either as single drug therapy or in combination. Recently, other treatment pathways have been explored as new strategies for the treatment of PAH.

Obstructive sleep apnea is characterized by prolonged partial upper airway obstruction or intermittent complete obstruction that disrupts normal ventilation during sleep and alters normal sleep patterns. Patients with obstructive sleep apnea tend to develop neurocognitive, cardiovascular, behavioral, attention issues, and poor academic performance. Therefore, it is essential to diagnose and treat obstructive sleep apnea early and avoid significant and long-lasting adverse outcomes. Most commonly, upper airway obstruction is caused by enlarged lymphoid tissues within the upper airway, and therefore adenotonsillectomy is considered as the first-line treatment of obstructive sleep apnea in children. Fifty to 70% of patients who have obstructive sleep apnea and treated by surgery are not entirely cured on follow-up polysomnography. In light of this, it is recommended that patients with suspected obstructive sleep apnea undergo a thorough evaluation, and all potential risk factors are identified and treated. The purpose of this review is to familiarize pediatricians with developmental, anatomical, and physiological risk factors involved in the development of obstructive sleep apnea. Additionally, we will present an array of evaluation techniques that can offer adequate assessment of the patient's upper airway anatomy and physiology.

Purpose. Patient motion artifacts present a prevalent challenge to image quality in interventional cone-beam CT (CBCT). We propose a novel reference-free similarity metric (DL-VIF) that leverages the capability of deep convolutional neural networks (CNN) to learn features associated with motion artifacts within realistic anatomical features. DL-VIF aims to address shortcomings of conventional metrics of motion-induced image quality degradation that favor characteristics associated with motion-free images, such as sharpness or piecewise constancy, but lack any awareness of the underlying anatomy, potentially promoting images depicting unrealistic image content. DL-VIF was integrated in an autofocus motion compensation framework to test its performance for motion estimation in interventional CBCT. Methods. DL-VIF is a reference-free surrogate for the previously reported visual image fidelity (VIF) metric, computed against a motion-free reference, generated using a CNN trained using simulated motion-corrupted and motion-free CBCT data. Relatively shallow (2-ResBlock) and deep (3-Resblock) CNN architectures were trained and tested to assess sensitivity to motion artifacts and generalizability to unseen anatomy and motion patterns. DL-VIF was integrated into an autofocus framework for rigid motion compensation in head/brain CBCT and assessed in simulation and cadaver studies in comparison to a conventional gradient entropy metric. Results. The 2-ResBlock architecture better reflected motion severity and extrapolated to unseen data, whereas 3-ResBlock was found more susceptible to overfitting, limiting its generalizability to unseen scenarios. DL-VIF outperformed gradient entropy in simulation studies yielding average multi-resolution structural similarity index (SSIM) improvement over uncompensated image of 0.068 and 0.034, respectively, referenced to motion-free images. DL-VIF was also more robust in motion compensation, evidenced by reduced variance in SSIM for various motion patterns (σ DL-VIF = 0.008 versus σ gradient entropy = 0.019). Similarly, in cadaver studies, DL-VIF demonstrated superior motion compensation compared to gradient entropy (an average SSIM improvement of 0.043 (5%) versus little improvement and even degradation in SSIM, respectively) and visually improved image quality even in severely motion-corrupted images. Conclusion: The studies demonstrated the feasibility of building reference-free similarity metrics for quantification of motion-induced image quality degradation and distortion of anatomical structures in CBCT. DL-VIF provides a reliable surrogate for motion severity, penalizes unrealistic distortions, and presents a valuable new objective function for autofocus motion compensation in CBCT.

Purpose of Review This review presents an overview of the known neurocritical care complications of severe acute respiratory virus 2 (SARS-CoV-2). We present readers with a review of the literature of severe neurologic complications of SARS-CoV-2 and cases from our institution to illustrate these conditions. Recent Findings Neurologic manifestations are being increasingly recognized in the literature. Some patients can have severe neurologic manifestations, though the true prevalence is unknown. Summary Severe neurologic complications of COVID-19 include large vessel occlusion ischemic stroke, intracranial hemorrhage, encephalitis, myelitis, Guillain-Barre syndrome, status epilepticus, posterior reversible encephalopathy syndrome, and hypoxic-ischemic encephalopathy. These conditions can manifest in COVID-19 patients even in the absence of risk factors and must be promptly identified as they can have a high mortality if left untreated.

Purpose: Cone-beam CT is a common approach for 3D guidance in interventional radiology (IR), but its long image acquisition time poses a limit to image quality due to complex deformable motion of soft-tissue structures. Multi-region autofocus optimization showed successful compensation of deformable motion with gains in image quality. However, the use of sub-optimal conventional autofocus metrics, accompanied by the high-dimensionality and non-convexity of the optimization problem result in challenged convergence. This work presents a learning-based approach to the design of autofocus metrics tailored to the specific problem of motion compensation in soft-tissue CBCT. Methods: A deep convolutional neural network (CNN) was used to automatically extract image features quantifying the local motion amplitude and principal direction in volumetric regions of interest (128 x 128x 128 voxels) of a motion contaminated volume. The estimated motion amplitude is then used as the core component of the cost function of the deformable autofocus method, complemented by a regularization term favoring similar motion for regions close in space. The network consists of a siamese arrangement of three branches acting on the three central orthogonal planes of the volumetric ROI. The network was trained with simulated data generated from publicly available CT datasets, including deformable motion fields from which volumetric ROIs with locally rigid motion were extracted. The performance of motion amplitude estimation and of the final CNN-based deformable autofocus were assessed on synthetic CBCT data generated similarly to the training dataset and featuring deformable motion fields with 1 to 5 components with random direction and random amplitude ranging from 0 mm to 50 mm. Results: Predicted local motion amplitude showed good agreement with the true values, showing a liner relationship (R2 = 0.96, slope 0.95), and slight underestimation of the motion amplitude. Absolute errors in total motion amplitude and individual components remained < 2 mm throughout the explored range. Relative errors were higher for low amplitude motion, pointing to need of a larger training cohort, more focused on low motion amplitude scenarios. Motion compensation with the learning-based metric showed successful compensation of motion artifacts in single ROI environments, with 40% reduction in median RMSE using the motion static image as reference. Deformable motion compensation resulted in better visualization of soft-tissue structures, and overall sharper image details, with slight residual motion artifacts for regions combining moderate motion amplitude with complex anatomical structures. Conclusion: Deformable motion compensation with automatically learned autofocus metrics was proved feasible, opening the way to the design of metrics with potential for more reliable performance and easier optimization than conventional autofocus metrics.

Purpose: Intraoperative cone-beam CT (CBCT) plays an important role in neurosurgical guidance but is conventionally limited to high-contrast bone visualization. This work reports a high-fidelity artifacts correction pipeline to advance image quality beyond conventional limits and achieve soft-tissue contrast resolution even in the presence of multiple metal objects – specifically, a stereotactic head frame. Methods: A new metal artifact reduction (MAR) method was developed based on a convolutional neural network (CNN) that simultaneously estimates metal-induced bias and metal path length in the projection domain. To improve generalizability of the network, a physics-based method was developed to generate highly accurate simulated, metalcontaminated projection training data. The MAR method was integrated with previously proposed artifacts correction methods (lag, glare, scatter, and beam-hardening) to form a high-fidelity artifacts correction pipeline. The proposed methods were tested using an intraoperative CBCT system (O-arm, Medtronic) emulating a realistic setup in stereotactic neurosurgery, including nominal (20 cm) and extended (40 cm) field of view (FOV) protocols. Results: The physics-based data generation method provided accurate simulation of metal in projection data, including scatter, polyenergetic, quantum noise, and electronic noise effects. The artifacts correction pipeline was able to accommodate both 20 cm and 40 cm FOV protocols and demonstrated ~80% improvement in image uniformity and ~20% increase in contrast-to-noise ratio (CNR). Fully corrected images in the smaller FOV mode exhibited ~32% increase in CNR compared to the 40 cm FOV mode, showing the method’s ability to handle truncated metal objects outside the FOV. Conclusion: The image quality of intraoperative CBCT was greatly improved with the proposed artifacts correction pipeline, with clear improvement in soft-tissue contrast resolution (e.g., cerebral ventricles) even in the presence of a complex metal stereotactic frame. Such capability gives clearer visualization of structures of interest for intracranial neurosurgery, and it provides an important basis for future work aiming to deformably register preoperative MRI to intraoperative CBCT. Ongoing work includes clinical studies now underway.

Purpose: Pelvic fracture fixation is a challenging procedure that commonly relies on 2D fluoroscopic guidance to place guidewires within complex bone corridors. Prior work reported on a video-on-drill navigation system for guidewire insertion as a potential solution. Here, we assess performance across a range of hardware components to help guide the design of future system prototypes with respect to clinical requirements. Methods: The video-on-drill system uses a camera rigidly mounted on the drill and multimodality fiducial markers (optical and radio-opaque) to provide real-time trajectory visualization. This work reports on the selection of a new camera+lens configuration. Configurations were assessed across two cameras (referred to as the ArduCam and ELP) and five lens options (A45-A90). Clinical requirements were specified by an orthopaedic-surgeon in terms of the nominal drill operating distance (𝐷) and operating area (𝐴). Performance was evaluated in terms of the accuracy of fiducial marker pose estimation (δ𝐷), and the field of view (FOV). Results: At matched FOV, the accuracy for the ELP camera was significantly better (p < 0.01) with median δ𝐷 of 1.26 mm (1.1 mm IQR) compared to the ArduCam [median δ𝐷 of 1.85 mm (2.0 mm IQR)]. The accuracy of the A45 and A55 lens was found to be suitable (δ𝐷 < 2 mm) while providing sufficient FOV at nominal drill operating conditions. Conclusion: With respect to application requirements, the camera+lens combination (ELP+Ardu55) was identified to provide the best performance, serving as an important precursor to future design iterations of the video-on-drill system.

Grado en Ingeniería Química

Background and Purpose: Coronavirus disease 2019 (COVID-19) has been associated with an increased incidence of thrombotic events, including stroke. However, characteristics and outcomes of COVID-19 patients with stroke are not well known. Methods: We conducted a retrospective observational study of risk factors, stroke characteristics, and short-term outcomes in a large health system in New York City. We included consecutively admitted patients with acute cerebrovascular events from March 1, 2020 through April 30, 2020. Data were stratified by COVID-19 status, and demographic variables, medical comorbidities, stroke characteristics, imaging results, and in-hospital outcomes were examined. Among COVID-19-positive patients, we also summarized laboratory test results. Results: Of 277 patients with stroke, 105 (38.0%) were COVID-19-positive. Compared with COVID-19-negative patients, COVID-19-positive patients were more likely to have a cryptogenic (51.8% versus 22.3%, P P =0.02), parietal ( P =0.002), occipital ( P =0.002), and cerebellar ( P =0.028) regions. In COVID-19-positive patients, mean coagulation markers were slightly elevated (prothrombin time 15.4±3.6 seconds, partial thromboplastin time 38.6±24.5 seconds, and international normalized ratio 1.4±1.3). Outcomes were worse among COVID-19-positive patients, including longer length of stay ( P P =0.017), and greater rate of neurological worsening during admission ( P P Conclusions: Baseline characteristics in patients with stroke were similar comparing those with and without COVID-19. However, COVID-19-positive patients were more likely to experience stroke in a lobar location, more commonly had a cryptogenic cause, and had worse outcomes.

Introduction: Cook Formula stents are widely used in pediatric cardiology owing to their low profile and availability in a variety of sizes. Literature regarding the long-term outcome of these stents is limited. Methods: A single center retrospective cohort study was conducted to investigate the long-term outcome of Cook Formula stents. Demographic and catheterization data were collected for all patients who underwent stent implantation between January 2014 and March 2020. Patients with stents at sites other than the aorta and pulmonary arteries were excluded. Generalized Estimating Equation model was used to determine predictors of expansibility of stents. Results: 48 patients with 70 stents were included in the study. Of these, 36 stents had no subsequent balloon dilation while 34 stents had one or more balloon dilations during a median follow-up time of 33.3 months. No stent related mortality or stent fractures were seen. 2 stents embolized to a distal location. No decline in median narrowest vessel diameter was noted between any two subsequent catheterizations. Age (OR 1.0, 95% CI 0.13 - 1.0003), sex (OR 0.45, 95% CI 0.08 - 2.5), stent diameter of 5, 6, 7 mm compared to 4 mm (OR 2.6, 95% CI 0.13 - 55.1; OR 1.3, 95% CI 0.13 - 14.4; OR 3.0, 95% CI 0.33 - 27.4 respectively) and initial narrowest vessel diameter (OR 1.5, 95% CI 0.81 - 2.8) were not found to have any significant predictive impact on the ability of stents to reach a target diameter of 10 mm. Conclusions: Cook Formula stents are safe in neonates, infants and children. They have a low rate of in-stent stenosis. They are easily expandable to diameters greater than 10 mm irrespective of age, sex and initial stent or vessel diameter.

We present a low-cost resource that aims to facilitate the teaching process of subjects related to optical fibers, given that it presents a practical application of this technology. The main purpose of this resource is to send a color image through optical fibers with a laser diode from one computer and receive it with a photoresistor on another computer, with a total estimated setup-cost of twenty dollars. The setup includes a visual user-interface, for both computers, programmed with the software LabVIEW, which lets to select and prepare an image for a proper sending and receiving. In this proposal we have used arduino boards and low-cost plastic optical fibers used in ornaments.

Purpose: Percutaneous fracture fixation is a challenging procedure that requires accurate interpretation of fluoroscopic images to insert guidewires through narrow bone corridors. We present a guidance system with a video camera mounted onboard the surgical drill to achieve real-time augmentation of the drill trajectory in fluoroscopy and/or CT. Approach: The camera was mounted on the drill and calibrated with respect to the drill axis. Markers identifiable in both video and fluoroscopy are placed about the surgical field and co-registered by feature correspondences. If available, a preoperative CT can also be co-registered by 3D-2D image registration. Real-time guidance is achieved by virtual overlay of the registered drill axis on fluoroscopy or in CT. Performance was evaluated in terms of target registration error (TRE), conformance within clinically relevant pelvic bone corridors, and runtime. Results: Registration of the drill axis to fluoroscopy demonstrated median TRE of 0.9 mm and 2.0 deg when solved with two views (e.g., anteroposterior and lateral) and five markers visible in both video and fluoroscopy-more than sufficient to provide Kirschner wire (K-wire) conformance within common pelvic bone corridors. Registration accuracy was reduced when solved with a single fluoroscopic view ( TRE = 3.4 mm and 2.7 deg) but was also sufficient for K-wire conformance within pelvic bone corridors. Registration was robust with as few as four markers visible within the field of view. Runtime of the initial implementation allowed fluoroscopy overlay and/or 3D CT navigation with freehand manipulation of the drill up to 10 frames / s . Conclusions: A drill-mounted video guidance system was developed to assist with K-wire placement. Overall workflow is compatible with fluoroscopically guided orthopaedic trauma surgery and does not require markers to be placed in preoperative CT. The initial prototype demonstrates accuracy and runtime that could improve the accuracy of K-wire placement, motivating future work for translation to clinical studies.

Purpose: Metal artifacts remain a challenge for CBCT systems in diagnostic imaging and image-guided surgery, obscuring visualization of metal instruments and surrounding anatomy. We present a method to predict C-arm CBCT orbits that will avoid metal artifacts by acquiring projection data that is least affected by polyenergetic bias. Methods: The metal artifact avoidance (MAA) method operates with a minimum of prior information, is compatible with simple mobile C-arms that are increasingly prevalent in routine use, and is consistent with either 3D filtered backprojection (FBP), more advanced (polyenergetic) model-based image reconstruction (MBIR), and/or metal artifact reduction (MAR) post-processing methods. MAA consists of the following steps: (i) coarse localization of metal objects in the field of view (FOV) via two or more low-dose scout views, coarse backprojection, and segmentation (e.g., with a U-Net); (ii) a simple model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices (gantry rotation and tilt angles) accessible by the imaging system; and (iii) definition of a source-detector orbit that minimizes the view-to-view inconsistency in spectral shift. The method was evaluated in anthropomorphic phantom study emulating pedicle screw placement in spine surgery. Results: Phantom studies confirmed that the MAA method could accurately predict tilt angles that minimize metal artifacts. The proposed U-Net segmentation method was able to localize complex distributions of metal instrumentation (over 70% Dice coefficient) with 6 low-dose scout projections acquired during routine pre-scan collision check. CBCT images acquired at MAA-prescribed tilt angles demonstrated ~50% reduction in “blooming” artifacts (measured as FWHM of the screw shaft). Geometric calibration for tilted orbits at prescribed angular increments with interpolation for intermediate values demonstrated accuracy comparable to non-tilted circular trajectories in terms of the modulation transfer function. Conclusion: The preliminary results demonstrate the ability to predict C-arm orbits that provide projection data with minimal spectral bias from metal instrumentation. Such orbits exhibit strongly reduced metal artifacts, and the projection data are compatible with additional post-processing (metal artifact reduction, MAR) methods to further reduce artifacts and/or reduce noise. Ongoing studies aim to improve the robustness of metal object localization from scout views and investigate additional benefits of non-circular C-arm trajectories.

Purpose: Cone-beam CT is increasingly used for 3D guidance in interventional radiology (IR), but long image acquisition time results in degradation from complex deformable motion of soft-tissue structures. Deformable motion compensation with multi-region autofocus optimization was shown to improve image quality. However, the high dimensionality and non-convexity of the optimization problem challenge its convergence. This work presents preliminary development and early results obtained from an automatic learning-based decision framework to obtain local estimates of basic properties of the deformable motion field, coupled to a preconditioning strategy to simplify the optimization. Methods: Deformable motion properties are estimated with a deep convolutional neural network (CNN) consisting of a concatenation of custom-designed residual blocks. The preliminary design provided an estimate of the local motion amplitude on an 8x8 grid covering an axial slice of a motion-contaminated CBCT volume. The decision framework is coupled to a preconditioning strategy that effectively favors more likely solutions through motion amplitude-driven spatially-varying regularization of the motion trajectory and spatially varying selection of the search range for the optimization problem. The network was trained on simulated data generated from publicly available CT datasets, including simple motion fields. Results: Predictions of local motion amplitude showed good agreement with the true values, with root mean squared error (RMSE) < 10 mm for the complete range of motion distributions explored (sufficient for the intended purpose of initialization). Combination of amplitude prediction with spatially varying regularization and search range setting resulted in improved motion compensation after 1000 iterations of the preconditioned multi-motion autofocus in an example case with complex deformable motion. Extensive validation in a large dataset of complex, multi-motion patterns is underway. Conclusion: The proposed approach shows promising initial results and the potential for automatic local motion estimation with learning-based methods. Pending ongoing development to extend this initial development, the method could simplify and accelerate complex deformable motion compensation with spatially varying preconditioning of the motion estimation.

Purpose: Complex, involuntary, non-periodic, deformable motion presents a confounding factor to cone-beam CT (CBCT) image quality due to long (>10 s) scan times. We report and demonstrate an image-based deformable motion compensation method for CBCT, including phantom, cadaver, and animal studies as precursors to clinical studies. Methods: The method corrects deformable motion in CBCT scan data by solving for a motion vector field (MVF) that optimizes a sharpness criterion in the 3D image (viz., gradient entropy). MVFs are estimated by interpolating M locally rigid motion trajectories across N temporal nodes and are incorporated in a modified 3D filtered backprojection approach. The method was evaluated in a cervical spine phantom under flexion, and a cadaver undergoing variable magnitude of complex motion while imaged on a mobile C-arm (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Further assessment was performed on a preclinical animal study using a clinical fixed-room C-arm (Artis Zee, Siemens Healthineers, Forchheim, Germany). Results: In phantom studies, the algorithm resolved visibility of cervical vertebrae under situations of strong flexion, reducing the root-mean-square error by 60% when compared to a motion-free reference. Reduced motion artifacts (blurring, streaks, and loss of soft-tissue edges) were evident in abdominal CBCT of a cadaver imaged during small, medium, and large motion-induced deformation. The animal study demonstrated reduction of streaks from complex motion of bowel gas during the scan. Conclusion: Overall, the studies demonstrate the robustness of the algorithm to a broad range of motion amplitudes, frequencies, data sources (i.e., mobile or fixed-room C-arms) and other confounding factors in real (not simulated) experimental data (e.g., truncation and scatter). These preclinical studies successfully demonstrate reduction of motion artifacts in CBCT and support translation of the method to clinical studies in interventional body radiology.

Mammography and breast CT are important tools for breast cancer screening and diagnosis. Current implementations are limited by scattered radiation and/or spatial resolution. In this work, we propose and develop a slot scan-based system to be used in both mammography and CT mode that can limit scatter and collect sparse CT data for improved image quality at low radiation exposures. Monte Carlo simulations of an anthropomorphic breast phantom show a factor of 10 reduction in scattering amplitude with our slot scan-based system compared to that of a full-field detector mammography system (area mode). Similarly, slot-scan improved the MTF (particularly the low-frequency response) compared to an area detector. Investigation of sparse CT sampling with doubly sparse acquisition data return better quality reconstruction, for which our slot-scanning system is capable, over angle-only projection. Thus, a system with the combined ability for slot-scanning mammography and slot-scanning breast CT has the potential to deliver improved dose-efficient imaging performance and become viable breast cancer screening and diagnostic tools.

Pelvic trauma surgical procedures rely heavily on guidance with 2D fluoroscopy views for navigation in complex bone corridors. This “fluoro-hunting” paradigm results in extended radiation exposure and possible suboptimal guidewire placement from limited visualization of the fractures site with overlapped anatomy in 2D fluoroscopy. A novel computer vision-based navigation system for freehand guidewire insertion is proposed. The navigation framework is compatible with the rapid workflow in trauma surgery and bridges the gap between intraoperative fluoroscopy and preoperative CT images. The system uses a drill-mounted camera to detect and track poses of simple multimodality (optical/radiographic) markers for registration of the drill axis to fluoroscopy and, in turn, to CT. Surgical navigation is achieved with real-time display of the drill axis position on fluoroscopy views and, optionally, in 3D on the preoperative CT. The camera was corrected for lens distortion effects and calibrated for 3D pose estimation. Custom marker jigs were constructed to calibrate the drill axis and tooltip with respect to the camera frame. A testing platform for evaluation of the navigation system was developed, including a robotic arm for precise, repeatable, placement of the drill. Experiments were conducted for hand-eye calibration between the drill-mounted camera and the robot using the Park and Martin solver. Experiments using checkerboard calibration demonstrated subpixel accuracy [−0.01 ± 0.23 px] for camera distortion correction. The drill axis was calibrated using a cylindrical model and demonstrated sub-mm accuracy [0.14 ± 0.70 mm] and sub-degree angular deviation.

Arnold-Chiari malformations are structural defects in the base of the skull and cerebellum, when part of the cerebellar tonsils herniates through the foramen magnum into the upper spinal canal, compressing against the brainstem. This anatomical defect can be asymptomatic but often presents with symptoms such as headaches, stridor, dysphagia, and nystagmus. It also presents with a variety of sleep-related breathing disorders such as snoring, obstructive sleep apnea, central sleep apnea, bradypnea, and sleep hypoventilation. Sometimes these conditions can coexist in one patient. Although obstructive sleep apnea can be a manifestation of Arnold-Chiari malformation, identifying causality and the site of obstruction in these children can be a diagnostic challenge. We review the case of a 14-month-old male who presented with noisy breathing and obstructive sleep apnea diagnosed on sleep study that was refractory to initial upper airway surgery. Although a brain computed tomography scan done in the emergency room for altered mental status revealed a type 1 Arnold-Chiari malformation, and a flexible awake laryngoscopy was normal, subsequent drug-induced sleep endoscopy was helpful in justifying surgical decompression of the Arnold-Chiari malformation that resulted in complete resolution of the obstructive sleep apnea. CITATION: Katwa U, Sisniega C, McKeon M, Lee GS. Sleep endoscopy-directed management of Arnold-Chiari malformation: a child with persistent obstructive sleep apnea. J Clin Sleep Med. 2020;16(2):325–329.

The present study analyses the impact of the different categories of land use and types of soils on cloud-to-ground (CG) lightning activity in the region of Asturias (Spain). Thirteen (fifteen) land uses (types of soils) and a range of fourteen years (2000 to 2013) of CG-lightning flash data were considered to complete the study. Some areas that have suffered the strong impact of human activity (urban, mining, and industrial) were associated with the increase of CG-lightning activity. When considering vegetated areas, areas with non-agricultural vegetation, arable land and permanent crops, it was showed a greater CG activity. With reference to the types of soils, Fluvisols, Regosols/Cambic-Arenosols, and Luvisols, these seemed to be associated to the increase of CG-lightning activity. The results found for the region of Asturias are different from those reported by Mora et al. (2015) for the region of Castilla y Leon (Spain).