Showing papers by "Philips published in 2020"

Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19).

Abstract: Background Chest CT is used to assess the severity of lung involvement in coronavirus disease 2019 (COVID-19). Purpose To determine the changes in chest CT findings associated with COVID-19 from initial diagnosis until patient recovery. Materials and Methods This retrospective review included patients with real-time polymerase chain reaction-confirmed COVID-19 who presented between January 12, 2020, and February 6, 2020. Patients with severe respiratory distress and/or oxygen requirement at any time during the disease course were excluded. Repeat chest CT was performed at approximately 4-day intervals. Each of the five lung lobes was visually scored on a scale of 0 to 5, with 0 indicating no involvement and 5 indicating more than 75% involvement. The total CT score was determined as the sum of lung involvement, ranging from 0 (no involvement) to 25 (maximum involvement). Results Twenty-one patients (six men and 15 women aged 25-63 years) with confirmed COVID-19 were evaluated. A total of 82 chest CT scans were obtained in these patients, with a mean interval (±standard deviation) of 4 days ± 1 (range, 1-8 days). All patients were discharged after a mean hospitalization period of 17 days ± 4 (range, 11-26 days). Maximum lung involved peaked at approximately 10 days (with a calculated total CT score of 6) from the onset of initial symptoms (R2 = 0.25, P < .001). Based on quartiles of chest CT scans from day 0 to day 26 involvement, four stages of lung CT findings were defined. CT scans obtained in stage 1 (0-4 days) showed ground-glass opacities (18 of 24 scans [75%]), with a mean total CT score of 2 ± 2; scans obtained in stage 2 (5-8 days) showed an increase in both the crazy-paving pattern (nine of 17 scans [53%]) and total CT score (mean, 6 ± 4; P = .002); scans obtained in stage 3 (9-13 days) showed consolidation (19 of 21 scans [91%]) and a peak in the total CT score (mean, 7 ± 4); and scans obtained in stage 4 (≥14 days) showed gradual resolution of consolidation (15 of 20 scans [75%]) and a decrease in the total CT score (mean, 6 ± 4) without crazy-paving pattern. Conclusion In patients recovering from coronavirus disease 2019 (without severe respiratory distress during the disease course), lung abnormalities on chest CT scans showed greatest severity approximately 10 days after initial onset of symptoms. © RSNA, 2020.

Pregnancy and Perinatal Outcomes of Women With Coronavirus Disease (COVID-19) Pneumonia: A Preliminary Analysis.

Abstract: OBJECTIVE. The purpose of this study was to describe the clinical manifestations and CT features of coronavirus disease (COVID-19) pneumonia in 15 pregnant women and to provide some initial evidence that can be used for guiding treatment of pregnant women with COVID-19 pneumonia. MATERIALS AND METHODS. We reviewed the clinical data and CT examinations of 15 consecutive pregnant women with COVID-19 pneumonia in our hospital from January 20, 2020, to February 10, 2020. A semiquantitative CT scoring system was used to estimate pulmonary involvement and the time course of changes on chest CT. Symptoms and laboratory results were analyzed, treatment experiences were summarized, and clinical outcomes were tracked. RESULTS. Eleven patients had successful delivery (10 cesarean deliveries and one vaginal delivery) during the study period, and four patients were still pregnant (three in the second trimester and one in the third trimester) at the end of the study period. No cases of neonatal asphyxia, neonatal death, stillbirth, or abortion were reported. The most common early finding on chest CT was ground-glass opacity (GGO). With disease progression, crazy paving pattern and consolidations were seen on CT. The abnormalities showed absorptive changes at the end of the study period for all patients. The most common onset symptoms of COVID-19 pneumonia in pregnant women were fever (13/15 patients) and cough (9/15 patients). The most common abnormal laboratory finding was lymphocytopenia (12/15 patients). CT images obtained before and after delivery showed no signs of pneumonia aggravation after delivery. The four patients who were still pregnant at the end of the study period were not treated with antiviral drugs but had achieved good recovery. CONCLUSION. Pregnancy and childbirth did not aggravate the course of symptoms or CT features of COVID-19 pneumonia. All the cases of COVID-19 pneumonia in the pregnant women in our study were the mild type. All the women in this study-some of whom did not receive antiviral drugs-achieved good recovery from COVID-19 pneumonia.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Abstract: Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.

Free fatty acid binding pocket in the locked structure of SARS-CoV-2 spike protein.

Abstract: COVID-19, caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), represents a global crisis. Key to SARS-CoV-2 therapeutic development is unraveling the mechanisms driving high infectivity, broad tissue tropism and severe pathology. Our 2.85 A cryo-EM structure of SARS-CoV-2 spike (S) glycoprotein reveals that the receptor binding domains (RBDs) tightly bind the essential free fatty acid (FFA) linoleic acid (LA) in three composite binding pockets. The pocket also appears to be present in the highly pathogenic coronaviruses SARS-CoV and MERS-CoV. LA binding stabilizes a locked S conformation giving rise to reduced ACE2 interaction in vitro. In human cells, LA supplementation synergizes with the COVID-19 drug remdesivir, suppressing SARS-CoV-2 replication. Our structure directly links LA and S, setting the stage for intervention strategies targeting LA binding by SARS-CoV-2.

Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs).

Abstract: Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

RGB-T Salient Object Detection via Fusing Multi-Level CNN Features

Abstract: RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast.

The pulmonary sequalae in discharged patients with COVID-19: a short-term observational study.

Abstract: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia were discharged from hospitals in Wuhan, China. We aimed to determine the cumulative percentage of complete radiological resolution at each time point, to explore the relevant affecting factors, and to describe the chest CT findings at different time points after hospital discharge. Patients with COVID-19 pneumonia confirmed by RT-PCR who were discharged consecutively from the hospital between 5 February 2020 and 10 March 2020 and who underwent serial chest CT scans on schedule were enrolled. The radiological characteristics of all patients were collected and analysed. The total CT score was the sum of non-GGO involvement determined at discharge. Afterwards, all patients underwent chest CT scans during the 1st, 2nd, and 3rd weeks after discharge. Imaging features and distributions were analysed across different time points. A total of 149 patients who completed all CT scans were evaluated; there were 67 (45.0%) men and 82 (55.0%) women, with a median age of 43 years old (IQR 36–56). The cumulative percentage of complete radiological resolution was 8.1% (12 patients), 41.6% (62), 50.3% (75), and 53.0% (79) at discharge and during the 1st, 2nd, and 3rd weeks after discharge, respectively. Patients ≤44 years old showed a significantly higher cumulative percentage of complete radiological resolution than patients > 44 years old at the 3-week follow-up. The predominant patterns of abnormalities observed at discharge were ground-glass opacity (GGO) (125 [83.9%]), fibrous stripe (81 [54.4%]), and thickening of the adjacent pleura (33 [22.1%]). The positive count of GGO, fibrous stripe and thickening of the adjacent pleura gradually decreased, while GGO and fibrous stripe showed obvious resolution during the first week and the third week after discharge, respectively. “Tinted” sign and bronchovascular bundle distortion as two special features were discovered during the evolution. Lung lesions in COVID-19 pneumonia patients can be absorbed completely during short-term follow-up with no sequelae. Two weeks after discharge might be the optimal time point for early radiological estimation.

Mental Health Care Goes Online: Practitioners' Experiences of Providing Mental Health Care During the COVID-19 Pandemic.

Abstract: The outbreak of the COVID-19 pandemic has necessitated sudden and radical changes in mental health care delivery, as strict social distancing and lockdown measures were imposed in the early phases of the pandemic. Almost overnight, practitioners were forced to transfer their face-to-face care practice to online means. To understand the implications of this drastic change for mental health care, and to improve the online care offerings, an online qualitative survey was held among mental health care professionals in Netherlands (n = 51). Our findings indicate that technological and usability problems pose a significant challenge, as do difficulties to establish rapport with clients. Moreover, not all mental health issues and treatment forms are equally amenable to online interaction. In contrast, in many instances, practitioners were positive about the effectiveness of treatment, and reported flexibility, a lower threshold for contact, and lack of travel time as advantages. Their most prominent needs concern better technological, organizational, and logistical support. It is critical that these needs are acted upon by institutions and governments. In addition, current results inform future research on the improvement of e-mental health technologies.

Principles and applications of multienergy CT: Report of AAPM Task Group 291.

Abstract: In x-ray computed tomography (CT), materials with different elemental compositions can have identical CT number values, depending on the mass density of each material and the energy of the detected x-ray beam. Differentiating and classifying different tissue types and contrast agents can thus be extremely challenging. In multienergy CT, one or more additional attenuation measurements are obtained at a second, third or more energy. This allows the differentiation of at least two materials. Commercial dual-energy CT systems (only two energy measurements) are now available either using sequential acquisitions of low- and high-tube potential scans, fast tube-potential switching, beam filtration combined with spiral scanning, dual-source, or dual-layer detector approaches. The use of energy-resolving, photon-counting detectors is now being evaluated on research systems. Irrespective of the technological approach to data acquisition, all commercial multienergy CT systems circa 2020 provide dual-energy data. Material decomposition algorithms are then used to identify specific materials according to their effective atomic number and/or to quantitate mass density. These algorithms are applied to either projection or image data. Since 2006, a number of clinical applications have been developed for commercial release, including those that automatically (a) remove the calcium signal from bony anatomy and/or calcified plaque; (b) create iodine concentration maps from contrast-enhanced CT data and/or quantify absolute iodine concentration; (c) create virtual non-contrast-enhanced images from contrast-enhanced scans; (d) identify perfused blood volume in lung parenchyma or the myocardium; and (e) characterize materials according to their elemental compositions, which can allow in vivo differentiation between uric acid and non-uric acid urinary stones or uric acid (gout) or non-uric acid (calcium pyrophosphate) deposits in articulating joints and surrounding tissues. In this report, the underlying physical principles of multienergy CT are reviewed and each of the current technical approaches are described. In addition, current and evolving clinical applications are introduced. Finally, the impact of multienergy CT technology on patient radiation dose is summarized.

PennPET Explorer: Design and Preliminary Performance of a Whole-Body Imager

Abstract: We report on the development of the PennPET Explorer whole-body imager. Methods: The PennPET Explorer is a multiring system designed with a long axial field of view. The imager is scalable and comprises multiple 22.9-cm-long ring segments, each with 18 detector modules based on a commercial digital silicon photomultiplier. A prototype 3-segment imager has been completed and tested with an active 64-cm axial field of view. Results: The instrument design is described, and its physical performance measurements are presented. These include sensitivity of 55 kcps/MBq, spatial resolution of 4.0 mm, energy resolution of 12%, timing resolution of 256 ps, and a noise-equivalent count rate above 1,000 kcps beyond 30 kBq/mL. After an evaluation of lesion torso phantoms to characterize quantitative accuracy, human studies were performed on healthy volunteers. Conclusion: The physical performance measurements validated the system design and led to high-quality human studies.

Adaptive Ultrasound Beamforming Using Deep Learning

Abstract: Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks, that adopt the algorithmic structure and constraints of adaptive signal processing techniques, can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance.

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.

Abstract: Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain. Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.

Timely Diagnosis and Treatment Shortens the Time to Resolution of Coronavirus Disease (COVID-19) Pneumonia and Lowers the Highest and Last CT Scores From Sequential Chest CT.

Abstract: OBJECTIVE. This study aims to assess correlations of the time from symptom onset to diagnosis and treatment with the time to disease resolution and CT scores as based on findings from sequential chest CT examinations. MATERIALS AND METHODS. Thirty patients with coronavirus disease (COVID-19) confirmed by reverse transcription-polymerase chain reaction analysis underwent chest CT examinations. Five patients who did not have positive CT findings or who had not yet fulfilled criteria for discharge from the hospital were excluded. CT scores were determined according to CT findings and lung involvement. The time from symptom onset to diagnosis and treatment was recorded for each patient, and on the basis of this information, patients with COVID-19 were divided into group 1 (patients for whom this interval was ≤ 3 days) and group 2 (those for whom this interval was > 3 days). The CT scores for each group were fitted using a Lorentzian line-shape curve to show the variation tendency during treatment. The differences in age, sex, and last CT scores determined before discharge between the two groups were analyzed, and correlations of the time from symptom onset to diagnosis and treatment with the time to disease resolution as well as with the highest CT score also underwent statistical analysis. RESULTS. A total of 25 subjects were enrolled in the study. The fitted tendency curves for group 1 and group 2 were significantly different, with peak points showing that the estimated highest CT score was 10 and 16 for each group, respectively, and the time to disease resolution was 6 and 13 days, respectively. The Mann-Whitney test showed that the last CT scores were lower for group 1 than for group 2 (p = 0.025), although the chi-square test found no difference in age and sex between the groups. The time from symptom onset to diagnosis and treatment had a positive correlation with the time to disease resolution (r = 0.93; p = 0.000) as well as with the highest CT score (r = 0.83; p = 0.006). CONCLUSION. Timely diagnosis and treatment are key to providing a better prognosis for patients with COVID-19.

Imaging Methods for Ultrasound Contrast Agents.

Abstract: Microbubble contrast agents were introduced more than 25 years ago with the objective of enhancing blood echoes and enabling diagnostic ultrasound to image the microcirculation. Cardiology and oncology waited anxiously for the fulfillment of that objective with one clinical application each: myocardial perfusion, tumor perfusion and angiogenesis imaging. What was necessary though at first was the scientific understanding of microbubble behavior in vivo and the development of imaging technology to deliver the original objective. And indeed, for more than 25 years bubble science and imaging technology have evolved methodically to deliver contrast-enhanced ultrasound. Realization of the basic bubbles properties, non-linear response and ultrasound-induced destruction, has led to a plethora of methods; algorithms and techniques for contrast-enhanced ultrasound (CEUS) and imaging modes such as harmonic imaging, harmonic power Doppler, pulse inversion, amplitude modulation, maximum intensity projection and many others were invented, developed and validated. Today, CEUS is used everywhere in the world with clinical indications both in cardiology and in radiology, and it continues to mature and evolve and has become a basic clinical tool that transforms diagnostic ultrasound into a functional imaging modality. In this review article, we present and explain in detail bubble imaging methods and associated artifacts, perfusion quantification approaches, and implementation considerations and regulatory aspects.

Spontaneous Pneumomediastinum: A Probable Unusual Complication of Coronavirus Disease 2019 (COVID-19) Pneumonia.

Abstract: Copyright © 2020 The Korean Society of Radiology Spontaneous Pneumomediastinum: A Probable Unusual Complication of Coronavirus Disease 2019 (COVID-19) Pneumonia Jing Wang, MD, PhD *, Xiaoyun Su, MD *, Tianjing Zhang, MS, Chuansheng Zheng, MD, PhD 2 Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China; Philips Healthcare, Guangzhou, China

Automated Lung Ultrasound B-Line Assessment Using a Deep Learning Algorithm

Abstract: Shortness of breath is a major reason that patients present to the emergency department (ED) and point-of-care ultrasound (POCUS) has been shown to aid in diagnosis, particularly through evaluation for artifacts known as B-lines. B-line identification and quantification can be a challenging skill for novice ultrasound users, and experienced users could benefit from a more objective measure of quantification. We sought to develop and test a deep learning (DL) algorithm to quantify the assessment of B-lines in lung ultrasound. We utilized ultrasound clips ( ${n} =400$ ) from an existing database of ED patients to provide training and test sets to develop and test the DL algorithm based on deep convolutional neural networks. Interpretations of the images by algorithm were compared to expert human interpretations on binary and severity (a scale of 0–4) classifications. Our model yielded a sensitivity of 93% (95% confidence interval (CI) 81%–98%) and a specificity of 96% (95% CI 84%–99%) for the presence or absence of B-lines compared to expert read, with a kappa of 0.88 (95% CI 0.79–0.97). Model to expert agreement for severity classification yielded a weighted kappa of 0.65 (95% CI 0.56–074). Overall, the DL algorithm performed well and could be integrated into an ultrasound system in order to help diagnose and track B-line severity. The algorithm is better at distinguishing the presence from the absence of B-lines but can also be successfully used to distinguish between B-line severity. Such methods could decrease variability and provide a standardized method for improved diagnosis and outcome.

Deep Salient Object Detection With Contextual Information Guidance

Abstract: Integration of multi-level contextual information, such as feature maps and side outputs, is crucial for Convolutional Neural Networks (CNNs)-based salient object detection. However, most existing methods either simply concatenate multi-level feature maps or calculate element-wise addition of multi-level side outputs, thus failing to take full advantages of them. In this paper, we propose a new strategy for guiding multi-level contextual information integration, where feature maps and side outputs across layers are fully engaged. Specifically, shallower-level feature maps are guided by the deeper-level side outputs to learn more accurate properties of the salient object. In turn, the deeper-level side outputs can be propagated to high-resolution versions with spatial details complemented by means of shallower-level feature maps. Moreover, a group convolution module is proposed with the aim to achieve high-discriminative feature maps, in which the backbone feature maps are divided into a number of groups and then the convolution is applied to the channels of backbone feature maps within each group. Eventually, the group convolution module is incorporated in the guidance module to further promote the guidance role. Experiments on three public benchmark datasets verify the effectiveness and superiority of the proposed method over the state-of-the-art methods.

Augmented reality navigation with intraoperative 3D imaging vs fluoroscopy-assisted free-hand surgery for spine fixation surgery: a matched-control study comparing accuracy.

Abstract: This study aimed to compare screw placement accuracy and clinical aspects between Augmented Reality Surgical Navigation (ARSN) and free-hand (FH) technique. Twenty patients underwent spine surgery with screw placement using ARSN and were matched retrospectively to a cohort of 20 FH technique cases for comparison. All ARSN and FH cases were performed by the same surgeon. Matching was based on clinical diagnosis and similar proportions of screws placed in the thoracic and lumbosacral vertebrae in both groups. Accuracy of screw placement was assessed on postoperative scans according to the Gertzbein scale and grades 0 and 1 were considered accurate. Procedure time, blood loss and length of hospital stay, were collected as secondary endpoints. A total of 262 and 288 screws were assessed in the ARSN and FH groups, respectively. The share of clinically accurate screws was significantly higher in the ARSN vs FH group (93.9% vs 89.6%, p < 0.05). The proportion of screws placed without a cortical breach was twice as high in the ARSN group compared to the FH group (63.4% vs 30.6%, p < 0.0001). No statistical difference was observed for the secondary endpoints between both groups. This matched-control study demonstrated that ARSN provided higher screw placement accuracy compared to free-hand.

Home-based monitoring of falls using wearable sensors in Parkinson's disease.

Abstract: INTRODUCTION: Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life. METHODS: We matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling. RESULTS: The incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0). CONCLUSION: Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. (c) 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The need for change: Understanding emotion regulation antecedents and consequences using ecological momentary assessment.

Abstract: In the last decades, Emotion Regulation (ER) represented one of the most studied constructs within the psychological field. Most research, however, has been conducted in laboratory settings; consequently, there are still questions that need to be addressed concerning the deployment and consequences of ER in everyday life. Beyond traditional methods, ecological momentary assessment (EMA) via mobile devices (e.g. smartphones) has the potential to capture ER dynamics during the flow of daily experiences and in real-life settings. Compared to retrospective surveys and laboratory experiments, this approach allows to ecologically and repeatedly investigate the deployment of ER, as well as understand the direct consequences of this process on different aspects of daily life, including behaviors and affect. We will discuss what we currently know about the deployment and consequences of ER in real-life settings focusing on studies that investigated this process by means of EMA. In doing so, we will point out the potentialities of this approach both from a theoretical and clinical point of view.

Chip-based measurement-device-independent quantum key distribution

Abstract: Modern communication strives towards provably secure systems which can be widely deployed. Quantum key distribution provides a methodology to verify the integrity and security of a key exchange based on physical laws. However, physical systems often fall short of theoretical models, meaning they can be compromised through uncharacterized side-channels. The complexity of detection means that the measurement system is a vulnerable target for an adversary. Here, we present secure key exchange up to 200 km while removing all side-channels from the measurement system. We use mass-manufacturable, monolithically integrated transmitters that represent an accessible, quantum-ready communication platform. This work demonstrates a network topology that allows secure equipment sharing which is accessible with a cost-effective transmitter, significantly reducing the barrier for widespread uptake of quantum-secured communication.

The Relationship Between Axon Density, Myelination, and Fractional Anisotropy in the Human Corpus Callosum.

Abstract: The corpus callosum serves the functional integration and interaction between the two hemispheres. Many studies investigate callosal microstructure via diffusion tensor imaging (DTI) fractional anisotropy (FA) in geometrically parcellated segments. However, FA is influenced by several different microstructural properties such as myelination and axon density, hindering a neurobiological interpretation. This study explores the relationship between FA and more specific measures of microstructure within the corpus callosum in a sample of 271 healthy participants. DTI tractography was used to assess 11 callosal segments and gain estimates of FA. We quantified axon density and myelination via neurite orientation dispersion and density imaging (NODDI) to assess intra-neurite volume fraction and a multiecho gradient spin-echo sequence estimating myelin water fraction. The results indicate three common factors in the distribution of FA, myelin content and axon density, indicating potentially shared rules of topographical distribution. Moreover, the relationship between measures varied across the corpus callosum, suggesting that FA should not be interpreted uniformly. More specific magnetic resonance imaging-based quantification techniques, such as NODDI and multiecho myelin water imaging, may thus play a key role in future studies of clinical trials and individual differences.

A Novel Augmented-Reality-Based Surgical Navigation System for Spine Surgery in a Hybrid Operating Room: Design, Workflow, and Clinical Applications

Abstract: Background Treatment of several spine disorders requires placement of pedicle screws. Detailed 3-dimensional (3D) anatomic information facilitates this process and improves accuracy. Objective To present a workflow for a novel augmented-reality-based surgical navigation (ARSN) system installed in a hybrid operating room for anatomy visualization and instrument guidance during pedicle screw placement. Methods The workflow includes surgical exposure, imaging, automatic creation of a 3D model, and pedicle screw path planning for instrument guidance during surgery as well as the actual screw placement, spinal fixation, and wound closure and intraoperative verification of the treatment results. Special focus was given to process integration and minimization of overhead time. Efforts were made to manage staff radiation exposure avoiding the need for lead aprons. Time was kept throughout the procedure and subdivided to reflect key steps. The navigation workflow was validated in a trial with 20 cases requiring pedicle screw placement (13/20 scoliosis). Results Navigated interventions were performed with a median total time of 379 min per procedure (range 232-548 min for 4-24 implanted pedicle screws).The total procedure time was subdivided into surgical exposure (28%), cone beam computed tomography imaging and 3D segmentation (2%), software planning (6%), navigated surgery for screw placement (17%) and non-navigated instrumentation, wound closure, etc (47%). Conclusion Intraoperative imaging and preparation for surgical navigation totaled 8% of the surgical time. Consequently, ARSN can routinely be used to perform highly accurate surgery potentially decreasing the risk for complications and revision surgery while minimizing radiation exposure to the staff.

Sharing Is Caring—Data Sharing Initiatives in Healthcare

Abstract: In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these ‘big data’ put together can be utilized to optimize treatments for each unique patient (‘precision medicine’). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the ‘valley of death’ of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

Comparison of feature tracking, fast-SENC, and myocardial tagging for global and segmental left ventricular strain.

Abstract: Aims A multitude of cardiac magnetic resonance (CMR) techniques are used for myocardial strain assessment; however, studies comparing them are limited. We sought to compare global longitudinal (GLS), circumferential (GCS), segmental longitudinal (SLS), and segmental circumferential (SCS) strain values, as well as reproducibility between CMR feature tracking (FT), tagging (TAG), and fast-strain-encoded (fast-SENC) CMR techniques. Methods and results Eighteen subjects (11 healthy volunteers and seven patients with heart failure) underwent two CMR scans (1.5T, Philips) with identical parameters. Global and segmental strain values were measured using FT (Medis), TAG (Medviso), and fast-SENC (Myocardial Solutions). Friedman's test, linear regression, Pearson's correlation coefficient, and Bland-Altman analyses were used to assess differences and correlation in measured GLS and GCS between the techniques. Two-way mixed intra-class correlation coefficient (ICC), coefficient of variance (COV), and Bland-Altman analysis were used for reproducibility assessment. All techniques correlated closely for GLS (Pearson's r: 0.86-0.92) and GCS (Pearson's r: 0.85-0.94). Intra-observer and inter-observer reproducibility was excellent in all techniques for both GLS (ICC 0.92-0.99, CoV 2.6-10.1%) and GCS (ICC 0.89-0.99, CoV 4.3-10.1%). Inter-study reproducibility was similar for all techniques for GLS (ICC 0.91-0.96, CoV 9.1-10.8%) and GCS (ICC 0.95-0.97, CoV 7.6-10.4%). Combined segmental intra-observer reproducibility was good in all techniques for SLS (ICC 0.914-0.953, CoV 12.35-24.73%) and SCS (ICC 0.885-0.978, CoV 10.76-19.66%). Combined inter-study SLS reproducibility was the worst in FT (ICC 0.329, CoV 42.99%), while fast-SENC performed the best (ICC 0.844, CoV 21.92%). TAG had the best reproducibility for combined inter-study SCS (ICC 0.902, CoV 19.08%), while FT performed the worst (ICC 0.766, CoV 32.35%). Bland-Altman analysis revealed considerable inter-technique biases for GLS (FT vs. fast-SENC 3.71%; FT vs. TAG 8.35%; and TAG vs. fast-SENC 4.54%) and GCS (FT vs. fast-SENC 2.15%; FT vs. TAG 6.92%; and TAG vs. fast-SENC 2.15%). Limits of agreement for GLS ranged from ±3.1 (TAG vs. fast-SENC) to ±4.85 (FT vs. TAG) for GLS and ±2.98 (TAG vs. fast-SENC) to ±5.85 (FT vs. TAG) for GCS. Conclusions We found significant differences in measured GLS and GCS between FT, TAG, and fast-SENC. Global strain reproducibility was excellent for all techniques. Acquisition-based techniques had better reproducibility than FT for segmental strain.

Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance.

Abstract: Background Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability. Aim In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance. Results A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well-trained end-users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved. Conclusion We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.

Augmented Reality Surgical Navigation in Spine Surgery to Minimize Staff Radiation Exposure.

Abstract: Study design Prospective observational study. Objective To assess staff and patient radiation exposure during augmented reality surgical navigation in spine surgery. Summary of background data Surgical navigation in combination with intraoperative three-dimensional imaging has been shown to significantly increase the clinical accuracy of pedicle screw placement. Although this technique may increase the total radiation exposure compared with fluoroscopy, the occupational exposure can be minimized, as navigation is radiation free and staff can be positioned behind protective shielding during three-dimensional imaging. The patient radiation exposure during treatment and verification of pedicle screw positions can also be reduced. Methods Twenty patients undergoing spine surgery with pedicle screw placement were included in the study. The staff radiation exposure was measured using real-time active personnel dosimeters and was further compared with measurements using a reference dosimeter attached to the C-arm (i.e., a worst-case staff exposure situation). The patient radiation exposures were recorded, and effective doses (ED) were determined. Results The average staff exposure per procedure was 0.21 ± 0.06 μSv. The average staff-to-reference dose ratio per procedure was 0.05% and decreased to less than 0.01% after a few procedures had been performed. The average patient ED was 15.8 ± 1.8 mSv which mainly correlated with the number of vertebrae treated and the number of cone-beam computed tomography acquisitions performed. A low-dose protocol used for the final 10 procedures yielded a 32% ED reduction per spinal level treated. Conclusion This study demonstrated significantly lower occupational doses compared with values reported in the literature. Real-time active personnel dosimeters contributed to a fast optimization and adoption of protective measures throughout the study. Even though our data include both cone-beam computed tomography for navigation planning and intraoperative screw placement verification, we find low patient radiation exposure levels compared with published data. Level of evidence 3.

Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.

Abstract: BACKGROUND: Quantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation. PURPOSE: To evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis. STUDY TYPE: Retrospective. POPULATION: In all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test). FIELD STRENGTH/SEQUENCE: 3.0T/2D multislice saturation recovery T 1 -weighted gradient echo sequence. ASSESSMENT: Accuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL-based processing were compared to the results obtained with the manually processed images. STATISTICAL TESTS: Bland-Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator. RESULTS: The mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland-Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per-myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90]. DATA CONCLUSION: We showed high accuracy, compared to manual processing, for the DL-based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1689-1696.

An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction

Abstract: Adaptive intelligence aims at empowering machine learning techniques with the additional use of domain knowledge. In this work, we present the application of adaptive intelligence to accelerate MR acquisition. Starting from undersampled k-space data, an iterative learning-based reconstruction scheme inspired by compressed sensing theory is used to reconstruct the images. We developed a novel deep neural network to refine and correct prior reconstruction assumptions given the training data. The network was trained and tested on a knee MRI dataset from the 2019 fastMRI challenge organized by Facebook AI Research and NYU Langone Health. All submissions to the challenge were initially ranked based on similarity with a known groundtruth, after which the top 4 submissions were evaluated radiologically. Our method was evaluated by the fastMRI organizers on an independent challenge dataset. It ranked #1, shared #1, and #3 on respectively the $8\times $ accelerated multi-coil, the $4\times $ multi-coil, and the $4\times $ single-coil tracks. This demonstrates the superior performance and wide applicability of the method.