CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment.

Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of 84.37 % .

Evaluating a Telemedicine Video Game-Linked High-Intensity Interval Training Exercise Programme in Paediatric Heart Transplant Recipients.

Paediatric heart transplant recipients (HTRs) have reduced exercise capacity, physical activity (PA), health-related quality of life (HRQoL), and self-efficacy towards PA. Exercise interventions have demonstrated improvements in exercise capacity and functional status in adult HTRs, with a specific emerging interest in the role of high-intensity interval training (HIIT). Studies of exercise interventions in paediatric HTRs have been limited and nonrandomized to date. HIIT has not yet been evaluated in paediatric HTRs. We thus seek to evaluate the safety and feasibility of a randomized crossover trial of a 12-week, home-based, video game-linked HIIT intervention using a cycle ergometer with telemedicine and remote physiological monitoring capabilities (MedBIKE) in paediatric HTRs. The secondary objective is to evaluate the impact of the intervention on (1) exercise capacity, (2) PA, (3) HRQoL and self-efficacy towards PA, and (4) sustained changes in secondary outcomes at 6 and 12 months after intervention. After a baseline assessment of the secondary outcomes, participants will be randomized to receive the MedBIKE intervention (12 weeks, 36 sessions) or usual care. After the intervention and a repeated assessment, all participants will cross over. Follow-up assessments will be administered at 6 and 12 months after the MedBIKE intervention. We anticipate that the MedBIKE intervention will be feasible and safely yield sustained improvements in exercise capacity, PA, HRQoL, and self-efficacy towards PA in paediatric HTRs. This study will serve as the foundation for a larger, multicentre randomized crossover trial and will help inform exercise rehabilitation programmes for paediatric HTRs.

La tolérance à l'effort, le niveau d'activité physique (AP), le score de la qualité de vie liée à la santé (QVLS) ainsi que l'auto-efficacité à la pratique d'une AP se trouvent diminués chez les patients pédiatriques ayant reçu une transplantation cardiaque. Il a été montré que les exercices physiques permettent d'améliorer la tolérance à l'effort ainsi que le statut fonctionnel chez les patients adultes ayant reçu une transplantation cardiaque. D'ailleurs, le rôle de l'entraînement par intervalles de haute intensité (EIHI) suscite depuis peu un nouvel intérêt à cet égard. Les études réalisées à ce jour sur les programmes d'activité physique chez les patients pédiatriques ayant reçu une transplantation cardiaque sont toutefois peu nombreuses et ne reposent pas sur une répartition aléatoire. De plus, l'EIHI n'a pas encore été évalué chez ce groupe de patients. La présente étude a donc pour objectif d'évaluer la faisabilité et l'innocuité d'un essai clinique croisé à répartition aléatoire d'une durée de 12 semaines chez des patients pédiatriques ayant reçu une transplantation cardiaque. Le programme d'activité physique prendra la forme d'un EIHI à la maison au moyen d'un jeu vidéo et d'une bicyclette ergométrique permettant une assistance et une surveillance des données physiologiques à distance (MedBIKE). Les objectifs secondaires de l'étude consistent à évaluer les effets du programme sur : 1) la tolérance à l'effort; 2) le niveau d'AP; 3) la QVLS ainsi que l'auto-efficacité à la pratique d'une AP; et 4) le maintien des améliorations relatives aux critères d'évaluation se-condaires à 6 et 12 mois. Après une évaluation initiale des critères d'évaluation secondaires, les participants seront répartis aléatoirement dans le groupe suivant le programme à l'aide du vélo MedBIKE (36 séances réparties sur 12 semaines) ou dans le groupe recevant le traitement usuel. Tous les participants changeront ensuite de groupe, et une nouvelle évaluation des critères d'évaluation se-condaires sera effectuée. Les évaluations de suivi auront lieu 6 et 12 mois après la fin du programme. On s'attend à ce que ce dernier soit sûr, facile à suivre et accompagné d'améliorations soutenues de la tolérance à l'effort, du niveau d'AP, de la QVLS et de l'auto-efficacité à la pratique d'une AP chez les patients pédiatriques ayant reçu une transplantation cardiaque. Cette étude servira de modèle à un essai clinique croisé, multicentrique, à répartition aléatoire de plus grande envergure. Elle permettra aussi de générer des renseignements utiles pour les programmes de réadaptation destinés aux patients pédiatriques ayant reçu une transplantation cardiaque.

Exploring the Promise of Telemedicine Exercise Interventions in Children and Adolescents With Congenital Heart Disease.

Youth with congenital heart disease (CHD) have reduced exercise capacity via various physical and psychosocial mechanisms. In addition to limited physiologic exercise capacity, these patients experience lower levels of physical activity, physical activity self-efficacy, health-related quality of life, and endothelial function. The study of exercise interventions and cardiac rehabilitation programs in pediatric CHD populations remains limited, particularly home-based interventions that incorporate real-time physiologic monitoring. Home-based interventions provide improved access and convenience to patients. This is principally important for patients from geographically disperse regions who receive their care at centralized subspecialty centres, as is the case for Canadian pediatric cardiac care. These programs, however, have traditionally not permitted the supervision of safety, technique, and adherence that are afforded by hospital/facility-based programs. As such, telemedicine is an important evolving area that combines the benefits of traditional home and facility-based cardiac rehabilitation. An additional key area lacking study surrounds the types of exercise interventions in youth with CHD. To date, interventions have often centred around moderate-intensity continuous exercise. High-intensity interval training might offer superior cardiorespiratory advantages but remains understudied in the CHD population. In this review, we highlight the existing evidence basis for exercise interventions in youth with CHD, explore the promise of incorporating telemedicine home-based solutions, and highlight key knowledge gaps. To address identified knowledge gaps, we are undertaking a 12-week randomized crossover trial of a home-based telemedicine high-intensity interval training intervention in youth with repaired moderate-severe CHD using a video game-linked cycle ergometer (known as the MedBIKE; https://spaces.facsci.ualberta.ca/ahci/projects/medical-projects/remote-rehab-bike-projects).

Deep Learning using Pre-Brachytherapy MRI to Automatically Predict Applicator Induced Complex Uterine Deformation.

This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy by the presence of an intrauterine (tandem)/intravaginal (ring) applicator during brachytherapy (BT) treatment for locally advanced cervical cancer. Paired pelvic MRI datasets from 92 subjects, acquired without (pre-BT) and with (at-BT) applicators, were used. We propose a novel automated algorithm to segment the uterus in pre-BT MR images using a deep convolutional neural network (CNN) incorporated with autoencoders. The proposed neural net is based on a pre-trained CNN Inception V4 architecture. It predicts a compressed vector by applying a multi-layer autoencoder, which is then back-projected into the segmentation contour of the uterus. Following this, another transfer learning approach using a modified U-net model is employed to predict the at-BT uterus shape from pre-BT MRI. The complex and large deformations of the uterus are quantified using free form deformation method. The proposed algorithm yielded an average Dice Coefficient (DC) of 94.1±3.3 and an average Hausdorff Distance (HD) of 4.0±3.1 mm compared to the manually defined ground truth by expert clinicians. Further, the modified U-net prediction of the at-BT uterus resulted in a DC accuracy of 88.1±3.8 and HD of 5.8±3.6 mm. The mean uterine surface point-to-point displacement was 25.0 [10.0-62.5] mm from the pre-BT position. Our unique DL method can thus successfully predict tandem-deformed uterine shape and position from MR images taken before the BT implant procedure i.e. without the applicator in place. Clinical relevance-The proposed DL-based framework can be incorporated as an automatic prediction tool of uterine deformation due to applicator insertion for personalized BT treatments. It holds promise for more streamlined clinical/technical decision-making before BT applicator insertion resulting in improved dosimetric outcomes.

Automatic Bone Segmentation from MRI for Real-Time Knee Tracking in Fluoroscopic Imaging.

Recent progress in real-time tracking of knee bone structures from fluoroscopic imaging using CT templates has opened the door to studying knee kinematics to improve our understanding of patellofemoral syndrome. The problem with CT imaging is that it exposes patients to extra ionising radiation, which adds to fluoroscopic imaging. This can be solved by segmenting bone templates from MRI instead of CT by using a deep neural network architecture called 2.5D U-Net. To train the network, we used the SKI10 database from the MICCAI challenge; it contains 100 knee MRIs with their corresponding annotated femur and tibia bones as the ground truth. Since patella tracking is essential in our application, the SKI10 database was augmented with a new label named UofA Patella. Using 70 MRIs from the database, a 2.5D U-Net was trained successfully after 75 epochs with an excellent final Dice score of 98%, which compared favourably with the best state-of-the-art algorithms. A test set of 30 MRIs were segmented using the trained 2.5D U-Net and then converted into 3D mesh templates by using a marching cube algorithm. The resulting 3D mesh templates were compared to the 3D mesh model extracted from the corresponding labelled data from the augmented SKI10. Even though the final Dice score (98%) compared well with the state-of-the-art algorithms, we initially found that the Euclidean distance between the segmented MRI and SKI10 meshes was over 6 mm in many regions, which is unacceptable for our application. By optimising many of the hyper-parameters of the 2.5D U-Net, we were able to find that, by changing the threshold used in the last layer of the network, one can significantly improve the average accuracy to 0.2 mm with a variance of 0.065 mm for most of the MRI mesh templates. These results illustrate that the Dice score is not always a good predictor of the geometric accuracy of segmentation and that fine-tuning hyper-parameters is critical for improving geometric accuracy.

Telemonitoring and protocolized case management for hypertensive community dwelling older adults (TECHNOMED): a randomized controlled trial.

BACKGROUND: Home blood pressure (BP) telemonitoring combined with case management leads to BP reductions in individuals with hypertension. However, its benefits are less clear in older (age ≥ 65 years) adults. METHODS: Twelve-month, open-label, randomized trial of community-dwelling older adults comparing the combination of home BP telemonitoring (HBPM) and pharmacist-led case management, vs. enhanced usual care with HBPM alone. The primary outcome was the proportion achieving systolic BP targets on 24-h ambulatory BP monitoring (ABPM). Changes in HBPM were also examined. Logistic and linear regressions were used for analyses, adjusted for baseline BP. RESULTS: Enrollment was stopped early due to coronavirus disease 2019. Participants randomized to intervention (n = 61) and control (n = 59) groups were mostly female (77%), with mean age 79.5 years. The adjusted odds ratio for ABPM BP target achievement was 1.48 (95% confidence interval 0.87-2.52, P = 0.15). At 12 months, the mean difference in BP changes between intervention and control groups was -1.6/-1.1 for ABPM (P-value 0.26 for systolic BP and 0.10 for diastolic BP), and -4.9/-3.1 for HBPM (P-value 0.04 for systolic BP and 0.01 for diastolic BP), favoring the intervention. Intervention group participants had hypotension (systolic BP < 110) more frequently (21% vs. 5%, P = 0.009), but no differences in orthostatic symptoms, syncope, non-mechanical falls, or emergency department visits. CONCLUSIONS: Home BP telemonitoring and pharmacist case management did not improve achievement of target range ambulatory BP, but did reduce home BP. It did not result in major adverse consequences.

Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.

Cardiovascular diseases are the leading cause of death globally, causing nearly 17.9 million deaths per year. Therefore, early detection and treatment are critical to help improve this situation. Many manufacturers have developed products to monitor patients' heart conditions as they perform their daily activities. However, very few can diagnose complex heart anomalies beyond detecting rhythm fluctuation. This paper proposes a new method that combines a Short-Time Fourier Transform (STFT) spectrogram of the ECG signal with handcrafted features to detect heart anomalies beyond commercial product capabilities. Using the proposed Convolutional Neural Network, the algorithm can detect 16 different rhythm anomalies with an accuracy of 99.79% with 0.15% false-alarm rate and 99.74% sensitivity. Additionally, the same algorithm can also detect 13 heartbeat anomalies with 99.18% accuracy with 0.45% false-alarm rate and 98.80% sensitivity.

Do agri-food market incentives improve food security and nutrition indicators? a microsimulation evaluation for Kenya.

The sustainable development goal #2 aims at ending hunger and malnutrition by 2030. Given the numbers of food insecure and malnourished people on the rise, the heterogeneity of nutritional statuses and needs, and the even worse context of COVID-19 pandemic, this has become an urgent challenge for food-related policies. This paper provides a comprehensive microsimulation approach to evaluate economic policies on food access, sufficiency (energy) and adequacy (protein, fat, carbohydrate) at household level. The improvement in market access conditions in Kenya is simulated as an application case of this method, using original insights from households' surveys and biochemical and nutritional information by food item. Simulation's results suggest that improving market access increases food purchasing power overall the country, with a pro-poor impact in rural areas. The daily energy consumption per capita and macronutrients intakes per capita increase at the national level, being the households with at least one stunted child under 5 years old, and poor households living areas outside Mombasa and Nairobi, those which benefit the most. The developed method and its Kenya's application contribute to the discussion on how to evaluate nutrition-sensitive policies, and how to cover most households suffering food insecurity and nutrition deficiencies in any given country. Supplementary Information: The online version contains supplementary material available at 10.1007/s12571-021-01215-2.

A New Semi-automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences.

PURPOSE: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio. METHODS: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no dependence on prior geometrical information, training data, or registration from an atlas. RESULTS: The method was evaluated using three-dimensional ultrasound scan sequences from 18 patients from the Mazankowski Alberta Heart Institute, Edmonton, Canada, and compared to manual delineations provided by an expert cardiologist and four other registration algorithms. The segmentation approach yielded the following results over the cardiac cycle: a mean absolute difference of 1.01 (0.21) mm, a Hausdorff distance of 4.41 (1.43) mm, and a Dice overlap score of 0.93 (0.02). CONCLUSION: The method performed well compared to the four other registration algorithms.

An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.

Today's wearable medical devices are becoming popular because of their price and ease of use. Most wearable medical devices allow users to continuously collect and check their health data, such as electrocardiograms (ECG). Therefore, many of these devices have been used to monitor patients with potential heart pathology as they perform their daily activities. However, one major challenge of collecting heart data using mobile ECG is baseline wander and motion artifacts created by the patient's daily activities, resulting in false diagnoses. This paper proposes a new algorithm that automatically removes the baseline wander and suppresses most motion artifacts in mobile ECG recordings. This algorithm clearly shows a significant improvement compared to the conventional noise removal method. Two signal quality metrics are used to compare a reference ECG with its noisy version: correlation coefficients and mean squared error. For both metrics, the experimental results demonstrate that the noisy signal filtered by our algorithm is improved by a factor of ten.

Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network.

Acute ischemic stroke is one of the leading causes of death and long-term disability worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain tissue. Segmentation of acute ischemic stroke lesions plays a vital role to improve diagnosis, outcome assessment, and treatment planning. The current standard approach of ischemic stroke lesion segmentation is simply thresholding the Computed Tomography Perfusion (CTP) maps, i.e., quantitative feature maps created by summarizing CTP time sequence scans. However, this approach is not precise enough (its Dice similarity score is only around 50%) to be used in practice. Numerous machine learning-based techniques have recently been proposed to improve the accuracy of ischemic stroke lesion segmentation. Although they have achieved remarkable results, they still need to be improved before they can be used in actual practice. This paper presents a novel deep learning-based technique, MutiRes U-Net, for the segmentation of ischemic stroke lesions in CTP maps. MultiRes U-Net is a modified version of the original U-Net that is re-designed to be robust to segment the objects in different scales and unusual appearances. Additionally, in this paper, we propose to enrich the input CTP maps by using their contra-lateral and corresponding Tmax images. We evaluated the proposed method using the ISLES challenge 2018 dataset. As compared to the state-of-the-art methods, the results show an improvement in segmentation task accuracy. The dice similarity score (DSC) was 68%, the Jaccard score was 57.13%, and the mean absolute volume error was 22.62(ml).

Multi-View 3-D Fusion Echocardiography: Enhancing Clinical Feasibility with a Novel Processing Technique.

A novel system for fusing 3-D echocardiography data sets from complementary acoustic windows was evaluated in 12 healthy volunteers and 12 patients with heart failure. We hypothesized that 3-D fusion would enable 3-D echocardiography in patients with limited acoustic windows. At least nine 3-D data sets were recorded, while three infrared cameras tracked the position and orientation of the transducer and chest respiratory movements. Corresponding 2-D planes of the fused 3-D data sets and of single-view 3-D data sets were assessed for image quality and compared with measurements of left ventricular function obtained with contrast 2-D echocardiography. The signal-to-noise ratio in accurately fused 3-D echocardiography recordings improved by 55% in systole (p < 0.001) and 47% in diastole (p < 0.00001) compared with the apical single-view recordings. The 3-D data sets acquired during short breath holds were successfully fused in 11 of 12 patients. The improvement in endocardial border definition (from 11.7 ± 6.0 to 24.0 ± 3.3, p < 0.01) enabled quantitative assessment of left ventricular function in 10 patients, with no significant difference in ejection fraction compared with contrast 2-D echocardiography. In patients with heart failure and limited acoustic windows, the novel fusion protocol provides 3-D data sets suitable for quantitative analysis of left ventricular function.

Skeletal Deformity in Patients With Unilateral Coronal Craniosynostosis: Perceptions of the General Public.

STUDY DESIGN: A two-alternative forced choice design was used to gather perceptual data regarding unicoronal synostosis (UCS). OBJECTIVE: Cranial vault remodeling aims at improving the aesthetic appearance of infants with UCS by reshaping the forehead and reducing the potential for psychosocial discrimination. People's perception of craniofacial deformity plays a role in the stigma of deformity. The purpose of this study is to examine the relationship between objective skull deformity in UCS patients and laypersons' perception of skull normality. METHODS: Forty layperson skull raters were recruited from the general public. Skull raters were asked to categorize 45 infant skull images as normal or abnormal. Twenty-one of the images were UCS skulls, and 24 were normal skulls. Skulls were displayed briefly on a computer to simulate a first impression scenario and generate a perceptual response. A χ 2 analysis and mixed-effects regression model were used to analyze the response data. RESULTS: Members of the general public were good at distinguishing between skull groups, χ 2 (1) = 281.97, P < .001. In addition, skull raters' responses were predicted by the severity of deformity in the UCS skulls (b = -0.10, z = -2.6, P = .010, CI: -0.18, -0.02). A skull with a deformity value of 2.8 mm (CI: 1.8, 4.1) was equally likely to be rated normal or abnormal. CONCLUSIONS: This is the first study to investigate the relationship between objective skull deformity in UCS and public perception. Laypersons were good at distinguishing the difference between normal and UCS skulls, and their perceptions of normality were predicted by the degree of skull deformity.

Test-retest validation of a cranial deformity index in unilateral coronal craniosynostosis.

Unilateral coronal craniosynostosis (UCS) affects many infants resulting in abnormalities affecting the forehead and orbits. As a result, the deformity caused by UCS is very noticeable and there are several surgical treatment options available to normalize the head shape. However, there is a lack of consistently used outcome measures, resulting in difficulty assessing surgical outcomes and on-going debate over optimal treatments. Current techniques to quantify deformity in UCS are cumbersome, provide limited information, or are based on subjective assessments. In this study, a cranial deformity index was developed to quantify abnormality at the frontal bones for UCS that is accessible, user-friendly, and generates objective surface distance measurements. The cranial deformity index is defined as the Euclidean distance at the point of the largest deviation between the deformed skull compared to a reference skull. In addition, the index was successfully used to quantify post-operative changes in a single case of UCS that underwent corrective surgery. The reproducibility of the index was assessed using test-retest reliability and was demonstrated to be highly reproducible (ICC = 0.93). A user-friendly measurement index that is based on open-source software may be a valuable tool for surgical teams. In addition, this information can augment the consultation experience for patients and their families.

Cardiac rehabilitation in the paediatric Fontan population: development of a home-based high-intensity interval training programme.

INTRODUCTION: We evaluated the safety and feasibility of high-intensity interval training via a novel telemedicine ergometer (MedBIKE™) in children with Fontan physiology. METHODS: The MedBIKE™ is a custom telemedicine ergometer, incorporating a video game platform and live feed of patient video/audio, electrocardiography, pulse oximetry, and power output, for remote medical supervision and modulation of work. There were three study phases: (I) exercise workload comparison between the MedBIKE™ and a standard cardiopulmonary exercise ergometer in 10 healthy adults. (II) In-hospital safety, feasibility, and user experience (via questionnaire) assessment of a MedBIKE™ high-intensity interval training protocol in children with Fontan physiology. (III) Eight-week home-based high-intensity interval trial programme in two participants with Fontan physiology. RESULTS: There was good agreement in oxygen consumption during graded exercise at matched work rates between the cardiopulmonary exercise ergometer and MedBIKE™ (1.1 ± 0.5 L/minute versus 1.1 ± 0.5 L/minute, p = 0.44). Ten youth with Fontan physiology (11.5 ± 1.8 years old) completed a MedBIKE™ high-intensity interval training session with no adverse events. The participants found the MedBIKE™ to be enjoyable and easy to navigate. In two participants, the 8-week home-based protocol was tolerated well with completion of 23/24 (96%) and 24/24 (100%) of sessions, respectively, and no adverse events across the 47 sessions in total. CONCLUSION: The MedBIKE™ resulted in similar physiological responses as compared to a cardiopulmonary exercise test ergometer and the high-intensity interval training protocol was safe, feasible, and enjoyable in youth with Fontan physiology. A randomised-controlled trial of a home-based high-intensity interval training exercise intervention using the MedBIKE™ will next be undertaken.

Real-Time Lung Tumor Tracking Using a CUDA Enabled Nonrigid Registration Algorithm for MRI.

OBJECTIVE: This study intends to develop an accurate, real-time tumor tracking algorithm for the automated radiation therapy for cancer treatment using Graphics Processing Unit (GPU) computing. Although a previous moving mesh based tumor tracking approach has been shown to be successful in delineating the tumor regions from a sequence of magnetic resonance image, the algorithm is computationally intensive and its computation time on standard Central Processing Unit (CPU) processors is too slow to be used clinically especially for automated radiation therapy system. METHOD: A re-implementation of the algorithm on a low-cost parallel GPU-based computing platform is utilized to accelerate this computation at a speed that is amicable to clinical usages. Several components in the registration algorithm such as the computation of similarity metric are inherently parallel which fits well with the GPU parallel processing capabilities. Solving a partial differential equation numerically to generate the mesh deformation is one of the computationally intensive components which has been accelerated by utilizing a much faster shared memory on the GPU. RESULTS: Implemented on an NVIDIA Tesla K40c GPU, the proposed approach yielded a computational acceleration improvement of over 5 times its implementation on a CPU. The proposed approach yielded an average Dice score of 0.87 evaluated over 600 images acquired from six patients. CONCLUSION: This study demonstrated that the GPU computing approach can be used to accelerate tumor tracking for automated radiation therapy for mobile lung tumors. Clinical Impact: Accurately tracking mobile tumor boundaries in real-time is important to automate radiation therapy and the proposed study offers an excellent option for fast tumor region tracking for cancer treatment.

A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG).

Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient's heart conditions have been introduced on the market. Most of these devices can record a patient's bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.

Microwave Imaging of Breast Tumor Using Time-Domain UWB Circular-SAR Technique.

This paper explores the competency of the time domain ultra-wideband (UWB)-circular synthetic aperture radar (CSAR) to image the breast and detect tumors. The image reconstruction is performed using a time domain global back projection technique adapted to the circular trajectory data acquisition. This paper also proposes a sectional image reconstruction method to compensate for the group velocity changes in different layers of a multilayer medium. Experiments on an advanced breast phantom examines the suitability of this technique for breast tumor imaging. The advanced breast phantom is designed based on a MRI of a real patient, fabricated using 3D printing technology, and filled with liquids that emulate normal and cancerous tissues. The measurement results, compared with MRI imaging of the phantom, demonstrate the suitability of the UWB-CSAR method for breast tumor imaging. This method can be a tool for early diagnosis as well as for treatment monitoring during chemotherapy or radiotherapy.

Remodeling of the Actin Network Associated with the Non-Structural Protein 1 (NS1) of West Nile Virus and Formation of NS1-Containing Tunneling Nanotubes.

The cellular response to the recombinant NS1 protein of West Nile virus (NS1WNV) was studied using three different cell types: Vero E6 simian epithelial cells, SH-SY5Y human neuroblastoma cells, and U-87MG human astrocytoma cells. Cells were exposed to two different forms of NS1WNV: (i) the exogenous secreted form, sNS1WNV, added to the extracellular milieu; and (ii) the endogenous NS1WNV, the intracellular form expressed in plasmid-transfected cells. The cell attachment and uptake of sNS1WNV varied with the cell type and were only detectable in Vero E6 and SH-SY5Y cells. Addition of sNS1WNV to the cell culture medium resulted in significant remodeling of the actin filament network in Vero E6 cells. This effect was not observed in SH-SY5Y and U-87MG cells, implying that the cellular uptake of sNS1WNV and actin network remodeling were dependent on cell type. In the three cell types, NS1WNV-expressing cells formed filamentous projections reminiscent of tunneling nanotubes (TNTs). These TNT-like projections were found to contain actin and NS1WNV proteins. Interestingly, similar actin-rich, TNT-like filaments containing NS1WNV and the viral envelope glycoprotein EWNV were also observed in WNV-infected Vero E6 cells.

X-ray vision: the accuracy and repeatability of a technology that allows clinicians to see spinal X-rays superimposed on a person's back.

OBJECTIVE: Since the discovery of ionizing radiation, clinicians have evaluated X-ray images separately from the patient. The objective of this study was to investigate the accuracy and repeatability of a new technology which seeks to resolve this historic limitation by projecting anatomically correct X-ray images on to a person's skin. METHODS: A total of 13 participants enrolled in the study, each having a pre-existing anteroposterior lumbar X-ray. Each participant's image was uploaded into the Hololens Mixed reality system which when worn, allowed a single examiner to view a participant's own X-ray superimposed on the participant's back. The projected image was topographically corrected using depth information obtained by the Hololens system then aligned via existing anatomic landmarks. Using this superimposed image, vertebral levels were identified and validated against spinous process locations obtained by ultrasound. This process was repeated 1-5 days later. The projection of each vertebra was deemed to be "on-target" if it fell within the known morphological dimensions of the spinous process for that specific vertebral level. RESULTS: The projection system created on-target projections with respect to individual vertebral levels 73% of the time with no significant difference seen between testing sessions. The average repeatability for all vertebral levels between testing sessions was 77%. CONCLUSION: These accuracy and repeatability data suggest that the accuracy and repeatability of projecting X-rays directly on to the skin is feasible for identifying underlying anatomy and as such, has potential to place radiological evaluation within the patient context. Future opportunities to improve this procedure will focus on mitigating potential sources of error.