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 % .

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).

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.

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.

Abstract P223: Hypertension Telemonitoring And Protocolized Case Management For Community-Dwelling Older Adults: A Randomized Controlled Trial

Background: Home blood pressure (BP) telemonitoring combined with case management leads can reduce BP in adults with hypertension. However, the benefits of telemonitoring and case management for hypertension are not well established in older (age >= 65 years) adults. Interventions that can safely improve BP control in this distinct group will be critically important, particularly given recent trials showing cardiovascular benefits of intensive BP lowering in this group, and their increased vulnerability to over-treatment-related side effects.

Methods: Twelve-month, open-label, randomized controlled trial of community-dwelling older adults comparing home BP telemonitoring (HBPM) with pharmacist-led case management versus enhanced usual care with HBPM alone. The primary outcome was the proportion achieving age-specific systolic BP targets on 24-hour ambulatory BP monitoring (ABPM). Changes in HBPM were also examined. Logistic and linear regression were used for analyses, with adjustment for baseline BP.

Results: Subjects randomized to intervention (n = 61) and control (n = 59) groups were mostly female (77%), with mean age of 79.5 years. The adjusted odds ratio (aOR) for ABPM BP target achievement was 1.48 (95% CI 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 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 subjects had a higher rate of systolic BP < 110 (21% vs 5%, p = 0.009), but no differences in orthostatic symptoms, syncope, non-mechanical falls, or ED visits. Adherence to HBPM declined by 12 months, with 65% of trial-completers providing all 4 of the study-driven 3-monthly HBPM series.

Conclusions: Home BP telemonitoring and pharmacist case management did not improve achievement of target range ambulatory BP, but did reduce home BP. It was not associated with major adverse consequences. Further trials will be necessary to refine HBPM interventions, improve adherence, and determine effectiveness in older individuals more conclusively.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

A Fully Convolutional Deep Neural Network for Lung Tumor Boundary Tracking in MRI

Delineation of lung tumor from adjacent tissue from a series of magnetic resonance images (MRI) poses many difficulties due to the image similarities of the region of interest and surrounding area as well as the influence of respiration. However, accurate segmentation of the tumor region is essential in planning a radiation therapy to prevent healthy tissues from receiving excessive radiation. The manual delineation of the entire MRI sequence is tedious, time-consuming and costly. This study investigates how one can perform automatic tracking of tumor boundaries during radiation therapy using convolutional neural networks. We proposed to use a convolutional neural network architecture with modified Dice metric as the cost function. The proposed approach was evaluated over 600 images in comparison to expert manual contours. The proposed method yielded an average Dice score of $0.91 \pm 0.03$ and Hausdorff distance of $2.88 \pm 0.86$ mm. The proposed approach outperformed recent state-of-the-art methods in terms of accuracy in the delineation of the mobile tumors.

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 (NS1^WNV) 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 NS1^WNV: (i) the exogenous secreted form, sNS1^WNV, added to the extracellular milieu; and (ii) the endogenous NS1^WNV, the intracellular form expressed in plasmid-transfected cells. The cell attachment and uptake of sNS1^WNV varied with the cell type and were only detectable in Vero E6 and SH-SY5Y cells. Addition of sNS1^WNV 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 sNS1^WNV and actin network remodeling were dependent on cell type. In the three cell types, NS1^WNV-expressing cells formed filamentous projections reminiscent of tunneling nanotubes (TNTs). These TNT-like projections were found to contain actin and NS1^WNV proteins. Interestingly, similar actin-rich, TNT-like filaments containing NS1^WNV and the viral envelope glycoprotein E^WNV were also observed in WNV-infected Vero E6 cells.

Gesture Classification Using LSTM Recurrent Neural Networks

The classification of human hand gestures has gained widespread recognition as a natural and powerful way to interact intuitively and efficiently with computers. Specifically, this approach has facilitated the development of a number of important applications in the medical training field, specially as a way to objectively evaluate surgical tasks of novices compared to an expert surgeon. In this paper, 3D medical gestures, acquired by an instrumented laparoscopic forceps, were classified using Long Short Term Memory (LSTM) recurrent neural networks (RNN). Recognition results are based on gesture dynamics and a comparison of gesture trajectories between novices to an expert motion are presented. Using LSTM RNN, we were able to achieve a recognition rate of 99.1%.

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.

Multiview Three-Dimensional Echocardiography Image Fusion Using a Passive Measurement Arm

Three-dimensional (3D) echocardiography offers a fast and efficient way to scan and assess the structures and function of the heart. However, due to limitations inherent to 3D echocardiography such as limited field-of-view and low signal-to-noise ratio, 3D assessment of the heart is performed only in a minority of patients who undergo transthoracic echocardiography. One approach for improving the field-of-view and image quality is to scan the heart from multiple locations by moving the transducer and fusing the resulting images into a single volume, which requires 3D alignment of individual volumetric echocardiography scans. Previous approaches relied on optical or electromagnetic trackers for transducer tracking. This study proposes a passive measurement arm system for tracking the position of the ultrasound transducer and thereby aligning multiple echocardiography scans. The proposed system does not suffer from line-of-sight limitation as in the case of an optical tracking based fusion system. Additionally, in contrast to an electromagnetic based tracking system, measurement arm measurements are not affected by other ferromagnetic materials in the vicinity. The proposed approach was tested by scanning a heart phantom and fusing nine echocardiography volumes acquired from different locations. The fusion of all nine scans yielded a percentage field-of-view improvement of 98.5%.

Tracking tumor boundary using point correspondence for adaptive radio therapy

BACKGROUND AND OBJECTIVE

Tracking mobile tumor regions during the treatment is a crucial part of image-guided radiation therapy because of two main reasons which negatively affect the treatment process: (1) a tiny error will lead to some healthy tissues being irradiated; and (2) some cancerous cells may survive if the beam is not accurately positioned as it may not cover the entire cancerous region. However, tracking or delineation of such a tumor region from magnetic resonance imaging (MRI) is challenging due to photometric similarities of the region of interest and surrounding area as well as the influence of motion in the organs. The purpose of this work is to develop an approach to track the center and boundary of tumor region by auto-contouring the region of interest in moving organs for radiotherapy.

METHODS

We utilize a nonrigid registration method as well as a publicly available RealTITracker algorithm for MRI to delineate and track tumor regions from a sequence of MRI images. The location and shape of the tumor region in the MRI image sequence varies over time due to breathing. We investigate two approaches: the first one uses manual segmentation of the first frame during the pretreatment stage; and the second one utilizes manual segmentation of all the frames during the pretreatment stage.

RESULTS

We evaluated the proposed approaches over a sequence of 600 images acquired from 6 patients. The method that utilizes all the frames in the pretreatment stage with moving mesh based registration yielded the best performance with an average Dice Score of 0.89 ± 0.04 and Hausdorff Distance of 3.38 ± 0.10 mm.

CONCLUSIONS

This study demonstrates a promising boundary tracking tool for delineating the tumor region that can deal with respiratory movement and the constraints of adaptive radiation therapy.

Surgical gesture classification using Dynamic Time Warping and affine velocity

Minimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determiMinimally Invasive Surgery (MIS) has become widespread as an important surgical technique due to its advantages related to pain relief and short recovery time periods. However, this approach implies the acquisition of special surgical skills, which represents a challenge in the objective assessment of surgical gestures. In this way, several studies shown that kinematics and kinetic analysis of hand movement is a valuable assessment tool of basic surgical skills in MIS. In addition, recent researches proved that human motion performed during surgery can be described as a sequence of constant affine velocity movements. In this paper, we present a novel method to classify gestures based on an affine velocity analysis of 3D motion and an implementation of the Dynamic Time Warping algorithm. In particular, affine velocity calculation correlates kinematics and geometrical variables such as curvature, torsion, and euclidean velocity, reducing the dimension of the conventional 3D problem. In this way, using the simplicity of dynamic time warping algorithm allows us to perform an accurate classification, easier to implement and understand. Experimental validation of the algorithm is presented based on the position and orientation data of a laparoscope instrument, determined by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS.ned by six cameras. Results show the advantages of the proposed method compared to conventional Multidimensional Dynamic Time Warping to classify surgical gestures in MIS.

Usability and Acceptability of a Home Blood Pressure Telemonitoring Device Among Community-Dwelling Senior Citizens With Hypertension: Qualitative Study

BACKGROUND

Hypertension is a major cause of cardiovascular disease in older individuals. To ensure that blood pressure (BP) levels are within the optimal range, accurate BP monitoring is required. Contemporary hypertension clinical practice guidelines strongly endorse the use of home BP measurement as a preferred method of BP monitoring for individuals with hypertension. The benefits of home BP monitoring may be optimized when measurements are telemonitored to care providers; however, this may be challenging for older individuals with less technological capabilities.

OBJECTIVE

The objective of this qualitative study was to examine the usability and acceptability of a home BP telemonitoring device among senior citizens.

METHODS

We conducted a qualitative descriptive study. Following a 1-week period of device use, individual, semistructured interviews were conducted. Interview audio recordings were anonymized, de-identified, and transcribed verbatim. We performed thematic analysis on interview transcripts.

RESULTS

Seven senior citizens participated in the usability testing of the home BP telemonitoring device. Participants comprised females (n=4) and males (n=3) with a mean age of 86 years (range, 70-95 years). Overall, eight main themes were identified from the interviews: (1) positive features of the device; (2) difficulties or problems with the device; (3) device was simple to use; (4) comments about wireless capability and components; (5) would recommend device to someone else; (6) would use device in future; (7) suggestions for improving the device; and (8) assistance to use device. Additional subthemes were also identified.

CONCLUSIONS

Overall, the home BP telemonitoring device had very good usability and acceptability among community-dwelling senior citizens with hypertension. To enhance its long-term use, few improvements were noted that may mitigate some of the relatively minor challenges encountered by the target population.

A Novel 4D Semi-Automated Algorithm for Volumetric Segmentation in Echocardiography

Segmentation of the left ventricle (LV) in temporal 3D echocardiography sequences poses a challenge. However, it is an essential component in generating quantitative clinical measurements for the diagnosis and treatment of various cardiac diseases. Identifying the endocardial borders of the left ventricle can be difficult due to the inherent properties of ultrasound. This study proposes a 4D segmentation algorithm that segments over temporal 3D volumes that has minimal user interaction and is based on a diffeomorphic registration approach. In contrast to several existing algorithms, the proposed method does not depend on training data or make any geometrical assumptions. The algorithm was evaluated on seven patients obtained from the Mazankowski Alberta Heart Institute, Edmonton, Canada in comparison to expert manual segmentation. The proposed approach yielded Dice scores of 0.94 (0.01), 0.91 (0.03) and 0.92 (0.02) at end diastole, at end systole and over the entire cardiac cycle, respectively. The corresponding Hausdorff distance values were 4.49 (1.01) mm, 4.94 (1.41) mm, and 5.05 (0.85) mm, respectively. These results demonstrate that the proposed 4D segmentation approach for the left ventricle is robust and can potentially be used in clinical practice.

Gesture segmentation and classification using affine speed and energy

The characterization and analysis of hand gestures are challenging tasks with an important number of applications in human-computer interaction, machine vision and control, and medical gesture recognition. Specifically, several researchers have tried to develop objective evaluation methods of surgical skills for medical training. As a result, the adequate selection and extraction of similarities and differences between experts and novices have become an important challenge in this area. In particular, some of these works have shown that human movements performed during surgery can be described as a sequence of constant affine-speed trajectories. In this article, we will show that affine speed can be used to segment medical hand movements and present how the mechanical energy computed in the segment is analyzed to compare surgical skills. The position and orientation of the instrument end effectors are determined by six video photographic cameras. In addition, two laparoscopic instruments are capable of measuring simultaneously the forces and torques applied to the tool. Finally, we will report the results of these experiments and present a correlation between the mechanical energy values, dissipated during a procedure, and the surgical skills.

In-vivo patellar tracking in individuals with patellofemoral pain and healthy individuals

Understanding of the exact cause of patellofemoral pain has been limited by methodological challenges to evaluate in-vivo joint motion. This study compared six degree-of-freedom patellar motion during a dynamic lunge task between individuals with patellofemoral pain and healthy individuals. Knee joints of eight females with patellofemoral pain and ten healthy females were imaged using a CT scanner in supine lying position, then by a dual-orthogonal fluoroscope while they performed a lunge. To quantify patellar motion, the three-dimensional models of the knee bones, reconstructed from CT scans, were registered on the fluoroscopy images using the Fluomotion registration software. At full knee extension, the patella was in a significantly laterally tilted (PFP: 11.77° ± 7.58° vs. healthy: 0.86° ± 4.90°; p = 0.002) and superiorly shifted (PFP: 17.49 ± 8.44 mm vs. healthy: 9.47 ± 6.16 mm, p = 0. 033) position in the patellofemoral pain group compared with the healthy group. There were also significant differences between the groups for patellar tilt at 45°, 60°, and 75° of knee flexion, and for superior-inferior shift of the patella at 30° flexion (p ≤ 0.031). In the non-weight-bearing knee extended position, the patella was in a significantly laterally tilted position in the patellofemoral pain group (7.44° ± 6.53°) compared with the healthy group (0.71° ± 4.99°). These findings suggest the critical role of passive and active patellar stabilizers as potential causative factors for patellar malalignment/maltracking. Future studies should investigate the associations between patellar kinematics with joint morphology, muscle activity, and tendon function in a same sample for a thorough understanding of the causes of patellofemoral pain. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Baculovirus-assisted Reovirus Infection in Monolayer and Spheroid Cultures of Glioma cells

The mammalian orthoreovirus Type 3 Dearing has great potential as oncolytic agent in cancer therapy. One of the bottlenecks that hampers its antitumour efficacy in vivo is the limited tumour-cell infection and intratumoural distribution. This necessitates strategies to improve tumour penetration. In this study we employ the baculovirus Autographa californica multiple nucleopolyhedrovirus as a tool to expand the reovirus' tropism and to improve its spread in three-dimensional tumour-cell spheroids. We generated a recombinant baculovirus expressing the cellular receptor for reovirus, the Junction Adhesion Molecule-A, on its envelope. Combining these Junction Adhesion Molecule-A-expressing baculoviruses with reovirus particles leads to the formation of biviral complexes. Exposure of the reovirus-resistant glioblastoma cell line U-118 MG to the baculovirus-reovirus complexes results in efficient reovirus infection, high reovirus yields, and significant reovirus-induced cytopathic effects. As compared to the reovirus-only incubations, the biviral complexes demonstrated improved penetration and increased cell killing of three-dimensional U-118 MG tumour spheroids. Our data demonstrate that reovirus can be delivered with increased efficiency into two- and three-dimensional tumour-cell cultures via coupling the reovirus particles to baculovirus. The identification of baculovirus' capacity to penetrate into tumour tissue opens novel opportunities to improve cancer therapy by improved delivery of oncolytic viruses into tumours.

Alpha-helicoidal HEAT-like Repeat Proteins (αRep) Selected as Interactors of HIV-1 Nucleocapsid Negatively Interfere with Viral Genome Packaging and Virus Maturation

A new generation of artificial proteins, derived from alpha-helicoidal HEAT-like repeat protein scaffolds (αRep), was previously characterized as an effective source of intracellular interfering proteins. In this work, a phage-displayed library of αRep was screened on a region of HIV-1 Gag polyprotein encompassing the C-terminal domain of the capsid, the SP1 linker and the nucleocapsid. This region is known to be essential for the late steps of HIV-1 life cycle, Gag oligomerization, viral genome packaging and the last cleavage step of Gag, leading to mature, infectious virions. Two strong αRep binders were isolated from the screen, αRep4E3 (32 kDa; 7 internal repeats) and αRep9A8 (28 kDa; 6 internal repeats). Their antiviral activity against HIV-1 was evaluated in VLP-producer cells and in human SupT1 cells challenged with HIV-1. Both αRep4E3 and αRep9A8 showed a modest but significant antiviral effects in all bioassays and cell systems tested. They did not prevent the proviral integration reaction, but negatively interfered with late steps of the HIV-1 life cycle: αRep4E3 blocked the viral genome packaging, whereas αRep9A8 altered both virus maturation and genome packaging. Interestingly, SupT1 cells stably expressing αRep9A8 acquired long-term resistance to HIV-1, implying that αRep proteins can act as antiviral restriction-like factors.

Parallel implementation of a nonrigid image registration algorithm for lung tumor boundary tracking in quasi real-time MRI

This study presents an accelerated implementation of a two-dimensional moving mesh point correspondence algorithm using a GPU for tracking mobile tumor boundaries during radiation therapy. Normal CPU implementation of this algorithm is computationally intensive and time-consuming which limits its clinical utility, hence the need for a faster GPU implementation. One of the computationally intensive parts of the registration algorithm involves numerically solving a partial differential equation. In this paper we demonstrate that the computational performance of the algorithms can be improved by utilizing a shared memory implementation on the GPU. Evaluations in comparison to 600 manually drawn contours showed that the proposed GPU-based tracking of the tumor boundaries yielded similar level of accuracy as the CPU based approach with improved computational efficiency.

Deciphering critical amino acid residues to modify and enhance the binding affinity of ankyrin scaffold specific to capsid protein of human immunodeficiency virus type 1

BACKGROUND

AnkGAG1D4 is an artificial ankyrin repeat protein which recognizes the capsid protein (CA) of the human immunodeficiency virus type 1 (HIV-1) and exhibits the intracellular antiviral activity on the viral assembly process. Improving the binding affinity of AnkGAG1D4 would potentially enhance the AnkGAG1D4-mediated antiviral activity.

OBJECTIVE

To augment the affinity of AnkGAG1D4 scaffold towards its CA target, through computational predictions and experimental designs.

METHOD

Three dimensional structure of the binary complex formed by AnkGAG1D4 docked to the CA was used as a model for van der Waals (vdW) binding energy calculation. The results generated a simple guideline to select the amino acids for modifications. Following the predictions, modified AnkGAG1D4 proteins were produced and further evaluated for their CA-binding activity, using ELISA-modified method and bio-layer interferometry (BLI).

RESULTS

Tyrosine at position 56 (Y56) in AnkGAG1D4 was experimentally identified as the most critical residue for CA binding. Rational substitutions of this residue diminished the binding affinity. However, vdW calculation preconized to substitute serine for tyrosine at position 45. Remarkably, the affinity for the viral CA was significantly enhanced in AnkGAG1D4-S45Y mutant, with no alteration of the target specificity.

CONCLUSIONS

The S-to-Y mutation at position 45, based on the prediction of interacting amino acids and on vdW binding energy calculation, resulted in a significant enhancement of the affinity of AnkGAG1D4 ankyrin for its CA target. AnkGAG1D4-S45Y mutant represented the starting point for further construction of variants with even higher affinity towards the viral CA, and higher therapeutic potential in the future.

Multiview echocardiography fusion using an electromagnetic tracking system

Three-dimensional ultrasound is an emerging modality for the assessment of complex cardiac anatomy and function. The advantages of this modality include lack of ionizing radiation, portability, low cost, and high temporal resolution. Major limitations include limited field-of-view, reliance on frequently limited acoustic windows, and poor signal to noise ratio. This study proposes a novel approach to combine multiple views into a single image using an electromagnetic tracking system in order to improve the field-of-view. The novel method has several advantages: 1) it does not rely on image information for alignment, and therefore, the method does not require image overlap; 2) the alignment accuracy of the proposed approach is not affected by any poor image quality as in the case of image registration based approaches; 3) in contrast to previous optical tracking based system, the proposed approach does not suffer from line-of-sight limitation; and 4) it does not require any initial calibration. In this pilot project, we were able to show that using a heart phantom, our method can fuse multiple echocardiographic images and improve the field-of view. Quantitative evaluations showed that the proposed method yielded a nearly optimal alignment of image data sets in three-dimensional space. The proposed method demonstrates the electromagnetic system can be used for the fusion of multiple echocardiography images with a seamless integration of sensors to the transducer.

PUMA gene delivery to synoviocytes reduces inflammation and degeneration of arthritic joints

In rheumatoid arthritis (RA), the proliferation of fibroblast-like synoviocytes (FLS) is the cause of chronic inflammation in joints and of joint damage. Delivery of the pro-apoptotic gene PUMA to FLS via human adenovirus type 5 (HAdV5) vectors has been tested as a therapeutic approach, but efficiency is hampered by low transduction, as FLS do not express HAdV5 receptors on the cell surface. Here we show that efficient transduction of PUMA in FLS can be achieved by conjugating HAdV5 to a baculovirus, which binds to the cell surface via the envelope glycoprotein Gp64. Intra-articular injection in an adjuvant-induced rat model of RA induces apoptosis of FLS, leading to significant decrease in joint inflammation, joint damage, and bone loss with improvement in joint function and mobility. Our results demonstrate the therapeutic potential of PUMA gene therapy as a local treatment in various forms of arthritis in which abnormal FLS proliferation is implicated.

Proliferation of synoviocytes contributes to joint damage in rheumatoid arthritis. Here the authors show that targeting of these cells by a vector, consisting of a baculovirus conjugated to an adenovirus carrying the pro-apoptotic gene PUMA, has therapeutic efficacy in a rat arthritis model.

Patient movement compensation for 3D echocardiography fusion

Limited field of view (FOV) is a major problem for 3D real-time echocardiography (3DRTE), which results in an incomplete representation of cardiac anatomy. Various image registration techniques have been proposed to improve the field of view in 3DRTE by fusing multiple image volumes. However, these techniques require significant overlap between the individual volumes and rely on high image resolution and high signal-to-noise ratio. Changes in the heart position due to patient movement during image acquisition can also reduce the quality of image fusion. In this paper, we propose a multi-camera based optical tracking system which 1) eliminates the need for image overlap and 2) compensates for patient movement during acquisition. We compensate for patient movement by continuously tracking the patient position using skin markers and incorporating this information into the fusion process. We fuse volumes acquired during R-R wave peaks based on Electrocardiogram (ECG) data to account for retrospective image acquisition. The fusion technique was validated using a heart phantom (Shelley Medical Imaging Technologies) and on one healthy volunteer. The fused ultrasound volumes could be generated in within 2 seconds and were found to have complete myocardial boundaries alignment upon visual assessment. No stitching artefacts or movement related artefacts were observed in the fused image.

Lung tumor boundary tracking in MRI with moving mesh correspondences for adaptive radio therapy

Delineation of lung tumor regions from magnetic resonance imaging (MRI) poses many difficulties due to MR signal similarities of the region of interest and surrounding area as well as the influence of respiration. However, accurate segmentation of the tumor region is of utmost importance in planning a radiation therapy since a small error can result in some healthy tissues to receive excessive radiation. This study presents a semi-automated method to delineate lung tumor regions from a sequence of MRIs. The proposed method uses a non-rigid image registration framework to propagate the boundaries of the tumor region in MRI acquired during a radiation treatment stage, given manual segmentation on frames acquired during pretreatment stage. We investigate two approaches: 1) the first one utilizes manual segmentation of the first frame during the pretreatment stage; and 2) the second one utilizes manual segmentation of all the frames during the pretreatment stage. We evaluated the proposed approaches over a sequence of 400 images acquired from 4 patients. The proposed method based on the utilization of all the frames yielded a Dice score of 0.90 ± 0.04 and a Hausdorff distance of 1.17 ± 0.35 pixels (2.83 ± 0.79 mm) in comparison to expert manual segmentation.

Three-dimensional morphological changes of the temporomandibular joint and functional effects after mandibulotomy

BACKGROUND

The midline and paramedian mandibulotomy are surgical procedures that divide the mandibular bone into two halves and disconnects the condylar heads of the TMJ from each other. This study aimed to prospectively evaluate the temporomandibular joint (TMJ) functional and morphological changes after mandibulotomy using a reconstructed 3D models of the TMJ.

METHODS

Sixteen adult patients diagnosed with oral and oropharyngeal tumors with planned surgical mandibulotomy (test group, 9 patients) or transoral (control group, seven patients) treatments were included in the study. MRI and CBCT images were obtained immediately preceeding surgery and 6-8 weeks after surgery. Using the MRI-CBCT registered images, TMJ tissues were segmented at the two occasions by the same operator and 3D models were reconstructed for morphological assessment. Changes across time were measured using the volume overlap and Hausdorff distance of the disc and condyle 3D models. Disc-condyle relationship was measured using point-based and color map analysis. To assess the early functional changes, the Jaw function limitation scale (JFLS) and the maximum mouth opening were measured. Two-sample Hotelling T² t-test was performed to determine the significance of the morphological and clinical outcomes' differences between the two groups.

RESULTS

The two-sample Hotelling T² t-test showed significant differences (T² (df1,df2) = 0.97 (5,26), p <0.01) between the mean values of all outcomes among the 2 groups. The change in disc displacement was significantly different between the two groups (p <0.05). However, the condylar displacement was not significantly different between the two groups (p =0.3). The average of the JFLS score was five times larger after mandibulotomy, and was 2 times larger after transoral surgery (p < 0.01). Patients showed decrease in the average of the maximum interincisal mouth opening by 11 mm after mandibulotomy, and by 5.4 mm after transoral surgery.

CONCLUSION

The quantitative assessment of the TMJ showed minimal changes of the condylar position and variable degrees of articular disc displacement associated with the paramedian split mandibulotomy. As well, limited jaw functions and vertical mouth opening were noticed more in the mandibultomy group compared to the transoral group in 6- weeks after surgery.

Three-Dimensional Assessment of Temporomandibular Joint Using MRI-CBCT Image Registration

Purpose

To introduce a new approach to reconstruct a 3D model of the TMJ using magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) registered images, and to evaluate the intra-examiner reproducibility values of reconstructing the 3D models of the TMJ.

Methods

MRI and CBCT images of five patients (10 TMJs) were obtained. Multiple MRIs and CBCT images were registered using a mutual information based algorithm. The articular disc, condylar head and glenoid fossa were segmented at two different occasions, at least one-week apart, by one investigator, and 3D models were reconstructed. Differences between the segmentation at two occasions were automatically measured using the surface contours (Average Perpendicular Distance) and the volume overlap (Dice Similarity Index) of the 3D models. Descriptive analysis of the changes at 2 occasions, including means and standard deviation (SD) were reported to describe the intra-examiner reproducibility.

Results

The automatic segmentation of the condyle revealed maximum distance change of 1.9±0.93 mm, similarity index of 98% and root mean squared distance of 0.1±0.08 mm, and the glenoid fossa revealed maximum distance change of 2±0.52 mm, similarity index of 96% and root mean squared distance of 0.2±0.04 mm. The manual segmentation of the articular disc revealed maximum distance change of 3.6±0.32 mm, similarity index of 80% and root mean squared distance of 0.3±0.1 mm.

Conclusion

The MRI-CBCT registration provides a reliable tool to reconstruct 3D models of the TMJ’s soft and hard tissues, allows quantification of the articular disc morphology and position changes with associated differences of the condylar head and glenoid fossa, and facilitates measuring tissue changes over time.

Pharmacokinetics and metabolism of [14C]-tozadenant (SYN-115), a novel A2a receptor antagonist ligand, in healthy volunteers

1. This phase-I study (NCT02240290) was designed to investigate the human absorption, disposition and mass balance of ¹⁴C-tozadenant, a novel A2a receptor antagonist in clinical development for Parkinson s disease. 2. Six healthy male subjects received a single oral dose of tozadenant (240 mg containing 81.47 KBq of [¹⁴C]-tozadenant). Blood, urine and feces were collected over 14 days. Radioactivity was determined by liquid scintillation counting or accelerator mass spectrometry (AMS). Tozadenant and metabolites were characterized using HPLC-MS/MS and HPLC-AMS with fraction collection. 3. At 4 h, the C_max of tozadenant was 1.74 μg/mL and AUC_(0-t) 35.0 h μg/mL, t_1/2 15 h, Vz/F 1.82 L/kg and CL/F 1.40 mL/min/kg. For total [¹⁴C] radioactivity, the C_max was 2.29 μg eq/mL at 5 h post-dose and AUC_(0-t) 43.9 h μg eq/mL. Unchanged tozadenant amounted to 93% of the radiocarbon AUC_(0-48h). At 312 h post-dose, cumulative urinary and fecal excretion of radiocarbon reached 30.5% and 55.1% of the dose, respectively. Unchanged tozadenant reached 11% in urine and 12% of the dose in feces. Tozadenant was excreted as metabolites, including di-and mono-hydroxylated metabolites, N/O dealkylated metabolites, hydrated metabolites. 4. The only identified species circulating in plasma was unchanged tozadenant. Tozadenant was primarily excreted in urine and feces in the form of metabolites.

Multiview 3-D Echocardiography Fusion with Breath-Hold Position Tracking Using an Optical Tracking System

Recent advances in echocardiography allow real-time 3-D dynamic image acquisition of the heart. However, one of the major limitations of 3-D echocardiography is the limited field of view, which results in an acquisition insufficient to cover the whole geometry of the heart. This study proposes the novel approach of fusing multiple 3-D echocardiography images using an optical tracking system that incorporates breath-hold position tracking to infer that the heart remains at the same position during different acquisitions. In six healthy male volunteers, 18 pairs of apical/parasternal 3-D ultrasound data sets were acquired during a single breath-hold as well as in subsequent breath-holds. The proposed method yielded a field of view improvement of 35.4 ± 12.5%. To improve the quality of the fused image, a wavelet-based fusion algorithm was developed that computes pixelwise likelihood values for overlapping voxels from multiple image views. The proposed wavelet-based fusion approach yielded significant improvement in contrast (66.46 ± 21.68%), contrast-to-noise ratio (49.92 ± 28.71%), signal-to-noise ratio (57.59 ± 47.85%) and feature count (13.06 ± 7.44%) in comparison to individual views.

Telemonitoring and Protocolized Case Management for Hypertensive Community-Dwelling Seniors With Diabetes: Protocol of the TECHNOMED Randomized Controlled Trial

Background

Diabetes and hypertension are devastating, deadly, and costly conditions that are very common in seniors. Controlling hypertension in seniors with diabetes dramatically reduces hypertension-related complications. However, blood pressure (BP) must be lowered carefully because seniors are also susceptible to low BP and attendant harms. Achieving “optimal BP control” (ie, avoiding both undertreatment and overtreatment) is the ultimate therapeutic goal in such patients. Regular BP monitoring is required to achieve this goal. BP monitoring at home is cheap, convenient, widely used, and guideline endorsed. However, major barriers prevent proper use. These may be overcome through use of BP telemonitoring—the secure teletransmission of BP readings to a health portal, where BP data are summarized for provider and patient use, with or without protocolized case management.

Objective

To examine the incremental effectiveness, safety, cost-effectiveness, usability, and acceptability of home BP telemonitoring, used with or without protocolized case management, compared with “enhanced usual care” in community-dwelling seniors with diabetes and hypertension.

Methods

A 300-patient, 3-arm, pragmatic randomized controlled trial with blinded outcome ascertainment will be performed in seniors with diabetes and hypertension living independently in seniors’ residences in greater Edmonton. Consenting patients will be randomized to usual care, home BP telemonitoring alone, or home BP telemonitoring plus protocolized pharmacist case management. Usual care subjects will receive a home BP monitor but neither they nor their providers will have access to teletransmitted data. In both telemonitored arms, providers will receive telemonitored BP data summaries. In the case management arm, pharmacist case managers will be responsible for reviewing teletransmitted data and initiating guideline-concordant and protocolized changes in BP management.

Results

Outcomes will be ascertained at 6 and 12 months. Within-study-arm change scores will be calculated and compared between study arms. These include: (1) clinical outcomes: proportion of subjects with a mean 24-hour ambulatory systolic BP in the optimal range (110-129 mmHg in patients 65-79 years and 110-139 mmHg in those ≥80 years: primary outcome); additional ambulatory and home BP outcomes; A1c and lipid profile; medications, cognition, health care use, cardiovascular events, and mortality. (2) Safety outcomes: number of serious episodes of hypotension, syncope, falls, and electrolyte disturbances (requiring third party assistance or medical attention). (3) Humanistic outcomes: quality of life, satisfaction, and medication adherence. (4) Economic outcomes: incremental costs, incremental cost-utility, and cost per mmHg change in BP of telemonitoring ± case management compared with usual care (health payor and societal perspectives). (5) Intervention usability and acceptability to patients and providers.

Conclusion

The potential benefits of telemonitoring remain largely unstudied and unproven in seniors. This trial will comprehensively assess the impact of home BP telemonitoring across a range of outcomes. Results will inform the value of implementing home-based telemonitoring within supportive living residences in Canada.

Trial Registration

Clinicaltrials.gov NCT02721667; https://clinicaltrials.gov/ct2/show/NCT02721667 (Archived by Webcite at http://www.webcitation.org/6i8tB20Mc)

Depth Perception of Surgeons in Minimally Invasive Surgery

Minimally invasive surgery (MIS) poses visual challenges to the surgeons. In MIS, binocular disparity is not freely available for surgeons, who are required to mentally rebuild the 3-dimensional (3D) patient anatomy from a limited number of monoscopic visual cues. The insufficient depth cues from the MIS environment could cause surgeons to misjudge spatial depth, which could lead to performance errors thus jeopardizing patient safety. In this article, we will first discuss the natural human depth perception by exploring the main depth cues available for surgeons in open procedures. Subsequently, we will reveal what depth cues are lost in MIS and how surgeons compensate for the incomplete depth presentation. Next, we will further expand our knowledge by exploring some of the available solutions for improving depth presentation to surgeons. Here we will review the innovative approaches (multiple 2D camera assembly, shadow introduction) and devices (3D monitors, head-mounted devices, and auto-stereoscopic monitors) for 3D image presentation from the past few years.

Detecting left ventricular impaired relaxation in cardiac MRI using moving mesh correspondences

Anatomical cine cardiovascular magnetic resonance (CMR) imaging is widely used to assess the systolic cardiac function because of its high soft tissue contrast. Assessment of diastolic LV function has not regularly been performed due the complex and time consuming procedures. This study presents a semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images. The proposed method is based on three main steps: (1) non-rigid registration, which yields a sequence of endocardial boundary points over the cardiac cycle based on a user-provided contour on the first frame; (2) LV volume and filling rate computations over the cardiac cycle; and (3) automated detection of the peak values of early (E) and late ventricular (A) filling waves. In 47 patients cine CMR imaging and Doppler-echocardiographic imaging were performed. CMR measurements of peak values of the E and A waves as well as the deceleration time were compared with the corresponding values obtained in Doppler-Echocardiography. For the E/A ratio the proposed algorithm for CMR yielded a Cohen's kappa measure of 0.70 and a Gwet's AC1 coefficient of 0.70.

CONCLUSION

Semi-automated assessment of the left ventricular (LV) diastolic function using anatomical short-axis cine CMR images provides mitral inflow measurements comparable to Doppler-Echocardiography.

Atlas to patient registration with brain tumor based on a mesh-free method

Brain atlas to patient registration in the presence of tumors is a challenging task because its presence cause brain structure deformations and introduce large intensity variation between the affected areas. This large dissimilarity affects the results of traditional registration methods based on intensity or shape similarities. In order to overcome these problems, we propose a novel method that brings closer the atlas and the patient's image by simulating the mechanical behavior of brain deformation under a tumor pressure. The proposed method use a mesh-free total Lagrangian Explicit Dynamic algorithm for the simulation of atlas deformation and a data driven model of the tumor using multi-modal MRI segmentation. Experimental results look structurally very similar to the patient's image and outperform two of the top ranking algorithms.

A GPU accelerated moving mesh correspondence algorithm with applications to RV segmentation

This study proposes a parallel nonrigid registration algorithm to obtain point correspondence between a sequence of images. Several recent studies have shown that computation of point correspondence is an excellent way to delineate organs from a sequence of images, for example, delineation of cardiac right ventricle (RV) from a series of magnetic resonance (MR) images. However, nonrigid registration algorithms involve optimization of similarity functions, and are therefore, computationally expensive. We propose Graphics Processing Unit (GPU) computing to accelerate the algorithm. The proposed approach consists of two parallelization components: 1) parallel Compute Unified Device Architecture (CUDA) version of the non-rigid registration algorithm; and 2) application of an image concatenation approach to further parallelize the algorithm. The proposed approach was evaluated over a data set of 16 subjects and took an average of 4.36 seconds to segment a sequence of 19 MR images, a significant performance improvement over serial image registration approach.