Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization

Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an area of interest for quantification of regional cardiac function from balanced, steady state free precession (SSFP) cine sequences. However, currently available techniques lack full automation, limiting reproducibility. We propose a fully automated technique whereby a CMR image sequence is first segmented with a deep, fully convolutional neural network (CNN) architecture, and quadratic basis splines are fitted simultaneously across all cardiac frames using least squares optimization. Experiments are performed using data from 42 patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control subjects. In terms of segmentation, we compared state-of-the-art CNN frameworks, U-Net and dilated convolution architectures, with and without temporal context, using cross validation with three folds. Performance relative to expert manual segmentation was similar across all networks: pixel accuracy was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU across foreground classes only was ~85%. Endocardial left ventricular circumferential strain calculated from the proposed pipeline was significantly different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201

Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

Controlled Clinical Trial of a Self-Help for Anxiety Intervention for Patients Waiting for Psychological Therapy

This study was a controlled clinical trial in which patients were offered a brief low cost, low intensity self-help intervention while waiting for psychological therapy. A CBT based self-help pack was given to patients with significant anxiety problems and no attempt was made to exclude patients on the basis of severity or co-morbidity. The treatment group received the intervention immediately following assessment and the control group after a delay of 8 weeks so comparisons between the two groups were made over 8 weeks. Although there was some support for the effectiveness of the self help intervention, with a significant time x group interaction for CORE-OM scores, this was not significant with the intention to treat analysis, nor for HADS anxiety and depression scores and the effect size was low. A follow up evaluation suggested some patients attributed significant goal attainment to the intervention. The findings suggest the routine use of self-help interventions in psychological therapies services should be considered although further more adequately powered research is required to identify the type of patients and problems that most benefit, possible adverse effects and the effect on subsequent uptake of and engagement in therapy

Adversarial Deformation Regularization for Training Image Registration Neural Networks

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.Comment: Accepted to MICCAI 201

Global and regional left ventricular myocardial deformation measures by magnetic resonance feature tracking in healthy volunteers: comparison with tagging and relevance of gender

BACKGROUND: Feature tracking software offers measurements of myocardial strain, velocities and displacement from cine cardiovascular magnetic resonance (CMR) images. We used it to record deformation parameters in healthy adults and compared values to those obtained by tagging. METHODS: We used TomTec 2D Cardiac Performance Analysis software to derive global, regional and segmental myocardial deformation parameters in 145 healthy volunteers who had steady state free precession (SSFP) cine left ventricular short (basal, mid and apical levels) and long axis views (horizontal long axis, vertical long axis and left ventricular out flow tract) obtained on a 1.5 T Siemens Sonata scanner. 20 subjects also had tagged acquisitions and we compared global and regional deformation values obtained from these with those from Feature Tracking. RESULTS: For globally averaged measurements of strain, only those measured circumferentially in short axis slices showed reasonably good levels of agreement between FT and tagging (limits of agreement -0.06 to 0.04). Longitudinal strain showed wide limits of agreement (-0.16 to 0.03) with evidence of overestimation of strain by FT relative to tagging as the mean of both measures increased. Radial strain was systematically overestimated by FT relative to tagging with very wide limits of agreement extending to as much as 100% of the mean value (-0.01 to 0.23). Reproducibility showed similar relative trends with acceptable global inter-observer variability for circumferential measures (coefficient of variation 4.9%) but poor reproducibility in the radial direction (coefficient of variation 32.3%). Ranges for deformation parameters varied between basal, mid and apical LV levels with higher levels at base compared to apex, and between genders by both FT and tagging. CONCLUSIONS: FT measurements of circumferential but not longitudinally or radially directed global strain showed reasonable agreement with tagging and acceptable inter-observer reproducibility. We record provisional ranges of FT deformation parameters at global, regional and segmental levels. They show evidence of variation with gender and myocardial region in the volunteers studied, but have yet to be compared with tagging measurements at the segmental level

Assembly and Characterization of SAMs Formed by the Adsorption of Alkanethiols on Zinc Selenide Substrates

Alkanethiols HS(CH2)nCH3 (n = 7, 11, 15, 17) and the hydroxy functional thiol HS(CH2)12OH are shown to adsorb from solution onto zinc selenide crystals and form well-organized monolayers. The chemisorption of these organosulfur compounds has been studied using transmission Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and surface wetting properties. FTIR indicates that the longer chain alkanethiols (n = 15, 17) form well-defined SAMs with crystalline-like conformations of the chains within the monolayers. As the chain length decreases, there is less conformational order present in the monolayers. The orientation of the largely trans-zigzag alkyl chains on the surface is at a slight tilt off the surface normal direction, as indicated by the dichroism of the infrared spectra and the chain-length-dependent scaling of the mass coverage of the SAM. The structural data obtained by XPS and FTIR are compatible with the inferences derived from measurements of surface wetting properties. The thickness of the SAM layers calculated using XPS is found to be comparable to the thickness of alkanethiol SAMs formed on gold and other metal surfaces. Changes in the surface of the ZnSe crystal because of chemisorption of the thiols are illustrated by data from AFM imaging. The feasibility of forming cohesive patterned monolayers by microcontact printing is also demonstrated

Which older people decline participation in a primary care trial of physical activity and why: insights from a mixed methods approach

This article is available through the Brunel Open Access Publishing Fund. Copyright 2014 Rogers et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background: Physical activity is of vital importance to older peoples’ health. Physical activity intervention studies with older people often have low recruitment, yet little is known about non-participants. Methods: Patients aged 60–74 years from three UK general practices were invited to participate in a nurse-supported pedometer-based walking intervention. Demographic characteristics of 298 participants and 690 non-participants were compared. Health status and physical activity of 298 participants and 183 non-participants who completed a survey were compared using age, sex adjusted odds ratios (OR) (95% confidence intervals). 15 non-participants were interviewed to explore perceived barriers to participation. Results: Recruitment was 30% (298/988). Participants were more likely than non-participants to be female (54% v 47%; p = 0.04) and to live in affluent postcodes (73% v 62% in top quintile; p < 0.001). Participants were more likely than non-participants who completed the survey to have an occupational pension OR 2.06 (1.35-3.13), a limiting longstanding illness OR 1.72 (1.05-2.79) and less likely to report being active OR 0.55 (0.33-0.93) or walking fast OR 0.56 (0.37-0.84). Interviewees supported general practice-based physical activity studies, particularly walking, but barriers to participation included: already sufficiently active, reluctance to walk alone or at night, physical symptoms, depression, time constraints, trial equipment and duration. Conclusion: Gender and deprivation differences suggest some selection bias. However, trial participants reported more health problems and lower activity than non-participants who completed the survey, suggesting appropriate trial selection in a general practice population. Non-participant interviewees indicated that shorter interventions, addressing physical symptoms and promoting confidence in pursuing physical activity, might increase trial recruitment and uptake of practice-based physical activity endeavours.The National Institute for Health Research (NIHR) under its Research for Patient Benefit Programme (Grant Reference Number PB-PG-0909-20055)

Unpaired mesh-to-image translation for 3D fluorescent microscopy images of neurons

While Generative Adversarial Networks (GANs) can now reliably produce realistic images in a multitude of imaging domains, they are ill-equipped to model thin, stochastic textures present in many large 3D fluorescent microscopy (FM) images acquired in biological research. This is especially problematic in neuroscience where the lack of ground truth data impedes the development of automated image analysis algorithms for neurons and neural populations. We therefore propose an unpaired mesh-to-image translation methodology for generating volumetric FM images of neurons from paired ground truths. We start by learning unique FM styles efficiently through a Gramian-based discriminator. Then, we stylize 3D voxelized meshes of previously reconstructed neurons by successively generating slices. As a result, we effectively create a synthetic microscope and can acquire realistic FM images of neurons with control over the image content and imaging configurations. We demonstrate the feasibility of our architecture and its superior performance compared to state-of-the-art image translation architectures through a variety of texture-based metrics, unsupervised segmentation accuracy, and an expert opinion test. In this study, we use 2 synthetic FM datasets and 2 newly acquired FM datasets of retinal neurons