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The sustainability of mariculture depends on adopting green technologies, which can mitigate the negative impacts on the environment and ensure long-term viability. However, existing studies do not comprehensively understand the characteristics and regional differences of green technology progress (GTP) in mariculture. According to data from ten coastal regions from 2008 to 2020, this study adopts the Epsilon-Based Measure (EBM)-Malmquist model to measure the GTP of mariculture, uses the Dagum Gini coefficient to analyze the spatial differences of GTP, and uses convergence models to explore the convergence of GTP. The results showed that: (1) the GTP of China’s mariculture showed a fluctuating upward trend temporally and significant spatial differences. The overall differences showed a dispersion trend over time. The contributions of inter-regional super variable net value difference (Gnb), inter-regional transvariation intensity (Gt), and intra-regional difference (Gw) were 38.813%, 31.256%, and 29.931%, respectively. (2) The degree of dispersion of GTP among different regions has not decreased with time, which means there is no apparent σ convergence. Absolute β convergence and conditional β convergence existed in GTP, and the absolute value of the latter was greater than that of the former. That is, the growth rate of GTP will first reach their respective steady-state levels and then approach a unified steady-state equilibrium level.

Primary immunodeficiency (PID) is a group of heterogeneous disorders resulting from immune system defects. The early PID diagnosis is compromised by the heterogeneous manifestations along with low clinical awareness. Most PID cases are significantly underdiagnosed leading to increased mortality, co-morbidities and healthcare visits and costs. Among PID, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PID. In light of available treatments for CID and CVID, it is critical to systematize their early diagnosis. Our study objectives were two-fold. First, we developed and evaluated an accurate deep learning model to analyze administrative medical claims data from EHRs towards systematizing screening and early identification of CID and CVID. Second, we revealed the most important CID- and CVID-associated clinical phenotypes and their combinations, demonstrating a systematic methodology to improve early identification of these PID. All data were composed of medical claims derived from the Optum® de-identified electronic health record (EHR) database. Four large cohorts were generated: 797, 797, 2,312, and 19,924 CID/CVID cases and equal control sizes in Cohorts 1–4, respectively (a total of 47,660 cases and controls). Two deep learning models were developed (TabMLPNet and TabResNet) and compared against baseline models. Univariate logistic regression was used to calculate odds ratios across all clinical phenotypes and their combinations. The TabMLPNet model showed the highest diagnostic performance across cohorts with sensitivity, specificity, and overall accuracy ranging from 0.82–0.88, 0.82–0.85, and 0.80–0.87, respectively. For the first time, we identified distinctive combinations of antecedent phenotypes associated with CID/CVID per cohort, being consisted of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies. Most phenotypes emerged were well described in the literature, which validated our findings. Moreover, several less well documented individual phenotypes (i.e., asthma, coagulation defects complicating pregnancy, cancer of lymphoid histiocytic tissue, lymphoid leukemia chronic) were also identified, which can lead to better clinical surveillance of PID. We demonstrated a generalized and accurate method evaluated on a large EHR-derived cohort of CID/CVID cases and controls. Our methodology can lead to the development of new clinical guidelines and pathways for earlier identification of the most important antecedent phenotypes and their combinations, enhance clinical awareness and be used to improve PID diagnosis and outcomes on a population level.

OBJECTIVESBone marrow adipose tissue (BMAT) represents >10% of total fat mass in healthy humans and further increases in diverse clinical conditions, but the impact of BMAT on human health and disease remains poorly understood. Magnetic resonance imaging (MRI) allows non-invasive measurement of the bone marrow fat fraction (BMFF), and human MRI studies have begun identifying associations between BMFF and skeletal or metabolic diseases. However, such studies have so far been limited to smaller cohorts: analysis of BMFF on a larger, population scale therefore has huge potential to reveal fundamental new knowledge of BMAT’s formation and pathophysiological functions. The UK Biobank (UKBB) is undertaking whole-body MRI of 100,000 participants, providing the ideal opportunity for such advances.MATERIALS AND METHODSHerein, we developed a deep learning pipeline for high-throughput BMFF analysis of these UKBB MRI data. Automatic bone marrow segmentation was achieved by designing new lightweight attention-based 3D U-Net convolutional neural networks that allowed more-accurate segmentation of small structures from large volumetric data. Using manual segmentations from 61-64 subjects, the models were trained against four bone marrow regions of interest: the spine, femoral head, total hip and femoral diaphysis. Models were validated using a further 10-12 datasets for each region and then used to segment datasets from a further 729 UKBB participants. BMFF was then determined and assessed for expected and new pathophysiological characteristics.RESULTSDice scores confirmed the accuracy of the models, which matched or exceeded that for conventional U-Net models. The BMFF measurements from the 729-subject cohort confirmed previously reported relationships between BMFF and age, sex and bone mineral density, while also identifying new site- and sex-specific BMFF characteristics.CONCLUSIONSWe have established a new deep learning method for accurate segmentation of small structures from large volumetric data. This method works well for accurate, large-scale BMFF analysis from UKBB MRI data and has the potential to reveal novel clinical insights. The application of our method across the full UKBB imaging cohort will therefore allow identification of the genetic and pathophysiological factors associated with altered BMAT. Together, our findings establish the utility of deep learning for population-level BMFF analysis and promise to help elucidate the full impact of BMAT on human health and disease.HighlightsWe establish a new deep learning method for image segmentation.Our method improves segmentation of small structures from large volumetric data.Using our method, we assess bone marrow fat fraction (BMFF) in UK Biobank MRI data.This is the first use of deep learning for large-scale, multi-site BMFF analysis.Our results highlight the potential of BMFF as a new clinical biomarker.

Aims Coronary artery calcification is a marker of cardiovascular risk, but its association with qualitatively and quantitatively assessed plaque subtypes is unknown. Methods and results In this post-hoc analysis, computed tomography (CT) images and 5-year clinical outcomes were assessed in SCOT-HEART trial participants. Agatston coronary artery calcium score (CACS) was measured on non-contrast CT and was stratified as zero (0 Agatston units, AU), minimal (1–9 AU), low (10–99 AU), moderate (100–399 AU), high (400–999 AU), and very high (≥1000 AU). Adverse plaques were investigated by qualitative (visual categorization of positive remodelling, low-attenuation plaque, spotty calcification, and napkin ring sign) and quantitative (calcified, non-calcified, low-attenuation, and total plaque burden; Autoplaque) assessments. Of 1769 patients, 36% had a zero, 9% minimal, 20% low, 17% moderate, 10% high, and 8% very high CACS. Amongst patients with a zero CACS, 14% had non-obstructive disease, 2% had obstructive disease, 2% had visually assessed adverse plaques, and 13% had low-attenuation plaque burden >4%. Non-calcified and low-attenuation plaque burden increased between patients with zero, minimal, and low CACS (P 1000 AU and low-attenuation plaque burden were the only predictors of myocardial infarction, independent of obstructive disease, and 10-year cardiovascular risk score. Conclusion In patients with stable chest pain, zero CACS is associated with a good but not perfect prognosis, and CACS cannot rule out obstructive coronary artery disease, non-obstructive plaque, or adverse plaque phenotypes, including low-attenuation plaque.

Deep convoloutional networks have been widely deployed in modern cyber–physical systems performing different visual classification tasks. As the fog and edge devices have different computing capacity and perform different subtasks, models trained for one device may not be deployable on another. Knowledge distillation technique can effectively compress well trained convolutional neural networks into light-weight models suitable to different devices. However, due to privacy issue and transmission cost, manually annotated data for training the deep learning models are usually gradually collected and archived in different sites. Simply training a model on powerful cloud servers and compressing them for particular edge devices failed to use the distributed data stored at different sites. This offline training approach is also inefficient to deal with new data collected from the edge devices. To overcome these obstacles, in this article, we propose the heterogeneous brain storming (HBS) method for object recognition tasks in real-world Internet of Things (IoT) scenarios. Our method enables flexible bidirectional federated learning of heterogeneous models trained on distributed datasets with a new “brain storming” mechanism and optimizable temperature parameters. In our comparison experiments, this HBS method outperformed multiple state-of-the-art single-model compression methods, as well as the newest multinetwork knowledge distillation methods with both homogeneous and heterogeneous classifiers. The ablation experiment results proved that the trainable temperature parameter into the conventional knowledge distillation loss can effectively ease the learning process of student networks in different methods. To the best of authors’ knowledge, this is the first IoT-oriented method that allows asynchronous bidirectional heterogeneous knowledge distillation in deep networks.

Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common information shared between modalities (an organ’s anatomy) is beneficial for multi-modality processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to obtain this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, even if few (semi-supervised) or no (unsupervised) annotations are available for this specific modality. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, which non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation. Code is available at https://github.com/vios-s/multimodal_segmentation .

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods., Highlights • Novel DiCyc architecture filters out domain-specific deformations. • The deformable layers are modified for less parameters and faster convergence. • Expectation–maximization training with 2-forward-1-backward back propagation. • New cycle-consistency and image alignment losses with less conflicted gradients. • An ablation study visualizes the trade-off between image alignment and quality.

Hepatosteatosis was associated with coronary artery disease at CT, including an increased burden of low-attenuation plaque, but this was not independent of other cardiovascular risk factors. PURPOSE: To assess the association between nonalcoholic fatty liver disease (NAFLD) and quantitative atherosclerotic plaque at CT. MATERIALS AND METHODS: In this post hoc analysis of the prospective Scottish Computed Tomography of the HEART trial (November 2010 to September 2014), hepatosteatosis and coronary artery calcium score were measured at noncontrast CT. Presence of stenoses, visually assessed high-risk plaque, and quantitative plaque burden were assessed at coronary CT angiography. Multivariable models were constructed to assess the impact of hepatosteatosis and cardiovascular risk factors on coronary artery disease. RESULTS: Images from 1726 participants (mean age, 58 years ± 9 [SD]; 974 men) were included. Participants with hepatosteatosis (155 of 1726, 9%) had a higher body mass index, more hypertension and diabetes mellitus, and higher cardiovascular risk scores (P < .001 for all) compared with those without hepatosteatosis. They had increased coronary artery calcium scores (median, 43 Agatston units [AU] [interquartile range, 0-273] vs 19 AU [0-225], P = .046), more nonobstructive disease (48% vs 37%, P = .02), and higher low-attenuation plaque burden (5.11% [0-7.16] vs 4.07% [0-6.84], P = .04). However, these associations were not independent of cardiovascular risk factors. Over a median of 4.7 years, there was no evidence of a difference in myocardial infarction between those with and without hepatosteatosis (1.9% vs 2.4%, P = .92). CONCLUSION: Hepatosteatosis at CT was associated with an increased prevalence of coronary artery disease at CT, but this was not independent of the presence of cardiovascular risk factors.Keywords: CT, Cardiac, Nonalcoholic Fatty Liver Disease, Coronary Artery Disease, Hepatosteatosis, Plaque QuantificationClinical trial registration no. NCT01149590 Supplemental material is available for this article. © RSNA, 2022See also commentary by Abohashem and Blankstein in this issue.

Background: Pericoronary adipose tissue (PCAT) attenuation and low-attenuation noncalcified plaque (LAP) burden can both predict outcomes. Objectives: This study sought to assess the relative and additive values of PCAT attenuation and LAP to predict future risk of myocardial infarction. Methods: In a post hoc analysis of the multicenter SCOT-HEART (Scottish Computed Tomography of the Heart) trial, the authors investigated the relationships between the future risk of fatal or nonfatal myocardial infarction and PCAT attenuation measured from coronary computed tomography angiography (CTA) using multivariable Cox regression models including plaque burden, obstructive coronary disease, and cardiac risk score (incorporating age, sex, diabetes, smoking, hypertension, hyperlipidemia, and family history). Results: In 1,697 evaluable participants (age: 58 ± 10 years), there were 37 myocardial infarctions after a median follow-up of 4.7 years. Mean PCAT was −76 ± 8 HU and median LAP burden was 4.20% (IQR: 0%-6.86%). PCAT attenuation of the right coronary artery (RCA) was predictive of myocardial infarction (HR: 1.55; P = 0.017, per 1 SD increment) with an optimum threshold of −70.5 HU (HR: 2.45; P = 0.01). In multivariable analysis, adding PCAT-RCA of ≥−70.5 HU to an LAP burden of >4% (the optimum threshold for future myocardial infarction; HR: 4.87; P < 0.0001) led to improved prediction of future myocardial infarction (HR: 11.7; P < 0.0001). LAP burden showed higher area under the curve compared to PCAT attenuation for the prediction of myocardial infarction (AUC = 0.71 [95% CI: 0.62-0.80] vs AUC = 0.64 [95% CI: 0.54-0.74]; P < 0.001), with increased area under the curve when the 2 metrics are combined (AUC = 0.75 [95% CI: 0.65-0.85]; P = 0.037). Conclusion: Coronary CTA–defined LAP burden and PCAT attenuation have marked and complementary predictive value for the risk of fatal or nonfatal myocardial infarction.

A drug delivery nanosystem of noble bimetallic nanoparticles (NPs) which consists of Au NPs capped with Pt NPs (Au@Pt NPs) is constructed and functionalised with a quinazoline based small molecule (Au@Pt@Q NPs), acting as a theranostic agent against glioblastoma. Two different hydrothermal synthetic procedures for bimetallic Au@Pt NPs are presented and the resulting nanostructures are fully characterised by means of spectroscopic and microscopic methods. The imaging and targeting capacity of the new drug delivery system is assessed through fluorescent optical microscopy and cytotoxicity evaluations. The constructed Au@Pt NPs consist a monodispersed colloidal solution of 25 nm with photoluminescent, fluorescent and X-Ray absorption properties that confirm their diagnostic potential. Haemolysis testing demonstrated that Au@Pt NPs are biocompatible and fluorescent microscopy confirmed their entering the cells. Cytological evaluation of the NPs through MTT assay showed that they do not inhibit the proliferation of control cell line HEK293, whereas they are toxic in U87MG, U251 and D54 glioblastoma cell lines; rendering them selective targeting agents for treating glioblastoma.

We formulate a single-species population model with fluctuations and migrations between two different patches (the nature reserve and the natural environment) to describe the situation that endangered species often meets with. Firstly, we prove that there exists a unique global positive solution to a stochastic single-species model with any positive initial value. Then, sufficient conditions that guarantee extinction and persistence in the mean of solutions are derived as the main results. It turns out that, under moderate fluctuation conditions, migration rates play significant roles for the persistence of endangered species in nature reserve and the natural environment. We further provide the sustainable strategies for local managers to avoid the extinction of endangered species, and separately carry out illustrative examples and numerical simulations at the end of this contribution.

In modern Internet of Things (IoT), visual analysis and predictions are often performed by deep learning models. Salient object detection (SOD) is a fundamental preprocessing for these applications. Executing SOD on the fog devices is a challenging task due to the diversity of data and fog devices. To adopt convolutional neural networks (CNN) on fog–cloud infrastructures for SOD-based applications, we introduce a semisupervised adversarial learning method in this article. The proposed model, named as SaliencyGAN, is empowered by a novel concatenated generative adversarial network (GAN) framework with partially shared parameters. The backbone CNN can be chosen flexibly based on the specific devices and applications. In the meanwhile, our method uses both the labeled and unlabeled data from different problem domains for training. Using multiple popular benchmark datasets, we compared state-of-the-art baseline methods to our SaliencyGAN obtained with 10–100% labeled training data. SaliencyGAN gained performance comparable to the supervised baselines when the percentage of labeled data reached 30%, and outperformed the weakly supervised and unsupervised baselines. Furthermore, our ablation study shows that SaliencyGAN were more robust to the common “mode missing” (or “mode collapse”) issue compared to the selected popular GAN models. The visualized ablation results have proved that SaliencyGAN learned a better estimation of data distributions. To the best of our knowledge, this is the first IoT-oriented semisupervised SOD method.

Supplemental Digital Content is available in the text., Background: The future risk of myocardial infarction is commonly assessed using cardiovascular risk scores, coronary artery calcium score, or coronary artery stenosis severity. We assessed whether noncalcified low-attenuation plaque burden on coronary CT angiography (CCTA) might be a better predictor of the future risk of myocardial infarction. Methods: In a post hoc analysis of a multicenter randomized controlled trial of CCTA in patients with stable chest pain, we investigated the association between the future risk of fatal or nonfatal myocardial infarction and low-attenuation plaque burden (% plaque to vessel volume), cardiovascular risk score, coronary artery calcium score or obstructive coronary artery stenoses. Results: In 1769 patients (56% male; 58±10 years) followed up for a median 4.7 (interquartile interval, 4.0–5.7) years, low-attenuation plaque burden correlated weakly with cardiovascular risk score (r=0.34; P

The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an important role in assessing the severity of the infection and can also be used to identify those symptomatic and asymptomatic COVID-19 carriers. With a surge of the cumulative number of COVID-19 patients, radiologists are increasingly stressed to examine the CT scans manually. Therefore, an automated 3D CT scan recognition tool is highly in demand since the manual analysis is time-consuming for radiologists and their fatigue can cause possible misjudgment. However, due to various technical specifications of CT scanners located in different hospitals, the appearance of CT images can be significantly different leading to the failure of many automated image recognition approaches. The multi-domain shift problem for the multi-center and multi-scanner studies is therefore nontrivial that is also crucial for a dependable recognition and critical for reproducible and objective diagnosis and prognosis. In this paper, we proposed a COVID-19 CT scan recognition model namely coronavirus information fusion and diagnosis network (CIFD-Net) that can efficiently handle the multi-domain shift problem via a new robust weakly supervised learning paradigm. Our model can resolve the problem of different appearance in CT scan images reliably and efficiently while attaining higher accuracy compared to other state-of-the-art methods., Comment: 32 pages, 8 figures, Applied Soft Computing

Class imbalance is one of the most basic and important problems of web data. The key to overcoming the class imbalance problems is to increase the effective instances of the minority, that is, data augmentation. Generative Adversarial Networks (GANs), which have recently been successfully applied in the field of image generation, can be used for data augmentation because they can learn the data distribution given ample training data instances and generate more data. However, learning the distributions from the imbalanced data can make GANs easily get stuck in a local optimum. In this work, we propose a new training strategy called Annealing Genetic GAN (AGGAN), which incorporates simulated annealing genetic algorithm into the training process of GANs. And this can help GANs avoid the local optimum trapping problem, which easily occurs when the training set is imbalanced. Unlike existing GANs, which use a fixed adversarial learning objective alternately training a generator, we use multiple adversarial learning objectives to train a set of generators and use the Metropolis criterion in simulated annealing to decide whether the generator should update. More specifically, the Metropolis criterion accepts worse solutions with a certain probability, so it can make our AGGAN escape from the local optimum and find a better solution. Theory and mathematical analysis provide strong theoretical support for the proposed training strategy. And experiments on several datasets demonstrate that AGGAN achieves convincing ability to solve the class imbalanced problem and reduces the training problems inherent in existing GANs.

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer's Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.

Introduction Pericoronary adipose tissue (PCAT) attenuation has emerged as a surrogate marker of pericoronary inflammation. To date, no studies have compared the impact of pericoronary adipose tissue(PCAT) attenuation and quantitative plaque burden on cardiac outcomes. We aimed to establish the relative merits of these approaches to risk prediction and hypothesised that the combination of PCAT attenuation and quantitative plaque burden measures could provide additive and improved prediction of myocardial infarction in patients with stable chest pain. Methods In a post-hoc analysis of a randomized controlled trial, we investigated the association between the future risk of fatal or non-fatal myocardial infarction and PCAT attenuation measured from CT coronary angiography using multivariable Cox regression models including plaque burden, obstructive coronary disease and cardiac risk score (incorporating age, sex, diabetes, smoking, hypertension, hyperlipidaemia and family history of cardiovascular disease). Results In 1697 evaluable participants (mean age 58±10 years), there were 37 myocardial infarctions after a median follow-up of 4.7 [interquartile interval, 4.0-5.7] years. Median low-attenuation plaque burden was 4.20[0-6.86] % and mean PCAT -76±8 Hounsfield units (HU). PCAT-RCA attenuation was higher in patients who suffered a myocardial infarction (-72.5±8.3 HU versus -76.5± 7.8 HU, p=0.0063), but there was no difference in PCAT-LAD (-76.3±8.6 HU vs -77.0±7.8 HU, p=0.54) or PCAT-LCx(-71.6±7.3 HU vs -73.3±7.7 HU, p=0.33). Patients sustaining a myocardial infarction also had higher total, non-calcified, low-attenuation and calcified plaque burden, higher Agatston calcium score, higher cardiovascular risk score and increased presence of obstructive disease on CCTA (table 1). On univariable analysis, PCAT-RCA attenuation was a predictor of myocardial infarction (HR 1.55, 95% CI 1.08 to 2.22, p=0.017), but PCAT-LAD or PCAT-LCx were not. Univariable analysis also identified the burden of non-calcified, low-attenuation and calcified plaque as well as Agatston coronary calcium score, presence of obstructive coronary artery disease and cardiovascular risk score were predictors of myocardial infarction (table 2). Male sex, age and body-mass index were not predictors of future myocardial infarction (p=0.06, p=0.86 and 0.26 respectively). In multivariable analysis, only the low-attenuation plaque burden (HR 1.80, 95% CI 1.16 to 2.81, p=0.011, per doubling) and PCAT-RCA (HR 1.47 95%1.02 to 2.13, p=0.040, per standard deviation increment) remained predictors of myocardial infarction (table 2). Based on the Youden’s index of the ROC curves, the optimal cut-off of the right coronary artery PCAT attenuation was –70.5 HU for the primary endpoint of fatal or non-fatal myocardial infarction. Patients with PCAT-RCA above ≥–70.5 HU were nearly 2.5 times more likely to suffer a myocardial infarction (HR 2.45, 95% CI 1.23 to 4.80; p=0.001). Patients with low-attenuation plaque burden (greater than 4%) were nearly 5 times more likely to suffer a myocardial infarction (HR 4.87, 95% CI 2.03 to 11.78, p 4% and PCAT-RCA ≥-70.5 HU were at the greatest risk of myocardial infarction (HR 11.7, 95% CI 3.3 to 40.9, p 4% and PCAT-RCA Conclusion CT coronary angiography defined PCAT attenuation and low-attenuation plaque have marked and additive predictive value for the risk of fatal or non-fatal myocardial infarction. Conflict of Interest Nil

OBJECTIVES: This study was designed to investigate whether coronary computed tomography angiography assessments of coronary plaque might explain differences in the prognosis of men and women presenting with chest pain. BACKGROUND: Important sex differences exist in coronary artery disease. Women presenting with chest pain have different risk factors, symptoms, prevalence of coronary artery disease and prognosis compared to men. METHODS: Within a multicenter randomized controlled trial, we explored sex differences in stenosis, adverse plaque characteristics (positive remodeling, low-attenuation plaque, spotty calcification, or napkin ring sign) and quantitative assessment of total, calcified, noncalcified and low-attenuation plaque burden. RESULTS: Of the 1,769 participants who underwent coronary computed tomography angiography, 772 (43%) were female. Women were more likely to have normal coronary arteries and less likely to have adverse plaque characteristics (p < 0.001 for all). They had lower total, calcified, noncalcified, and low-attenuation plaque burdens (p < 0.001 for all) and were less likely to have a low-attenuation plaque burden >4% (41% vs. 59%; p < 0.001). Over a median follow-up of 4.7 years, myocardial infarction (MI) occurred in 11 women (1.4%) and 30 men (3%). In those who had MI, women had similar total, noncalcified, and low-attenuation plaque burdens as men, but men had higher calcified plaque burden. Low-attenuation plaque burden predicted MI (hazard ratio: 1.60; 95% confidence interval: 1.10 to 2.34; p = 0.015), independent of calcium score, obstructive disease, cardiovascular risk score, and sex. CONCLUSIONS: Women presenting with stable chest pain have less atherosclerotic plaque of all subtypes compared to men and a lower risk of subsequent MI. However, quantitative low-attenuation plaque is as strong a predictor of subsequent MI in women as in men. (Scottish Computed Tomography of the HEART Trial [SCOT-HEART]; NCT01149590).

Aim: To examine the multimodal contrasting ability of gold-dotted magnetic nanoparticles (Au*MNPs) for magnetic resonance (MR), computed tomography (CT) and intravascular ultrasound (IVUS) imaging. Materials & methods: Au*MNPs were prepared by adapting an impregnation method, without using surface capping reagents and characterized (transmission electron microscopy, x-ray diffraction and Fourier-transform infrared spectroscopy) with their in vitro cytotoxicity assessed, followed by imaging assessments. Results: The contrast-enhancing ability of Au*MNPs was shown to be concentration-dependent across MR, CT and IVUS imaging. The Au content of the Au*MNP led to evident increases of the IVUS signal. Conclusion: We demonstrated that Au*MNPs showed concentration-dependent contrast-enhancing ability in MRI and CT imaging, and for the first-time in IVUS imaging due to the Au content. These Au*MNPs are promising toward solidifying tri-modal imaging-based theragnostics.

Cerebral amyloid angiopathy (CAA) is a cerebral small vessel disease associated with perivascular β-amyloid deposition. CAA is also associated with strokes due to lobar intracerebral haemorrhage (ICH).DynamicThe mIF 1-TC model detected perfusion deficits andPharmacokinetic (1-TC) modelling using a 30 min PET-MR time frame detected impaired haemodynamics and increased amyloid load in probable CAA. Perfusion deficits and amyloid burden co-existed within cases with CAA, demonstrating a distinct imaging pattern which may have merit in elucidating the pathophysiological process of CAA.

Multiview based learning has generally returned dividends in performance because additional information can be extracted for the representation of the diversity of different views. The advantage of multiview based learning fits the purpose of segmenting cardiac anatomy from multiview echocardiography, which is a non-invasive, low-cost and low-risk imaging modality. Nevertheless, it is still challenging because of limited training data, a poor signal-to-noise ratio of the echocardiographic data, and large variances across views for a joint learning. In addition, for a better interpretation of pathophysiological processes, clinical decision-making and prognosis, such cardiac anatomy segmentation and quantitative analysis of various clinical indices should ideally be performed for the data covering the full cardiac cycle. To tackle these challenges, a multiview recurrent aggregation network (MV-RAN) has been developed for the echocardiographic sequences segmentation with the full cardiac cycle analysis. Experiments have been carried out on multicentre and multi-scanner clinical studies consisting of spatio-temporal (2D + t) datasets. Compared to other state-of-the-art deep learning based methods, the MV-RAN method has achieved significantly superior results (0.92 ± 0.04 Dice scores) for the segmentation of the left ventricle on the independent testing datasets. For the estimation of clinical indices, our MV-RAN method has also demonstrated great promise and will undoubtedly propel forward the understanding of pathophysiological processes, computer-aided diagnosis and personalised prognosis using echocardiography.

Automatic segmentation of multi-sequence (multi-modal) cardiac MR (CMR) images plays a significant role in diagnosis and management for a variety of cardiac diseases. However, the performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information. Furthermore, particular diseases, such as myocardial infarction, display irregular shapes on images and occupy small regions at random locations. These facts make pathology segmentation of multi-modal CMR images a challenging task. In this paper, we present the Max-Fusion U-Net that achieves improved pathology segmentation performance given aligned multi-modal images of LGE, T2-weighted, and bSSFP modalities. Specifically, modality-specific features are extracted by dedicated encoders. Then they are fused with the pixel-wise maximum operator. Together with the corresponding encoding features, these representations are propagated to decoding layers with U-Net skip-connections. Furthermore, a spatial-attention module is applied in the last decoding layer to encourage the network to focus on those small semantically meaningful pathological regions that trigger relatively high responses by the network neurons. We also use a simple image patch extraction strategy to dynamically resample training examples with varying spacial and batch sizes. With limited GPU memory, this strategy reduces the imbalance of classes and forces the model to focus on regions around the interested pathology. It further improves segmentation accuracy and reduces the mis-classification of pathology. We evaluate our methods using the Myocardial pathology segmentation (MyoPS) combining the multi-sequence CMR dataset which involves three modalities. Extensive experiments demonstrate the effectiveness of the proposed model which outperforms the related baselines. The code is available at https://github.com/falconjhc/MFU-Net.

Magnetic Resonance (MR) protocols use several sequences to evaluate pathology and organ status. Yet, despite recent advances, the analysis of each sequence’s images (modality hereafter) is treated in isolation. We propose a method suitable for multimodal and multi-input learning and analysis, that disentangles anatomical and imaging factors, and combines anatomical content across the modalities to extract more accurate segmentation masks. Mis-registrations between the inputs are handled with a Spatial Transformer Network, which non-linearly aligns the (now intensity-invariant) anatomical factors. We demonstrate applications in Late Gadolinium Enhanced (LGE) and cine MRI segmentation. We show that multi-input outperforms single-input models, and that we can train a (semi-supervised) model with few (or no) annotations for one of the modalities. Code is available at https://github.com/agis85/multimodal_segmentation.

Inflammation detected through the uptake of ultrasmall superparamagnetic particles of iron oxide (USPIO) on magnetic resonance imaging (MRI) and finite element (FE) modelling of tissue stress both hold potential in the assessment of abdominal aortic aneurysm (AAA) rupture risk. This study aimed to examine the spatial relationship between these two biomarkers. Patients (n = 50) > 40 years with AAA maximum diameters > = 40 mm underwent USPIO-enhanced MRI and computed tomography angiogram (CTA). USPIO uptake was compared with wall stress predictions from CTA-based patient-specific FE models of each aneurysm. Elevated stress was commonly observed in areas vulnerable to rupture (e.g. posterior wall and shoulder). Only 16% of aneurysms exhibited co-localisation of elevated stress and mural USPIO enhancement. Globally, no correlation was observed between stress and other measures of USPIO uptake (i.e. mean or peak). It is suggested that cellular inflammation and stress may represent different but complimentary aspects of AAA disease progression. Electronic supplementary material The online version of this article (doi:10.1007/s12265-017-9766-9) contains supplementary material, which is available to authorized users.