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Cardiovascular calcification is a health disorder with increasing prevalence and high morbidity and mortality. The only available therapeutic options for calcific vascular and valvular heart disease are invasive transcatheter procedures or surgeries that do not fully address the wide spectrum of these conditions; therefore, an urgent need exists for medical options. Cardiovascular calcification is an active process, which provides a potential opportunity for effective therapeutic targeting. Numerous biological processes are involved in calcific disease, including matrix remodelling, transcriptional regulation, mitochondrial dysfunction, oxidative stress, calcium and phosphate signalling, endoplasmic reticulum stress, lipid and mineral metabolism, autophagy, inflammation, apoptosis, loss of mineralization inhibition, impaired mineral resorption, cellular senescence and extracellular vesicles that act as precursors of microcalcification. Advances in molecular imaging and big data technology, including in multiomics and network medicine, and the integration of these approaches are helping to provide a more comprehensive map of human disease. In this Review, we discuss ectopic calcification processes in the cardiovascular system, with an emphasis on emerging mechanistic knowledge obtained through patient data and advances in imaging methods, experimental models and multiomics-generated big data. We also highlight the potential and challenges of artificial intelligence, machine learning and deep learning to integrate imaging and mechanistic data for drug discovery.

The nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state. A challenge in diagnostics is integrating different data modalities to characterize physiological state. Here, the authors show, using the heart as a model system, that cross-modal autoencoders can integrate and translate modalities to improve diagnostics and identify associated genetic variants.

Cardiovascular malformations and diseases are common but complex and often not yet fully understood. To better understand the effects of structural and microstructural changes of the heart and the vasculature on their proper functioning, a detailed characterization of the microstructure is crucial. In vivo imaging approaches are noninvasive and allow visualizing the heart and the vasculature in 3D. However, their spatial image resolution is often too limited for microstructural analyses, and hence, ex vivo imaging is preferred for this purpose. Ex vivo X-ray microfocus computed tomography (microCT) is a rapidly emerging high-resolution 3D structural imaging technique often used for the assessment of calcified tissues. Contrast-enhanced microCT (CE-CT) or phase-contrast microCT (PC-CT) improve this technique by additionally allowing the distinction of different low X-ray-absorbing soft tissues. In this review, we present the strengths of ex vivo microCT, CE-CT and PC-CT for quantitative 3D imaging of the structure and/or microstructure of the heart, the vasculature and their substructures in healthy and diseased state. We also discuss their current limitations, mainly with regard to the contrasting methods and the tissue preparation.

Vascular diseases are becoming an epidemic with an increasing aging population and increases in obesity and type II diabetes. Point-of-care (POC) diagnosis and monitoring of vascular diseases is an unmet medical need. Photoacoustic imaging (PAI) provides label-free multiparametric information of deep vasculature based on strong absorption of light photons by hemoglobin molecules. However, conventional PAI systems use bulky nanosecond lasers which hinders POC applications. Recently, light-emitting diodes (LEDs) have emerged as cost-effective and portable optical sources for the PAI of living subjects. However, state-of-art LED arrays carry significantly lower optical energy (<0.5 mJ/pulse) and high pulse repetition frequencies (PRFs) (4 KHz) compared to the high-power laser sources (100 mJ/pulse) with low PRFs of 10 Hz. Given these tradeoffs between portability, cost, optical energy and frame rate, this work systematically studies the deep tissue PAI performance of LED and laser illuminations to help select a suitable source for a given biomedical application. To draw a fair comparison, we developed a fiberoptic array that delivers laser illumination similar to the LED array and uses the same ultrasound transducer and data acquisition platform for PAI with these two illuminations. Several controlled studies on tissue phantoms demonstrated that portable LED arrays with high frame averaging show higher signal-to-noise ratios (SNRs) of up to 30 mm depth, and the high-energy laser source was found to be more effective for imaging depths greater than 30 mm at similar frame rates. Label-free in vivo imaging of human hand vasculature studies further confirmed that the vascular contrast from LED-PAI is similar to laser-PAI for up to 2 cm depths. Therefore, LED-PAI systems have strong potential to be a mobile health care technology for diagnosing vascular diseases such as peripheral arterial disease and stroke in POC and resource poor settings.

Cardiac dysfunction constitutes common cardiovascular health issues in the society, and has been an investigation topic of strong focus by researchers in the medical imaging community. Diagnostic modalities based on echocardiography, magnetic resonance imaging, chest radiography and computed tomography are common techniques that provide cardiovascular structural information to diagnose heart defects. However, functional information of cardiovascular flow, which can in fact be used to support the diagnosis of many cardiovascular diseases with a myriad of hemodynamics performance indicators, remains unexplored to its full potential. Some of these indicators constitute important cardiac functional parameters affecting the cardiovascular abnormalities. With the advancement of computer technology that facilitates high speed computational fluid dynamics, the realization of a support diagnostic platform of hemodynamics quantification and analysis can be achieved. This article reviews the state-of-the-art medical imaging and high fidelity multi-physics computational analyses that together enable reconstruction of cardiovascular structures and hemodynamic flow patterns within them, such as of the left ventricle (LV) and carotid bifurcations. The combined medical imaging and hemodynamic analysis enables us to study the mechanisms of cardiovascular disease-causing dysfunctions, such as how (1) cardiomyopathy causes left ventricular remodeling and loss of contractility leading to heart failure, and (2) modeling of LV construction and simulation of intra-LV hemodynamics can enable us to determine the optimum procedure of surgical ventriculation to restore its contractility and health This combined medical imaging and hemodynamics framework can potentially extend medical knowledge of cardiovascular defects and associated hemodynamic behavior and their surgical restoration, by means of an integrated medical image diagnostics and hemodynamic performance analysis framework.

Cardiovascular autonomic neuropathy (CAN) is associated with increased mortality in diabetes. Since CAN often develops in parallel with diabetic nephropathy as a confounder, we aimed to investigate the isolated impact of CAN on cardiovascular disease in normoalbuminuric patients. Fifty-six normoalbuminuric, type 1 diabetic patients were divided into 26 with (+) and 30 without (-) CAN according to tests of their autonomic nerve function. Coronary artery plaque burden and coronary artery calcium score (CACS) were evaluated using computed tomography. Left ventricular function was evaluated using echocardiography. Blood pressure and electrocardiography were recorded through 24 h to evaluate nocturnal drop in blood pressure (dipping) and pulse pressure. In patients +CAN compared with -CAN, the CACS was higher, and only patients +CAN had a CACS >400. A trend toward a higher prevalence of coronary plaques and flow-limiting stenosis in patients +CAN was nonsignificant. In patients +CAN, left ventricular function was decreased in both diastole and systole, nondipping was more prevalent, and pulse pressure was increased compared with -CAN. In multivariable analysis, CAN was independently associated with increased CACS, subclinical left ventricular dysfunction, and increased pulse pressure. In conclusion, CAN in normoalbuminuric type 1 diabetic patients is associated with distinct signs of subclinical cardiovascular disease.

Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

The microstructural and macrostructural integrity of white matter (WM) underpins efficient brain function, and is known to decline with age and vascular burden. Key aspects of WM health include axonal fibre density, myelination, free-water content, and the presence of tissue damage or lesions. Magnetic Resonance Imaging (MRI) offers multiple complementary sequences to non-invasively estimate these properties in vivo. For example, diffusion-weighted imaging (DWI) provides sensitive measures of microstructure, while T1-weighted and T2-weighted MRI can estimate total WM volume and hyper-intensities, and magnetisation transfer imaging (MT) and T1:T2 ratios can indicate myelin content. In this study, we leveraged all of these MRI-derived measures in a large population-based cohort (Cam-CAN) to identify latent WM factors and test how these factors relate to cardiovascular health and cognitive performance. Among 11 commonly-used WM metrics [Fractional Anisotropy (FA); Mean Signal Diffusion (MSD); Mean Signal Kurtosis (MSK); Neurite Density Index (NDI); fibre Orientation Dispersion Index (ODI); Free water volume faction (F iso ); spread of Mean Signal Diffusivity values (MSDvar); Magnetisation Transfer Ratio (MTR); T1:T2 ratio; volume of White Matter Hyper-Intensities (WMHI); White Matter Volume (WMV)], latent factor analysis showed that four factors were needed to explain 89% of the variance, which we interpreted in terms of (1) fibre density/myelination, (2) free-water / tissue damage, (3) fibre-crossing complexity and (4) microstructural complexity. These factors showed distinct effects of age and sex. To test the validity of these factors, we related them to measures of cardiovascular health and cognitive performance. Specifically, we ran path analyses linking (1) cardiovascular factors to the WM factors, and (2) the WM factors to cognitive measures. Even after adjusting for age and sex, we found that a vascular factor related to pulse pressure predicted the WM factor capturing free-water/tissue damage, and that several WM factors made unique predictions for fluid intelligence and processing speed. Our results show that there is both complementary and redundant information across common MR measures of WM, and their underlying latent factors may be useful for pinpointing the differential causes and contributions of white matter health in aging.

Small animal CT imaging provides high resolution imaging of bone structure, lungs, and gross anatomy. However, it is limited in its ability to provide high soft tissue contrast. Several blood pool CT contrast agents have been developed to enhance vascular and tissue contrast for preclinical imaging with varying enhancement capabilities. VivoVist TM is the most recent commercially available blood pool CT contrast agent for preclinical applications. This study independently evaluated its radiopacity and tissue enhancement compared to two existing preclinical CT contrast agents, Mvivo-Au, and Fenestra-HDVC. Healthy nude mice were administered one of the three contrast agents. CT imaging was performed before and at 5 minutes, 1 hour, 4 hours, 24 hours, 48 hours, 96 hours, and 7 days post-injection. Tissue intensity and the enhancement ratio relative to pre-injection levels were quantified for each contrast agent at each time point. VivoVist demonstrated significantly higher blood enhancement compared to Mvivo-Au and Fenestra-HDVC at 5 minutes and 1 hour post-injection. However, the enhancement at 4 hours and later time points was inferior to that of Mvivo-Au. VivoVist exhibited the fastest blood clearance among the three contrast agents, with a blood half-life of 3.1 hours and was largely cleared from the blood by 24 hours post-injection. In CT imaging after 24 hours post-injection, VivoVist showed the highest liver enhancement, which remained high over the 7-day imaging period. Biodistribution assessment showed that the splenic uptake of VivoVist was extremely high. Histological examination of the tissues identified abundant contrast agent accumulation in the liver and spleen. No overt pathological changes were observed in either organ one month after the injection of VivoVist. Overall, the evaluation confirmed that VivoVist is an effective CT contrast agent for vascular and liver imaging with low toxicity. However, its relatively short blood half-life limits its use as a vascular contrast agent for a prolonged period.