Harmonizing heterogeneous transcriptomics datasets for machine learning-based analysis to identify spaceflown murine liver-specific changes.

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches.

Deep neural networks detect regional wall motion abnormalities and preclinical cardiovascular disease from 12-lead ECGs.

Background: Identifying regional wall motion abnormalities (RWMAs) is critical for diagnosing and risk stratifying patients with cardiovascular disease, particularly ischemic heart disease. We hypothesized that a deep neural network could accurately identify patients with regional wall motion abnormalities from a readily available standard 12-lead electrocardiogram (ECG).

Methods: This observational, retrospective study included patients who were treated at Beth Israel Deaconess Medical Center and had an ECG and echocardiogram performed within 14 days of each other between 2008 and 2019.

Associations of Muscle Density and Area With Coronary Artery Plaque and Physical Function.

Objective: Skeletal muscle quality and mass are important for maintaining physical function during advancing age. We leveraged baseline data from Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) to evaluate whether paraspinal muscle density and muscle area are associated with cardiac or physical function outcomes in people with HIV (PWH).

Methods: REPRIEVE is a double-blind randomized trial evaluating the effect of pitavastatin for primary prevention of major adverse cardiovascular events in PWH.

A multi-omics longitudinal study of the murine retinal response to chronic low-dose irradiation and simulated microgravity.

The space environment includes unique hazards like radiation and microgravity which can adversely affect biological systems. We assessed a multi-omics NASA GeneLab dataset where mice were hindlimb unloaded and/or gamma irradiated for 21 days followed by retinal analysis at 7 days, 1 month or 4 months post-exposure. We compared time-matched epigenomic and transcriptomic retinal profiles resulting in a total of 4178 differentially methylated loci or regions, and 457 differentially expressed genes.

Standardized measurement of abdominal muscle by computed tomography: association with cardiometabolic risk in the Framingham Heart Study.

Objectives: To provide a standard for total abdominal muscle mass (TAM) quantification on computed tomography (CT) and investigate its association with cardiovascular risk in a primary prevention setting.

Methods: We included 3016 Framingham Heart Study participants free of cardiovascular disease (CVD) who underwent abdominal CT between 2002 and 2005. On a single CT slice at the level of L3/L4, we segmented (1) TAM-Area, (2) TAM-Index (= TAM-Area/height) and, (3) TAM-Fraction (= TAM-Area/total cross-sectional CT-area).

A Noninvasive Assessment of Flow Based on Contrast Dispersion in Computed Tomography Angiography: A Computational and Experimental Phantom Study.

Transluminal attenuation gradient (TAG), defined as the gradient of the contrast agent attenuation drop along the vessel, is an imaging biomarker that indicates stenosis in the coronary arteries. The transluminal attenuation flow encoding (TAFE) equation is a theoretical platform that quantifies blood flow in each coronary artery based on computed tomography angiography (CTA) imaging. This formulation couples TAG (i.

Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer.

Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data (n = 858) with manual heart segmentations provided by cardiovascular radiologists.

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT.

Small whole heart volume predicts cardiovascular events in patients with stable chest pain: insights from the PROMISE trial.

Objectives: The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT).

Methods: Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac.

Sex Differences in Left Ventricular Remodeling and Outcomes in Chronic Aortic Regurgitation.

Background: Left ventricular (LV) dilatation is a key compensatory feature in patients with chronic aortic regurgitation (AR). However, sex-differences in LV remodeling and outcomes in chronic AR have been poorly investigated so far.

Methods: We performed cardiovascular magnetic resonance imaging (CMR) including phase-contrast velocity-encoded imaging for the measurement of regurgitant fraction (RegF) at the sinotubular junction, in consecutive patients with at least mild AR on echocardiography.

Validation of Wall Shear Stress Assessment in Non-invasive Coronary CTA versus Invasive Imaging: A Patient-Specific Computational Study.

Endothelial shear stress (ESS) identifies coronary plaques at high risk for progression and/or rupture leading to a future acute coronary syndrome. In this study an optimized methodology was developed to derive ESS, pressure drop and oscillatory shear index using computational fluid dynamics (CFD) in 3D models of coronary arteries derived from non-invasive coronary computed tomography angiography (CTA). These CTA-based ESS calculations were compared to the ESS calculations using the gold standard with fusion of invasive imaging and CTA.

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling.

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress.

Radiomics of Coronary Artery Calcium in the Framingham Heart Study.

Purpose: To extract radiomic features from coronary artery calcium (CAC) on CT images and to determine whether this approach could improve the ability to identify individuals at risk for a composite endpoint of clinical events.

Materials And Methods: Participants from the Offspring and Third Generation cohorts of the community-based Framingham Heart Study underwent noncontrast cardiac CT (2002-2005) and were followed for more than a median of 9.1 years for composite major events.

Cardiac Computed Tomography - More Than Coronary Arteries? A Clinical Update.

Background:  Rapid improvement of scanner and postprocessing technology as well as the introduction of minimally invasive procedures requiring preoperative imaging have led to the broad utilization of cardiac computed tomography (CT) beyond coronary CT angiography (CTA).

Method:  This review article presents an overview of recent literature on cardiac CT. The goal is to summarize the current guidelines on performing cardiac CT and to list established as well as emerging techniques with a special focus on extracoronary applications.

Effect of Wall Elasticity on Hemodynamics and Wall Shear Stress in Patient-Specific Simulations in the Coronary Arteries.

Wall shear stress (WSS) has been shown to be associated with myocardial infarction (MI) and progression of atherosclerosis. Wall elasticity is an important feature of hemodynamic modeling affecting WSS calculations. The objective of this study was to investigate the role of wall elasticity on WSS, and justify use of either rigid or elastic models in future studies.

Density and morphology of coronary artery calcium for the prediction of cardiovascular events: insights from the Framingham Heart Study.

Objectives: To investigate the association between directly measured density and morphology of coronary artery calcium (CAC) with cardiovascular disease (CVD) events, using computed tomography (CT).

Methods: Framingham Heart Study (FHS) participants with CAC in noncontrast cardiac CT (2002-2005) were included and followed until 2016. Participants with known CVD or uninterpretable CT scans were excluded.

Flow Dynamics in the Aortic Arch and Its Effect on the Arterial Input Function in Cardiac Computed Tomography.

The arterial input function (AIF)-time-density curve (TDC) of contrast at the coronary ostia-plays a central role in contrast enhanced computed tomography angiography (CTA). This study employs computational modeling in a patient-specific aorta to investigate mixing and dispersion of contrast in the aortic arch (AA) and to compare the TDCs in the coronary ostium and the descending aorta. Here, we examine the validity of the use of TDC in the descending aorta as a surrogate for the AIF.

A Highly Automated Computational Method for Modeling of Intracranial Aneurysm Hemodynamics.

Intracranial aneurysms manifest in a vast variety of morphologies and their growth and rupture risk are subject to patient-specific conditions that are coupled with complex, non-linear effects of hemodynamics. Thus, studies that attempt to understand and correlate rupture risk to aneurysm morphology have to incorporate hemodynamics, and at the same time, address a large enough sample size so as to produce reliable statistical correlations. In order to perform accurate hemodynamic simulations for a large number of aneurysm cases, automated methods to convert medical imaging data to simulation-ready configuration with minimal (or no) human intervention are required.

Computed tomography-based fat and muscle characteristics are associated with mortality after transcatheter aortic valve replacement.

Background: Computed tomography (CT)-based fat and muscle measures are associated with outcome in large populations. We tested if muscle and fat characteristics are associated with long-term outcomes after TAVR.

Methods: We included 403 clinical CTs performed prior to TAVR at our center between 2008 and 2016, measuring area (cm) and density (Hounsfield units, HU) of both psoas muscles (PM), subcutaneous adipose (SAT), and visceral adipose tissue (VAT).

Estimating coronary blood flow using CT transluminal attenuation flow encoding: Formulation, preclinical validation, and clinical feasibility.

Background: We present the formulation and testing of a new CT angiography (CTA)-based method for noninvasive measurement of absolute coronary blood flow (CBF) termed transluminal attenuation flow encoding (TAFE). CTA provides assessment of coronary plaque but does not allow for detection of vessel specific ischemia. A simple and direct method to calculate absolute CBF from a standard CTA could isolate the functional consequence of disease and aid therapy decisions.

Computational Study of Computed Tomography Contrast Gradients in Models of Stenosed Coronary Arteries.

Recent computed tomography coronary angiography (CCTA) studies have noted higher transluminal contrast agent gradients in arteries with stenotic lesions, but the physical mechanism responsible for these gradients is not clear. We use computational fluid dynamics (CFD) modeling coupled with contrast agent dispersion to investigate the mechanism for these gradients. Simulations of blood flow and contrast agent dispersion in models of coronary artery are carried out for both steady and pulsatile flows, and axisymmetric stenoses of severities varying from 0% (unobstructed) to 80% are considered.

A novel method for quantifying the in-vivo mechanical effect of material injected into a myocardial infarction.

Background: Infarcted regions of myocardium exhibit functional impairment ranging in severity from hypokinesis to dyskinesis. We sought to quantify the effects of injecting a calcium hydroxyapatite-based tissue filler on the passive material response of infarcted left ventricles.

Methods: Three-dimensional finite element models of the left ventricle were developed using three-dimensional echocardiography data from sheep with a treated and untreated anteroapical infarct, to estimate the material properties (stiffness) in the infarct and remote regions.