Vascular inflammation drives the development of coronary atherosclerosis and the rupture of vulnerable plaques, resulting in acute coronary syndrome (ACS).¹ Most high-risk plaques are nonobstructive and may not be detected with myocardial perfusion imaging tests, such as cardiac magnetic resonance imaging, single-photon emission computed tomography (CT), or echocardiography, as these stress tests assess stress-induced myocardial ischemia that is associated with obstructive coronary stenosis.² Standard noninvasive approaches are unable to measure coronary inflammation.³ With fluorine 18–labeled (¹⁸F) fluorodeoxyglucose positron emission tomography (PET), the detection of coronary inflammation is challenging and requires a PET scan with complex imaging protocols and processing.⁴ Thus, the noninvasive detection and identification of inflamed coronary plaques remains challenging in clinical practice.⁵

Patients who have pacemakers or defibrillators are often denied the opportunity to undergo magnetic resonance imaging (MRI) because of safety concerns, unless the devices meet certain criteria specified by the Food and Drug Administration (termed “MRI-conditional” devices).

Artificial intelligence (AI) has been developed for echocardiography^1–3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of −10.4%, 95% confidence interval: −13.2% to −7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of −0.96%, 95% confidence interval: −1.34% to −0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.

Increased attenuation of pericoronary adipose tissue (PCAT) around the proximal right coronary artery (RCA) from coronary computed tomography angiography (CTA) has been shown to be associated with coronary inflammation and improved prediction of cardiac death over plaque features. Our aim was to investigate whether PCAT CT attenuation is related to progression of coronary plaque burden. We analysed CTA studies of 111 stable patients (age 59.2 ± 9.8 years, 77% male) who underwent sequential CTA (3.4 ± 1.6 years between scans) with identical acquisition protocols. Total plaque (TP), calcified plaque (CP), non-calcified plaque (NCP), and low-density non-calcified plaque (LD-NCP) volumes and corresponding burden (plaque volume × 100%/vessel volume) were quantified using semi-automated software. PCAT CT attenuation (HU) was measured around the proximal RCA, the most standardized method for PCAT analysis. Patients with an increase in NCP burden (n = 51) showed an increase in PCAT attenuation, whereas patients with a decrease in NCP burden (n = 60) showed a decrease {4.4 [95% confidence interval (CI) 2.6–6.2] vs. −2.78 (95% CI −4.6 to −1.0) HU, P < 0.0001}. Changes in PCAT attenuation correlated with changes in the burden of NCP (r = 0.55, P < 0.001) and LD-NCP (r = 0.24, P = 0.01); but not CP burden (P = 0.3). Increased baseline PCAT attenuation ≥−75 HU was independently associated with increase in NCP (odds ratio 3.07, 95% CI 1.4–7.0; P < 0.008) and TP burden on follow-up CTA. PCAT attenuation measured from routine CTA is related to the progression of NCP and TP burden. This imaging biomarker may help to identify patients at increased risk of high-risk plaque progression and allow monitoring of beneficial changes from medical therapy.

It is well known that cardiovascular disease manifests differently in women and men. The underlying causes of these differences during the aging lifespan are less well understood. Sex differences in cardiac and vascular phenotypes are seen in childhood and tend to track along distinct trajectories related to dimorphism in genetic factors as well as response to risk exposures and hormonal changes during the life course. These differences underlie sex-specific variation in cardiovascular events later in life, including myocardial infarction, heart failure, ischemic stroke, and peripheral vascular disease. With respect to cardiac phenotypes, females have intrinsically smaller body size–adjusted cardiac volumes and they tend to experience greater age-related wall thickening and myocardial stiffening with aging. With respect to vascular phenotypes, sexual dimorphism in both physiology and pathophysiology are also seen, including overt differences in blood pressure trajectories. The majority of sex differences in myocardial and vascular alterations that manifest with aging seem to follow relatively consistent trajectories from the very early to the very later stages of life. This review aims to synthesize recent cardiovascular aging-related research to highlight clinically relevant studies in diverse female and male populations that can inform approaches to improving the diagnosis, management, and prognosis of cardiovascular disease risks in the aging population at large.

Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.