Ultrasound (US) imaging is a critical tool in medical diagnostics, offering real-time visualization of physiological processes. One of its major advantages is its ability to capture temporal dynamics, which is essential for assessing motion patterns in applications such as cardiac monitoring, fetal development, and vascular imaging. Despite its importance, current deep learning models often overlook the temporal continuity of ultrasound sequences, analyzing frames independently and missing key temporal dependencies. To address this gap, we propose a method for learning effective temporal representations from ultrasound videos, with a focus on echocardiography-based ejection fraction (EF) estimation. EF prediction serves as an ideal case study to demonstrate the necessity of temporal learning, as it requires capturing the rhythmic contraction and relaxation of the heart. Our approach leverages temporally consistent masking and contrastive learning to enforce temporal coherence across video frames, enhancing the model's ability to represent motion patterns. Evaluated on the EchoNet-Dynamic dataset, our method achieves a substantial improvement in EF prediction accuracy, highlighting the importance of temporally-aware representation learning for real-time ultrasound analysis.

Advances in intracoronary imaging have made it possible to distinguish different pathological mechanisms underlying acute coronary syndrome (ACS) in vivo. Accurate identification of these mechanisms is increasingly recognized as essential for enabling tailored therapeutic strategies. ACS pathogenesis is primarily classified into 2 major types: plaque rupture (PR) and plaque erosion (PE). Patients with PR are treated with intracoronary stenting, whereas those with PE may be potentially managed conservatively without stenting. The aim of this study is to develop neural networks capable of distinguishing PR from PE solely using coronary angiography (CAG). A total of 842 videos from 278 ACS patients (PR:172, PE:106) were included. To ensure the reliability of the ground truth for PR/PE classification, the ACS pathogenesis for each patient was confirmed using Optical Coherence Tomography (OCT). To enhance the learning of discriminative features across consecutive frames and improve PR/PE classification performance, we propose Sequence Contrastive Learning (SeqCon), which addresses the limitations inherent in conventional contrastive learning approaches. In the experiments, the external test set consisted of 18 PR patients (46 videos) and 11 PE patients (30 videos). SeqCon achieved an accuracy of 82.8%, sensitivity of 88.9%, specificity of 72.3%, positive predictive value of 84.2%, and negative predictive value of 80.0% at the patient-level. This is the first report to use contrastive learning for diagnosing the underlying mechanism of ACS by CAG. We demonstrated that it can be feasible to distinguish between PR and PE without intracoronary imaging modalities.

Epicardial adipose tissue (EAT) is a metabolically active fat depot associated with coronary atherosclerosis and cardiovascular (CV) risk. While EAT is a known prognostic marker in lung cancer screening, its sex-specific prognostic value remains unclear. This study investigated sex differences in the prognostic utility of serial EAT measurements on low-dose chest CTs. We analyzed baseline and two-year changes in EAT volume and density using a validated automated deep-learning algorithm in 24,008 heavy-smoking participants from the National Lung Screening Trial (NLST). Sex-stratified multivariable Cox models, adjusted for CV risk factors, BMI, and coronary artery calcium (CAC), assessed associations between EAT and all-cause and CV mortality (median follow-up 12.3 years [IQR: 11.9-12.8], 4,668 [19.4%] all-cause deaths, 1,083 [4.5%] CV deaths).Women (n = 9,841; 41%) were younger, with fewer CV risk factors, lower BMI, fewer pack-years, and lower CAC than men (all P < 0.001). Baseline EAT was associated with similar all-cause and CV mortality risk in both sexes (max. aHR women: 1.70; 95%-CI: 1.13-2.55; men: 1.83; 95%-CI: 1.40-2.40, P-interaction=0.986). However, two-year EAT changes predicted CV death only in women (aHR: 1.82; 95%-CI: 1.37-2.49, P < 0.001), and showed a stronger association with all-cause mortality in women (aHR: 1.52; 95%-CI: 1.31-1.77) than in men (aHR: 1.26; 95%-CI: 1.13-1.40, P-interaction=0.041). In this large lung cancer screening cohort, serial EAT changes independently predicted CV mortality in women and were more strongly associated with all-cause mortality in women than in men. These findings support routine EAT quantification on chest CT for improved, sex-specific cardiovascular risk stratification.

Accurate cardiac motion estimation from cine cardiac magnetic resonance (CMR) images is vital for assessing cardiac function and detecting its abnormalities. Existing methods often struggle to capture heart motion accurately because they rely on intensity-based image registration similarity losses that may overlook cardiac anatomical regions. To address this, we propose CardioMorphNet, a recurrent Bayesian deep learning framework for 3D cardiac shape-guided deformable registration using short-axis (SAX) CMR images. It employs a recurrent variational autoencoder to model spatio-temporal dependencies over the cardiac cycle and two posterior models for bi-ventricular segmentation and motion estimation. The derived loss function from the Bayesian formulation guides the framework to focus on anatomical regions by recursively registering segmentation maps without using intensity-based image registration similarity loss, while leveraging sequential SAX volumes and spatio-temporal features. The Bayesian modelling also enables computation of uncertainty maps for the estimated motion fields. Validated on the UK Biobank dataset by comparing warped mask shapes with ground truth masks, CardioMorphNet demonstrates superior performance in cardiac motion estimation, outperforming state-of-the-art methods. Uncertainty assessment shows that it also yields lower uncertainty values for estimated motion fields in the cardiac region compared with other probabilistic-based cardiac registration methods, indicating higher confidence in its predictions.

Inflammation plays important roles in the pathogenesis of vascular diseases. We here show the involvement of perivascular inflammation in aortic dilatation of Marfan syndrome (MFS). In the aorta of MFS patients and Fbn1C1041G/+ mice, macrophages markedly accumulated in periaortic tissues with increased inflammatory cytokine expression. Metabolic inflammatory stress induced by a high-fat diet (HFD) enhanced vascular inflammation predominantly in periaortic tissues and accelerated aortic dilatation in Fbn1C1041G/+ mice, both of which were inhibited by low-dose pitavastatin. HFD feeding also intensifies structural disorganization of the tunica media in Fbn1C1041G/+ mice, including elastic fiber fragmentation, fibrosis, and proteoglycan accumulation, along with increased activation of TGF-β downstream targets. Pitavastatin treatment mitigated these alterations. For non-invasive assessment of PVAT inflammation in a clinical setting, we developed an automated analysis program for CT images using machine learning techniques to calculate the perivascular fat attenuation index of the ascending aorta (AA-FAI), correlating with periaortic fat inflammation. The AA-FAI was significantly higher in patients with MFS compared to patients without hereditary connective tissue disorders. These results suggest that perivascular inflammation contributes to aneurysm formation in MFS and might be a potential target for preventing and treating vascular events in MFS.

Pretrained segmentation models for cardiac magnetic resonance imaging (MRI) struggle to generalize across different imaging sequences due to significant variations in image contrast. These variations arise from changes in imaging protocols, yet the same fundamental spin properties, including proton density, T1, and T2 values, govern all acquired images. With this core principle, we introduce Reverse Imaging, a novel physics-driven method for cardiac MRI data augmentation and domain adaptation to fundamentally solve the generalization problem. Our method reversely infers the underlying spin properties from observed cardiac MRI images, by solving ill-posed nonlinear inverse problems regularized by the prior distribution of spin properties. We acquire this "spin prior" by learning a generative diffusion model from the multiparametric SAturation-recovery single-SHot acquisition sequence (mSASHA) dataset, which offers joint cardiac T1 and T2 maps. Our method enables approximate but meaningful spin-property estimates from MR images, which provide an interpretable "latent variable" that lead to highly flexible image synthesis of arbitrary novel sequences. We show that Reverse Imaging enables highly accurate segmentation across vastly different image contrasts and imaging protocols, realizing wide-spectrum generalization of cardiac MRI segmentation.

Accelerated Cardiovascular Magnetic Resonance (CMR) image reconstruction remains a critical challenge due to the trade-off between scan time and image quality, particularly when generalizing across diverse acquisition settings. We propose GENRE-CMR, a generative adversarial network (GAN)-based architecture employing a residual deep unrolled reconstruction framework to enhance reconstruction fidelity and generalization. The architecture unrolls iterative optimization into a cascade of convolutional subnetworks, enriched with residual connections to enable progressive feature propagation from shallow to deeper stages. To further improve performance, we integrate two loss functions: (1) an Edge-Aware Region (EAR) loss, which guides the network to focus on structurally informative regions and helps prevent common reconstruction blurriness; and (2) a Statistical Distribution Alignment (SDA) loss, which regularizes the feature space across diverse data distributions via a symmetric KL divergence formulation. Extensive experiments confirm that GENRE-CMR surpasses state-of-the-art methods on training and unseen data, achieving 0.9552 SSIM and 38.90 dB PSNR on unseen distributions across various acceleration factors and sampling trajectories. Ablation studies confirm the contribution of each proposed component to reconstruction quality and generalization. Our framework presents a unified and robust solution for high-quality CMR reconstruction, paving the way for clinically adaptable deployment across heterogeneous acquisition protocols.

Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed from patient-specific medical images via machine learning algorithms, bypassing the need for time-intensive computational fluid simulation. However, it is extremely challenging to amass large-enough datasets to effectively train such models. We could address this data scarcity by means of self-supervised pre-training and foundations models given large datasets of geometric artery models. In the context of coronary arteries, leveraging learned representations to improve hemodynamic biomarker assessment has not yet been well studied. In this work, we address this gap by investigating whether a large dataset (8449 shapes) consisting of geometric models of 3D blood vessels can benefit wall shear stress assessment in coronary artery models from a small-scale clinical trial (49 patients). We create a self-supervised target for the 3D blood vessels by computing the heat kernel signature, a quantity obtained via Laplacian eigenvectors, which captures the very essence of the shapes. We show how geometric representations learned from this datasets can boost segmentation of coronary arteries into regions of low, mid and high (time-averaged) wall shear stress even when trained on limited data.

BackgroundSphericity is a measurement of how closely an object approximates a globe. The sphericity of the blood pool of the left ventricle (LV), is an emerging measure linked to myocardial dysfunction. MethodsVideo-based deep learning models were trained for semantic segmentation (pixel labeling) in cardiac magnetic resonance imaging in 84,327 UK Biobank participants. These labeled pixels were co-oriented in 3D and used to construct surface meshes. LV ejection fraction, mass, volume, surface area, and sphericity were calculated. Epidemiologic and genetic analyses were conducted. Polygenic score validation was performed in All of Us. Results3D LV sphericity was found to be more strongly associated (HR 10.3 per SD, 95% CI 6.1-17.3) than LV ejection fraction (HR 2.9 per SD reduction, 95% CI 2.4-3.6) with dilated cardiomyopathy (DCM). Paired with whole genome sequencing, these measurements linked LV structure and function to 366 distinct common and low-frequency genetic loci--and 17 genes with rare variant burden--spanning a 25-fold range of effect size. The discoveries included 22 out of the 26 loci that were recently associated with DCM. LV genome-wide polygenic scores were equivalent to, or outperformed, dedicated hypertrophic cardiomyopathy (HCM) and DCM polygenic scores for disease prediction. In All of Us, those in the polygenic extreme 1% had an estimated 6.6% risk of DCM by age 80, compared to 33% for carriers of rare truncating variants in the gene TTN. Conclusions3D sphericity is a distinct, heritable LV measurement that is intricately linked to risk for HCM and DCM. The genetic findings from this study raise the possibility that the majority of common genetic loci that will be discovered in future large-scale DCM analyses are present in the current results.

Accurate prediction of major adverse cardiac events (MACE) remains a central challenge in cardiovascular prognosis. We present PRISM (Prompt-guided Representation Integration for Survival Modeling), a self-supervised framework that integrates visual representations from non-contrast cardiac cine magnetic resonance imaging with structured electronic health records (EHRs) for survival analysis. PRISM extracts temporally synchronized imaging features through motion-aware multi-view distillation and modulates them using medically informed textual prompts to enable fine-grained risk prediction. Across four independent clinical cohorts, PRISM consistently surpasses classical survival prediction models and state-of-the-art (SOTA) deep learning baselines under internal and external validation. Further clinical findings demonstrate that the combined imaging and EHR representations derived from PRISM provide valuable insights into cardiac risk across diverse cohorts. Three distinct imaging signatures associated with elevated MACE risk are uncovered, including lateral wall dyssynchrony, inferior wall hypersensitivity, and anterior elevated focus during diastole. Prompt-guided attribution further identifies hypertension, diabetes, and smoking as dominant contributors among clinical and physiological EHR factors.