Hasil untuk "Diseases of the circulatory (Cardiovascular) system"

Giuseppe Marano, Ida Paris, Marianna Mazza

Niranjana Arun Menon, Yulong Li, Iqra Farooq et al.

Cardiovascular disease (CVD) risk stratification remains a major challenge due to its multifactorial nature and limited availability of high-quality labeled datasets. While genomic and electrophysiological data such as SNP variants and ECG phenotypes are increasingly accessible, effectively integrating these modalities in low-label settings is non-trivial. This challenge arises from the scarcity of well-annotated multimodal datasets and the high dimensionality of biological signals, which limit the effectiveness of conventional supervised models. To address this, we present a few-label multimodal framework that leverages large language models (LLMs) to combine genetic and electrophysiological information for cardiovascular risk stratification. Our approach incorporates a pseudo-label refinement strategy to adaptively distill high-confidence labels from weakly supervised predictions, enabling robust model fine-tuning with only a small set of ground-truth annotations. To enhance the interpretability, we frame the task as a Chain of Thought (CoT) reasoning problem, prompting the model to produce clinically relevant rationales alongside predictions. Experimental results demonstrate that the integration of multimodal inputs, few-label supervision, and CoT reasoning improves robustness and generalizability across diverse patient profiles. Experimental results using multimodal SNP variants and ECG-derived features demonstrated comparable performance to models trained on the full dataset, underscoring the promise of LLM-based few-label multimodal modeling for advancing personalized cardiovascular care.

Peihong Zhang, Zhixin Li, Rui Sang et al.

The coupling signal refers to a latent physiological signal that characterizes the transformation from cardiac electrical excitation, captured by the electrocardiogram (ECG), to mechanical contraction, recorded by the phonocardiogram (PCG). By encoding the temporal and functional interplay between electrophysiological and hemodynamic events, it serves as an intrinsic link between modalities and offers a unified representation of cardiac function, with strong potential to enhance multi-modal cardiovascular disease (CVD) detection. However, existing coupling signal estimation methods remain highly vulnerable to noise, particularly in real-world clinical and physiological settings, which undermines their robustness and limits practical value. In this study, we propose Noise-Robust Multi-Modal Coupling Signal Estimation (NMCSE), which reformulates coupling signal estimation as a distribution matching problem solved via optimal transport. By jointly aligning amplitude and timing, NMCSE avoids noise amplification and enables stable signal estimation. When integrated into a Temporal-Spatial Feature Extraction (TSFE) network, the estimated coupling signal effectively enhances multi-modal fusion for more accurate CVD detection. To evaluate robustness under real-world conditions, we design two complementary experiments targeting distinct sources of noise. The first uses the PhysioNet 2016 dataset with simulated hospital noise to assess the resilience of NMCSE to clinical interference. The second leverages the EPHNOGRAM dataset with motion-induced physiological noise to evaluate intra-state estimation stability across activity levels. Experimental results show that NMCSE consistently outperforms existing methods under both clinical and physiological noise, highlighting it as a noise-robust estimation approach that enables reliable multi-modal cardiac detection in real-world conditions.

Chanti Raju Mylay, Bobin Deng, Zhipeng Cai et al.

Crop diseases pose significant threats to global food security, agricultural productivity, and sustainable farming practices, directly affecting farmers' livelihoods and economic stability. To address the growing need for effective crop disease management, AI-based disease alerting systems have emerged as promising tools by providing early detection and actionable insights for timely intervention. However, existing systems often overlook critical aspects such as data privacy, market pricing power, and farmer-friendly usability, leaving farmers vulnerable to privacy breaches and economic exploitation. To bridge these gaps, we propose AgriSentinel, the first Privacy-Enhanced Embedded-LLM Crop Disease Alerting System. AgriSentinel incorporates a differential privacy mechanism to protect sensitive crop image data while maintaining classification accuracy. Its lightweight deep learning-based crop disease classification model is optimized for mobile devices, ensuring accessibility and usability for farmers. Additionally, the system includes a fine-tuned, on-device large language model (LLM) that leverages a curated knowledge pool to provide farmers with specific, actionable suggestions for managing crop diseases, going beyond simple alerting. Comprehensive experiments validate the effectiveness of AgriSentinel, demonstrating its ability to safeguard data privacy, maintain high classification performance, and deliver practical, actionable disease management strategies. AgriSentinel offers a robust, farmer-friendly solution for automating crop disease alerting and management, ultimately contributing to improved agricultural decision-making and enhanced crop productivity.

Zahra Mirzaiyan, Michele Girfoglio, Gianluigi Rozza

It is well known that in the computational fluid dynamics simulations related to the cardiovascular system the enforcement of outflow boundary conditions is a crucial point. In fact, they highly affect the computed flow and a wrong setup could lead to unphysical results. In this chapter we discuss the main features of two different ways for the estimation of proper outlet boundary conditions in the context of hemodynamics simulations: on one side, a lumped parameter model of the downstream circulation and, on the other one, a technique based on optimal control.

Md. Shaheenur Islam Sumon, Md. Sakib Bin Islam, Md. Sohanur Rahman et al.

The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relationships between various features within biological systems remain ambiguous in these spaces, highlighting the necessity of early detection and prediction of cardiovascular diseases to reduce the severe morbidity and mortality with these conditions worldwide. We handle this issue with CardioTabNet, which exploits the strength of tab transformer to extract feature space which carries strong understanding of clinical cardiovascular data and its feature ranking. As a result, performance of downstream classical models significantly showed outstanding result. Our study utilizes the open-source dataset for heart disease prediction with 1190 instances and 11 features. In total, 11 features are divided into numerical (age, resting blood pressure, cholesterol, maximum heart rate, old peak, weight, and fasting blood sugar) and categorical (resting ECG, exercise angina, and ST slope). Tab transformer was used to extract important features and ranked them using random forest (RF) feature ranking algorithm. Ten machine-learning models were used to predict heart disease using selected features. After extracting high-quality features, the top downstream model (a hyper-tuned ExtraTree classifier) achieved an average accuracy rate of 94.1% and an average Area Under Curve (AUC) of 95.0%. Furthermore, a nomogram analysis was conducted to evaluate the model's effectiveness in cardiovascular risk assessment. A benchmarking study was conducted using state-of-the-art models to evaluate our transformer-driven framework.

Reinier W. P. Tack, Jasper R. Senff, Tamara N. Kimball et al.

Background Stroke and dementia are leading causes of mortality and can be prevented through risk factor management. Risk factor assessment requires laboratory or physical measurements. We aimed to determine whether self‐reported risk factors serve as reliable proxies and predict stroke‐ and dementia‐related mortality. Methods and Results We used cross‐sectional data from the NHANES (National Health and Nutrition Examination Survey) from 1999 to 2018 linked to National Death Index records. We included participants with available data on self‐reported and measured hypertension, hypercholesterolemia, diabetes, kidney disease, hearing impairment and overweight. Reliability was assessed using F1 scores, and used survey‐weighted Cox‐proportional hazards models evaluated associations with stroke‐ or dementia‐related mortality. Reliability of self‐reported risk factors was highest in overweight (F1 score 0.81, sensitivity 76%, specificity 77%) and diabetes (F1 score 0.71, sensitivity 77%, specificity 97%) and lowest for kidney disease (F1 score 0.25, sensitivity 16%, specificity 98%). Self‐reported hypertension (hazard ratio [HR], 1.49 [95% CI, 1.14–1.94]) and diabetes (HR, 1.58 [95% CI, 1.18–2.12]) were associated with stroke‐related mortality, comparable to measured risk factors. For dementia‐related mortality, only measured hearing impairment (all dementia cases had hearing impairment at baseline) and both self‐reported (HR, 0.50 [95% CI, 0.37–0.68]) and measured overweight (HR, 0.70 [95% CI, 0.52–0.93]) were associated. Conclusions In conclusion, the reliability and validity of self‐reported risk factors for stroke and dementia differ between risk factors. Although self‐reported measures vary in their reliability, they perform equally as well as objective metrics for evaluating the risk of stroke‐ and dementia‐related mortality.

Jean-Paul Henri Dedam, Matthew Fogel, Elizabeth Fogel

Olatunji Mumini Omisore, Toluwanimi Akinyemi, Anh Nguyen et al.

Robot-assisted catheterization has garnered a good attention for its potentials in treating cardiovascular diseases. However, advancing surgeon-robot collaboration still requires further research, particularly on task-specific automation. For instance, automated tool segmentation can assist surgeons in visualizing and tracking of endovascular tools during cardiac procedures. While learning-based models have demonstrated state-of-the-art segmentation performances, generating ground-truth labels for fully-supervised methods is both labor-intensive time consuming, and costly. In this study, we propose a weakly-supervised learning method with multi-lateral pseudo labeling for tool segmentation in cardiovascular angiogram datasets. The method utilizes a modified U-Net architecture featuring one encoder and multiple laterally branched decoders. The decoders generate diverse pseudo labels under different perturbations, augmenting available partial labels. The pseudo labels are self-generated using a mixed loss function with shared consistency across the decoders. The weakly-supervised model was trained end-to-end and validated using partially annotated angiogram data from three cardiovascular catheterization procedures. Validation results show that the model could perform closer to fully-supervised models. Also, the proposed weakly-supervised multi-lateral method outperforms three well known methods used for weakly-supervised learning, offering the highest segmentation performance across the three angiogram datasets. Furthermore, numerous ablation studies confirmed the model's consistent performance under different parameters. Finally, the model was applied for tool segmentation in a robot-assisted catheterization experiments. The model enhanced visualization with high connectivity indices for guidewire and catheter, and a mean processing time of 35 ms per frame.

María Teresa García-Ordás, Martín Bayón-Gutiérrez, Carmen Benavides et al.

Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

Guangkun Nie, Qinghao Zhao, Gongzheng Tang et al.

Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning. However, real-world age distributions are often imbalanced, posing significant challenges for deep learning models. In this paper, we introduce a novel, simple, and effective loss function named the Dist Loss to address deep imbalanced regression tasks. We trained a one-dimensional convolutional neural network (Net1D) incorporating the Dist Loss on the extensive UK Biobank dataset (n=502,389) to estimate vascular age from PPG signals and validate its efficacy in characterizing cardiovascular health. The model's performance was validated on a 40% held-out test set, achieving state-of-the-art results, especially in regions with small sample sizes. Furthermore, we divided the population into three subgroups based on the difference between predicted vascular age and chronological age: less than -10 years, between -10 and 10 years, and greater than 10 years. We analyzed the relationship between predicted vascular age and several cardiovascular events over a follow-up period of up to 10 years, including death, coronary heart disease, and heart failure. Our results indicate that the predicted vascular age has significant potential to reflect an individual's cardiovascular health status. Our code will be available at https://github.com/Ngk03/AI-vascular-age.

João Borges-Rosa, Pedro A. Sousa, Natália António et al.

Introduction and Objectives: Atrial fibrillation (AF) and heart failure (HF) often coexist. AF catheter ablation improves left ventricular ejection fraction (LVEF), but its impact varies between patients. We aimed to identify predictors of LVEF improvement in HF patients with impaired LVEF undergoing AF ablation. Methods: We conducted a retrospective single-center study in HF patients with LVEF <50% undergoing AF catheter ablation between May 2016 and May 2022. The primary endpoint was the LVEF recovery rate (‘responders’). Secondary endpoints were one-year safety and effectiveness. We also aimed to validate a prediction model for LVEF recovery. Results: The study included 100 patients (79% male, median age 60 years, 70% with probable tachycardia-induced cardiomyopathy [TIC], mean LVEF 37%, 29% with paroxysmal AF). After a median follow-up of 12 months after catheter ablation, LVEF improved significantly (36±10% vs. 53±10%, p<0.001), with an 82% responder rate. A suspected diagnosis of TIC (OR 4.916 [95% CI 1.166–20.732], p=0.030), shorter QRS duration (OR 0.969 [95% CI 0.945–0.994], p=0.015), and smaller left ventricle (OR 0.893 [95% CI 0.799–0.999], p=0.049) were independently associated with LVEF improvement. Freedom from any documented atrial arrhythmia was 86% (64% under antiarrhythmic drugs), and the rate of adverse events was 2%. The prediction model had a good discriminative performance (AUC 0.814 [95% CI 0.681–0.947]). Conclusion: In AF patients with HF and impaired LVEF, suspected TIC, shorter QRS duration, and smaller LV diameter were associated with LVEF recovery following AF catheter ablation. Resumo: Introdução e objetivos: A fibrilhação auricular (FA) e a insuficiência cardíaca (IC) frequentemente coexistem. A ablação de FA melhora a fração de ejeção do ventrículo esquerdo (FEVE), mas o impacto varia entre doentes. O nosso objetivo foi identificar preditores da melhoria da FEVE em doentes com IC e FEVE reduzida submetidos a ablação de FA. Métodos: Estudo retrospetivo unicêntrico de doentes com IC e FEVE < 50% submetidos a ablação de FA entre 05/2016 e 05/2022. O endpoint primário foi a avaliação da taxa de recuperação da FEVE (“Respondedores”). Os endpoints secundários centraram-se na segurança e eficácia a um ano. Também procurámos validar um modelo preditivo de recuperação da FEVE. Resultados: Foram incluídos 100 doentes (79% homens, idade média 60 anos, 70% com provável taquicardiomiopatia, FEVE média 37%, 29% com FA paroxística). Após um seguimento mediano de 12 meses, a FEVE melhorou significativamente (36 ± 10% versus 53 ± 10%, p < 0,001), com 82% de «Respondedores». Suspeita de taquicardiomiopatia (OR 4,916 [95% CI 1,166-20,732], p = 0,030), QRS de menor duração (OR 0,969 [0,945-0,994], p = 0,015) e VE de menor diâmetro (OR 0,893 [0,799-0,999], p = 0,049) associaram-se à melhoria da FEVE. A maioria dos doentes (86%) não apresentou recorrência de arritmia e a taxa de eventos adversos foi de 2%. O modelo preditivo demonstrou bom desempenho (AUC 0,814 [0,681-0,947], 95% CI). Conclusões: Em doentes com FA e IC com FEVE reduzida, a suspeita de taquicardiomiopatia, QRS de menor duração e menor diâmetro do VE associam-se à recuperação da FEVE após a ablação de FA.

Oday Salman, Lei Zhao, Jordana B. Cohen et al.

Background Kidney disease is common in heart failure with preserved ejection fraction (HFpEF). However, the biologic correlates and prognostic significance of kidney injury (KI), in HFpEF, beyond the estimated glomerular filtration rate (eGFR), are unclear. Methods and Results Using baseline plasma samples from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial, we measured the following KI biomarkers: cystatin‐C, fatty acid‐binding protein‐3, Beta‐2 microglobulin, neutrophil gelatinase‐associated lipocalin, and kidney‐injury molecule‐1. Factor analysis was used to extract the common variability underlying these biomarkers. We assessed the relationship between the KI‐factor score and the risk of death or HF‐related hospital admission in models adjusted for the Meta‐Analysis Global Group in Chronic Heart Failure risk score and eGFR. We also assessed the relationship between the KI factor score and ~5000 plasma proteins, followed by pathway analysis. We validated our findings among HFpEF participants in the Penn Heart Failure Study. KI was associated with the risk of death or HF‐related hospital admission independent of the Meta‐Analysis Global Group in Chronic Heart Failure risk score and eGFR. Both the risk score and eGFR were no longer associated with death or HF‐related hospital admission after adjusting for the KI factor score. KI was predominantly associated with proteins and biologic pathways related to complement activation, inflammation, fibrosis, and cholesterol homeostasis. KI was associated with 140 proteins, which reproduced across cohorts. Findings regarding biologic associations and the prognostic significance of KI were also reproduced in the validation cohort. Conclusions KI is associated with adverse outcomes in HFpEF independent of baseline eGFR. Patients with HFpEF and KI exhibit a plasma proteomic signature indicative of complement activation, inflammation, fibrosis, and impaired cholesterol homeostasis.

Ludovico Dulcetta, Paolo Marra, Riccardo Muglia et al.

Abstract Background Chronic total occlusion (CTO) of the portal vein is one of the main causes of portal hypertension, which may result in life-threatening complications often managed by interventional radiology (IR). The aim of this study is to report the innovative experience with percutaneous revascularization therapy in the management of portal vein CTO in paediatric and adult patients. Materials and methods From January 2020 to December 2023 consecutive paediatric and adult patients with severe portal hypertension resulting from portal vein CTO who underwent attempts at percutaneous recanalization were retrospectively reviewed. Technical aspects including the percutaneous approach, portal vein stenting, transjugular intrahepatic portosystemic shunt (TIPS) creation, varices embolization and clinical outcomes including adverse events and control of portal hypertension were analyzed. Technical success was defined as at least partial restoration of the portal vein patency at the final angiogram. Clinical success was defined as the improvement of clinical-laboratory signs of portal hypertension and control for variceal bleeding. Results Fifteen patients (median age = 21 years, range = 59 years; 10 males; 5 children) with portal vein CTO underwent a total of 25 percutaneous revascularization procedures. Nine patients (60%; 5 children, 4 adults) were liver transplant recipients. All patients except one had cavernous transformation of the extra-hepatic portal vein, involving the spleno-mesenteric confluence in 5 cases. Technical success was achieved in 13/15 (87%) patients of whom 8 had portal revascularization through the placement of an extra-hepatic stent; indeed, in six cases, a TIPS was performed to achieve sustained portal vein patency. Embolization of varices and/or cavernoma was performed in 12 patients. Adverse events occurred in 2/15 (splenic artery perforation and hemoperitoneum, one each) managed without sequelae. Technical success led to clinical success in all the 13/15 (87%) cases, with a median follow-up of 20 months (IQR 4–34 months). Conclusion CTO can be managed effectively by interventional radiology. Restored portal flow physiology alone is possible in most patients, while TIPS may be required in a small proportion of them, to prolong portal vein patency and control portal hypertension.

E. A. Zakharyan, Iuliana I. Shramko, Ani M. Arzumanyan et al.

Introduction. Coronary heart disease is a common pathology, playing a key role in cardiovascular mortality. Damage of circulatory system affects people of all ages, including the active population, which provides an adverse impact on the socio-economic state of society. In this research, we analyzed the processes of cardiomyocyte apoptosis and their effects on myocardial ischemic damage. Aim was to identify the role of cardiomyocyte apoptosis in the development of coronary heart disease. Materials and Methods. A meta-analysis of 50 literary sources was performed in the PubMed database over the past 11 years. Results and Discussion. Meta-analysis has demonstrated that prevention of cardiomyocyte apoptosis is an extremely important therapeutic goal today. Ischemia/reperfusion, exercise- induced hypertrophy, and post-infarct myocardial remodeling are associated with myocyte apoptosis. This indicates that current treatments proven effective in these diseases may interfere with apoptosis. Conclusions. Studying apoptosis and its regulation is important for both understanding the pathogenesis of cardiovascular diseases and developing new therapeutic approaches. Apoptosis, i. e., controlled cell death, is of great importance in cardiac pathology. For instance, it plays a key role in ischemia/reperfusion, myocardial hypertrophy, and post-infarct remodeling. Key words: ischemic heart disease, apoptosis, cardiomyocytes.

S. Prajapat, Krushna Ch. Maharana, Sanjiv Singh

S. Mohd. Shiraz Rizvi, Sini Sunny, Irshad A. Wani et al.

Assessing regional wall motion abnormalities (RWMA) in the myocardium may provide early diagnosis and treat chronic remodeling in STEMI patients. We assessed RWMA in 217 subjects with anterior STEMI admitted to Era University Hospital in Lucknow, UP, India. Besides abnormalities in the LAD territory, sub-sets of patients exhibited diffuse regional myocardial dysfunction. Interestingly, variations in serum electrolytes, specifically sodium and potassium, significantly affected the distribution and frequency of RWMA. Notably, RWMA occurred in the basal septum, apical septum, apex, and lateral wall in the anterior STEMI group. Additionally, the rate of regional dysfunction varied with serum urea and creatinine levels. This suggests that anterior STEMI can manifest myocardial abnormalities beyond the LAD territory. These findings indicate that ST-segment elevation might not be specific, possibly influenced by electrolyte changes affecting cardiac rhythm. Therefore, diagnosing and correcting region-specific wall motion abnormalities and electrolyte imbalances may improve outcomes in STEMI patients.

Hiroki Yagi, MD, PhD, Eisuke Amiya, MD, PhD, Masae Uehara, MD, PhD et al.

Background: IgG4-related disease (IgG4-RD) is a systemic disease characterized by serum IgG4 upregulation, massive infiltration of IgG4-positive plasma cells, and storiform fibrosis, which results in nodules or thickening of the involved organs. Cardiologists have recently recognized that IgG4-RD can be complicated by coronary artery events (CAEs); however, the mechanisms and clinical characteristics of this phenomenon are unknown. We evaluated the clinical signs of patients with coronary periarteritis (CP), aortic periarteritis (AP), and pericardial thickening, which are complications of IgG4-RD, to determine the contributing factors. Methods: We retrospectively examined 19 patients with IgG4-RD who attended or consulted a cardiologist in our department at the University of Tokyo Hospital between January 1, 2004, and December 31, 2021. Results: The frequency of CAEs was significantly higher in the CP group than in the non-CP group. Furthermore, the CP group had significantly lower event-free survival than the non-CP group (log-rank test, P = 0.008). However, the frequency of incidents and event-free survival for CAEs after IgG4-RD diagnosis did not differ significantly between the AP and non-AP groups. Although no statistically significant difference was present between the frequency of CAEs for those with vs without pericardial thickening, the group with pericardial thickening had significantly worse event-free survival than the group without pericardial thickening (log-rank test, P = 0.017). Conclusions: The incidence and clinical course of CAEs complicated by IgG4-RD could be predicted by identifying CP and pericardial thickening in IgG4-RD but not AP. Résumé: Contexte: La maladie liée aux immunoglobulines de type G4 (ML-IgG4) est une maladie généralisée caractérisée par une augmentation du taux sérique d’IgG4, par une infiltration massive de plasmocytes exprimant les IgG4 et par une fibrose storiforme, qui produit des nodules ou un épaississement des organes touchés. Les cardiologues ont récemment reconnu que la ML-IgG4 peut être compliquée par des événements coronariens; les mécanismes et caractéristiques cliniques de ce phénomène demeurent cependant inconnus. Nous avons évalué les signes cliniques chez des patients atteints de périartérite coronarienne (PC), de périaortite (PA) et d’épaississement du péricarde, des complications de la ML-IgG4, pour tenter d’établir les facteurs contributifs. Méthodologie: Nous avons examiné de manière rétrospective les dossiers de 19 patients atteints de ML-IgG4 qui ont été admis à notre service de l’Hôpital de l’Université de Tokyo ou qui ont consulté un cardiologue du service entre le 1er janvier 2004 et le 31 décembre 2021. Résultats: La fréquence des événements coronariens était significativement plus élevée dans le groupe PC que dans les autres groupes. Par ailleurs, le groupe PC avait une survie sans événement significativement plus courte que les autres groupes (test logarithmique par rangs; p = 0,008). En outre, la fréquence des événements coronariens et la survie sans événement coronarien après un diagnostic de ML-IgG4 ne variaient pas de manière significative entre le groupe PA et les autres groupes. Bien qu’aucune différence statistiquement significative n’ait été constatée quant à la fréquence des événements coronariens entre les patients présentant un épaississement du péricarde et les autres patients, le premier groupe affichait une survie sans événement significativement plus courte que l’autre (test logarithmique par rangs; p = 0,017). Conclusions: L’incidence et le déroulement clinique des événements coronariens compliqués par la ML-IgG4 pouvaient être anticipés dans les cas de ML-IgG4 en présence de PC et d’un épaississement du péricarde, mais pas de PA.

F.-X. Herion, A. Beurton, C. Oddos et al.

G. Soulat, P. McCarthy, M. Markl

Magnetic resonance imaging (MRI) has become an important tool for the clinical evaluation of patients with cardiac and vascular diseases. Since its introduction in the late 1980s, quantitative flow imaging with MRI has become a routine part of standard-of-care cardiothoracic and vascular MRI for the assessment of pathological changes in blood flow in patients with cardiovascular disease. More recently, time-resolved flow imaging with velocity encoding along all three flow directions and three-dimensional (3D) anatomic coverage (4D flow MRI) has been developed and applied to enable comprehensive 3D visualization and quantification of hemodynamics throughout the human circulatory system. This article provides an overview of the use of 4D flow applications in different cardiac and vascular regions in the human circulatory system, with a focus on using 4D flow MRI in cardiothoracic and cerebrovascular diseases. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 22 is June 4, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.