Articles tagged with Cardiac Imaging
Researchers have developed compact diamond magnetometers that measure biomagnetic signals with high precision, opening new avenues for medical diagnostics. The technology offers improved paths towards early detection of conditions such as myocarditis and epilepsy.
Researchers developed V-UNet, a novel model combining global and local information to address noise and redundant information in medical images. The model achieved competitive segmentation performance while maintaining low computational requirements, promising a more efficient and robust AI-assisted medical image analysis.
Columbia researchers have developed a new technique to capture electrical activity and mechanical motion within the heart using cardiac ultrasound images. This technology allows for early detection and risk assessment of Mitral Valve Disease and arrhythmias, transforming care for those suffering from these conditions.
A new imaging system links heart structure and electrical activity across the entire organ, revealing how scar tissue interferes with heartbeat. The technology identifies different tissue types based on light interaction and tracks electrical signals in real-time.
A Tulane University team is using AI to discover new superconductors, which could improve the nation's electrical grid, medical imaging, and quantum computing. The project combines high-fidelity calculations, physics-aware AI, and experimental measurements to accelerate discovery.
AI model improves cardiac MRI interpretation, while immersive VR simulators aid poststroke rehabilitation. Meanwhile, menopause tracking apps offer empowerment but also pose risks due to inaccurate information and targeted marketing.
Researchers found that long-term exposure to air pollution is associated with more advanced coronary artery disease, even at moderate levels. The study analyzed data from 11,128 adults and found that exposure to fine particulate matter and nitrogen dioxide increased the risk of coronary artery disease.
A simple, seven-second X-ray scan can accurately detect the severity of pulmonary valve regurgitation in patients with repaired Tetralogy of Fallot. The test uses dynamic chest radiography and requires minimal radiation, offering a more accessible diagnostic option for those who cannot undergo traditional methods.
Researchers developed an AI-based score from standard ECGs that reflect biological development on a spectrum rather than in fixed categories. The Electrocardiographic Sex Index (ESI) captures step-by-step changes of normal growth and hormonal changes, offering a more precise way to account for developmental stage.
Researchers developed a new cardiac MRI-based measurement, eRVEF, to assess tricuspid regurgitation and improve mortality risk prediction. The study found that eRVEF predicted adverse outcomes better than traditional measures.
Researchers developed an AI system called CMR-CLIP that can interpret complex heart scans without manual labels, outperforming existing models by up to 35%. The system learned from over a million images and hundreds of thousands of motion sequences collected from Cleveland Clinic.
The study found that AI-enhanced single-shot cine MRI produces better image quality compared to conventional cine MRI, particularly in participants with arrhythmia. The technique demonstrated a 100% success rate for image acquisition, outperforming conventional cine sequences.
Researchers developed a new MRI technique that simultaneously displays heart tissue and blood flow, enabling precise planning for surgical repairs. This innovation provides high-quality flow images without radiation, making it essential for repeated imaging in children.
Researchers have developed a non-invasive method to estimate blood oxygen levels in heart failure patients using standard cardiac MRI. This breakthrough could spare thousands from undergoing risky tube procedures, allowing for safer and more frequent monitoring.
A landmark study analyzed health data from over 600,000 patients across 10 countries to assess patient risk for non-ST-elevation acute coronary syndrome (NSTE-ACS). The AI-powered model GRACE 3.0 predicts risk more accurately and guides personalized treatment decisions.
MetaSeg achieves the same segmentation performance as U-Nets but requires 90% fewer parameters, making medical image segmentation more cost-effective. The new approach leverages implicit neural representations to quickly adjust to new images and decode accurate labels.
Researchers at Linköping University used magnetic cameras to examine blood flow in an artificial heart in real-time, revealing a pulsing pattern similar to that of a healthy heart. The study aims to design the heart to minimize complications such as blood clots and red blood cells breakdown.
The Society for Cardiovascular Angiography and Interventions (SCAI) has published evidence-based clinical practice guidelines for the management of chronic venous disease. The guidelines provide patient-centered recommendations on therapeutic options, including compression therapy, wound care, and minimally invasive procedures. Key fin...
A newly developed cardiac PET imaging technique offers a simpler way to detect significant coronary artery disease by eliminating the need for complex scanning protocols. The technique has been shown to accurately estimate myocardial flow reserve during both exercise and pharmacologic stress, making advanced heart imaging more accessible.
Researchers developed a simpler way to identify patients with suspected cardiac sarcoidosis at increased risk of sudden cardiac death. CMR phenotyping can help determine which patients would benefit from an implantable cardioverter-defibrillator for primary prevention, improving decision-making in clinical practice.
A novel screening approach called VEST uses virtual echocardiography to identify patients at high risk of pulmonary arterial hypertension, a life-threatening form of heart failure. The tool has been shown to generate accurate PAH risk scores without manual calculations and can guide timely referrals for expert care.
The SCAI/SCCT expert opinion document presents a comprehensive framework for using CCTA to guide PCI. It outlines current evidence and future opportunities, providing practical guidance on image acquisition, interpretation, and procedural strategy. The document aims to bridge the gap between imaging and intervention, emphasizing educat...
Researchers at Mayo Clinic developed AI-ECG tools that can detect heart muscle weakness in women of childbearing age, allowing for earlier identification and management. The tools demonstrated high diagnostic performance, with an area under the curve of .94 for AI-ECG and .98 for AI digital stethoscope.
Researchers found larger falcine sinus diameters associated with higher risks of developmental delays, neurological issues and mortality in infants born with VOGM. Fetal MRI can help predict short- and intermediate-term outcomes, enabling early intervention and better care for high-risk patients.
A new imaging technology has been developed that combines super-resolution imaging with artificial intelligence to reveal subcellular structures and dynamics in living cells. This breakthrough enables scientists to better understand the root causes of diseases, leading to improved treatments.
Dr. David Winchester will lead the American College of Cardiology's Board of Governors, guiding chapters representing all 50 states and promoting heart health improvement in communities. His term aims to address practice challenges through advocacy and chapter support.
A new method combines ECGI with digital twins to locate the origin of premature ventricular contractions, improving accuracy by an average of 7.8 mm. The method has been applied in a real clinical case and is expected to facilitate planning interventions and reduce treatment costs.
Dr. Christopher Kramer began a one-year term as head of the American College of Cardiology, addressing workforce issues, health equity and AI-driven solutions to improve cardiovascular care. With over 35 years of membership and leadership roles, Kramer brings experience in cardiovascular magnetic resonance imaging.
Researchers at Waseda University developed a new technique using terahertz imaging to visualize the internal structure of the mouse cochlea with high resolution. The study successfully demonstrated the potential of THz imaging as a non-invasive diagnostic tool for auditory disorders and other medical applications.
Researchers at Waseda University develop a new imaging technique that uses neutron activation to transform gold nanoparticles into radioisotopes, enabling long-term tracking of their movement in the body. This breakthrough could lead to more effective cancer treatments and precision monitoring of drug distribution.
Researchers are enrolling volunteers for a four-year study that will use advanced imaging, genetics, exercise science, neuroscience, and remote monitoring to investigate age-related health decline. The goal is to help individuals and healthcare practitioners better prevent the impact of disease on older adults.
UTA research projects contributed $59 million to the national economy in 2024, supporting student development and collaboration with other research organizations. The university's research infrastructure, including cutting-edge equipment, helped drive economic impact in North Texas and beyond.
A study published in Mayo Clinic Proceedings found that patients who can exercise during cardiac stress testing have a lower mortality rate than those who cannot. The researchers discovered that assessing a patient's ability to exercise during testing provides a stronger distinction between high- and low-risk patients.
The University of Texas MD Anderson Cancer Center received nearly $23 million in CPRIT funding to advance cancer research, translational science, and clinical trials. The funding will support the recruitment of a first-time tenure-track faculty member and enhance the understanding of cancer biology.
A new deep learning technique called CTLESS enhances myocardial perfusion imaging accuracy without requiring additional radiation scans. This method leverages deep learning to estimate attenuation maps, improving diagnostic interpretation and potentially boosting technological health equality across the U.S. and worldwide.
The ACC Advancing the Cardiovascular Care of the Oncology Patient conference will provide clinicians with tools to improve cardiovascular care of cancer patients. Key sessions will include discussions on pulmonary tumor thrombotic microangiopathy, AI and technology in cardio-oncology.
A recent study introduces an innovative method for analyzing body composition, providing accurate assessments of body fat and muscle distribution. The approach utilizes deep, nonlinear methods to enhance estimation accuracy, surpassing previous linear models.
The open-source AI model analyzes medical images, generates detailed reports, and answers clinical questions to streamline diagnostics and improve accuracy. BiomedGPT aims to democratize healthcare and reduce disparities amongst patients by providing easily accessible data to bolster underserved hospitals.
Researchers found AI-based imaging technology improves disease diagnosis accuracy, particularly in cardiology, oncology, neurology, and ophthalmology. The technology also enhances diagnostic efficiency and reduces healthcare disparities by delivering high-quality diagnostics to underserved areas.
Researchers developed a non-invasive way to assess fat composition around the heart using magnetic resonance imaging. This technique may help doctors identify patients at greatest risk for cardiac problems and predict treatment outcomes.
Lanza joins 170 inventors from around the world who have generated over 20,000 licensed technologies and hold more than 2,000 patents. His nanoparticle-based innovations, including targeted PFC nanoparticles, are being used to detect blood clots and treat breast cancers.
Fogarty's research aims to monitor language function and recovery in post-stroke patients using DOT. She hopes to establish the feasibility of brain-computer interfaces to restore inter-personal communication for post-stroke patients.
Researchers have developed a new coronary risk score specifically for women, accurately predicting and categorizing the risk of major adverse cardiovascular events. The findings suggest that this novel approach can help identify high-risk women earlier, reducing the risk of heart attacks and sudden cardiac death.
TU Graz researcher Gerhard A. Holzapfel leads a six-year project to develop AI-based methods for analyzing soft tissue mechanical properties using transcriptomics and microstructure imaging. The team aims to improve disease diagnosis and therapy in clinical practice.
The new center will advance multiple areas of active research on campus, including neurocognitive sciences and musculo-skeletal health. With support from across campus, UTA aims to improve research capabilities to enhance undergraduate and graduate educational opportunities.
Researchers at the University of Ottawa have created a new type of contrast agent using gold nanoparticles to improve doctors' ability to diagnose heart conditions. The new agent, AuSC@(13FS)2, showed strong binding to P-selectin and improved IV-OCT imaging in rats with inflamed blood vessels.
A $3.7 million NIH grant supports a study to improve PE diagnosis and treatment by using advanced imaging techniques, which may measure the effectiveness of clot-dissolving therapies. The goal is to help clinicians better diagnose and treat patients with PE, a devastating cardiovascular ailment.
Researchers at TU Graz have developed a new machine learning method that generates precise live MRI images of the beating heart using only a few MRI measurement data. This breakthrough enables faster and cheaper MRI applications, including quantitative MRI for diagnoses.
A new study published in the Journal of the American College of Cardiology found that subclinical atherosclerosis is independently associated with the risk of dying from any cause. The study also showed that monitoring the progression of atherosclerosis can improve the prediction and prevention of death from any cause.
Researchers developed a noninvasive method to monitor postprandial cardiovascular health using spatial frequency domain imaging (SFDI). The technique effectively tracks diet-induced changes in cardiovascular physiology, revealing significant differences in tissue responses after consuming high-fat and low-fat meals.
Researchers at Soochow University introduced coherence entropy as a global characterization of light fields subjected to random fluctuations. Coherence entropy remains stable during the propagation of light through complex media, making it a robust indicator of light field behavior in non-ideal conditions.
A study found that CT scans can identify individuals at high risk of type 2 diabetes through automated analysis of various body components. The index of visceral fat showed the highest predictive performance for diabetes.
Researchers developed an AI model to analyze heart MRI scans, which can save NHS time and resources. The model provides a complete analysis of the entire heart using a view that shows all four chambers, leading to faster and more accurate diagnosis of heart failure and other cardiac conditions.
Researchers compared human hearts with those of great apes, discovering a more compact muscle structure in humans, related to greater cardiac function. This finding supports the hypothesis that human heart evolved to meet higher demands of human physiology, such as larger brain size and physical activity.
A study by Linköping University researchers found that disturbed blood flow can cause inflammation and breakdown of the vessel wall in cases of aortic dilation. This discovery could lead to better diagnosis and treatment options for patients at risk of serious complications.
Researchers developed a non-invasive imaging technique to visualize cardiac micro-vessels in high resolution, enabling better understanding of cardiovascular diseases. The study improves diagnosis and treatment of conditions like microvascular coronary disease and cardiomyopathies.
Jeffrey W. Moses, MD, is recognized for advancing the field of interventional cardiovascular medicine through technical excellence and leadership. Dr. Moses has performed over 20,000 interventional procedures and made significant contributions to clinical research and educational activities in interventional vascular therapy.
A new approach to heart bypass surgery has been successfully tested, using non-invasive cardiac-CT scan images and AI-powered blood flow analysis. The trial showed a 99.1% feasibility rate, indicating that the procedure is safe and effective without invasive diagnostic catheterisation.
A new AI-based video biomarker is associated with aortic stenosis development and progression, allowing for opportunistic risk stratification across various imaging modalities. The study's findings suggest potential applications on handheld devices for early detection and intervention.
The T2oFu method offers a new approach to quantitative phase and polarization-sensitive tomography, enabling high-contrast images of muscle fibers with implications for diagnosing skeletal myopathies. The technique has been successfully tested on heart tissue samples with cardiac amyloidosis, providing promising results.