Cardiovascular disease (CVD) is the leading cause of death in the western world and is becoming more important in the developing world. Recently, advances in monitoring, revascularisation and pharmacotherapy have resulted in a reduction in mortality. However, although mortality rates have declined, the burden of disease remains large resulting in high direct and indirect healthcare costs related to CVDs. In Australia, acute coronary syndrome (ACS) accounts for more than 300000 years of life lost due to premature death and a total cost exceeding eight billion dollars annually. It is also the main contributor towards the discrepancy in life expectancy between indigenous and non-indigenous Australians. The high prevalence of CVD along with its associated cost urgently requires a reliable but non-invasive and cost-effective imaging modality. The imaging modality of choice should be able to accelerate the diagnosis of ACS, aid in the risk stratification of de novo coronary artery disease and avail incremental information of prognostic value such as viability which cardiovascular magnetic resonance (CMR) allows. Despite its manifold benefits, there are limitations to its wider use in routine clinical assessment and more studies are required into assessing its cost-effectiveness. It is hoped that with greater development in the technology and imaging protocols, CMR could be made less cumbersome, its imaging protocols less lengthy, the technology more inexpensive and easily applied in routine clinical practice.
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http://dx.doi.org/10.4330/wjc.v6.i6.405 | DOI Listing |
Chin Med J (Engl)
January 2025
Department of Magnetic Resonance Imaging, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Radiol Clin North Am
March 2025
Radiology Department, Northwestern University Feinberg School of Medicine, Arkes Pavilion, 676 North St Clair Street, Suite 800, Chicago, IL 60611, USA. Electronic address:
Cardiac MR imaging and pulmonary MR angiography (MRA) are important clinical tools for the assessment of pulmonary vascular diseases. There are evolving noncontrast and contrast-enhanced techniques to evaluate pulmonary vasculature. Pulmonary MRA is a feasible imaging alternative to CTA in pulmonary embolism detection.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
February 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine - Nanyang Technological University, Singapore. Electronic address:
Background: Cardiovascular risk factors (CRFs) like hypertension, high cholesterol, and diabetes mellitus are increasingly linked to cognitive decline and dementia, especially in cerebral small vessel disease (cSVD). White matter hyperintensities (WMH) are closely associated with cognitive impairment, but the mechanisms behind their development remain unclear. Blood-brain barrier (BBB) dysfunction may be a key factor, particularly in cSVD.
View Article and Find Full Text PDFJ Thorac Cardiovasc Surg
January 2025
Division of Cardiology, The Hospital for Sick Children, Toronto, ON, Canada; Center for Image Guided Innovation and Therapeutic Intervention, The Hospital for Sick Children, Toronto, ON, Canada.
Objectives: Mixed reality (MixR) is an innovative visualization tool that presents virtual elements in a real-world environment, enabling real-time interaction between the user and the combined digital/physical reality. We aimed to explore the feasibility of MixR in enhancing preoperative planning and intraoperative guidance for the correction of various complex congenital heart defects (CHDs).
Methods: Patients underwent cardiac computed tomography or cardiac magnetic resonance and segmentation of digital imaging and communications in medicine (DICOM) images was performed.
J Clin Med
January 2025
Guthrie Cortland Medical Center, Cortland, NY 13045, USA.
Artificial intelligence (AI) in echocardiography represents a transformative advancement in cardiology, addressing longstanding challenges in cardiac diagnostics. Echocardiography has traditionally been limited by operator-dependent variability and subjective interpretation, which impact diagnostic reliability. This study evaluates the role of AI, particularly machine learning (ML), in enhancing the accuracy and consistency of echocardiographic image analysis and its potential to complement clinical expertise.
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