Advanced Imaging Modalities on Cardiovascular Diseases: Diagnosis and Management

Abstract

Background: Advanced cardiac imaging modalities, such as coronary computed tomography angiography (CCTA), cardiac positron emission tomography (PET)/CT, and cardiac magnetic resonance imaging (MRI), are at the forefront of diagnosing cardiovascular diseases (CVDs). These technologies offer superior imaging quality and insights into heart anatomy and physiology, though their integration into clinical practice remains slow compared to traditional methods like 2D echocardiography and Single Photon Emission Computed Tomography (SPECT). This gap is primarily due to the complexity and costs associated with new imaging techniques.

Aim: The aim of this review is to evaluate the impact of advanced imaging modalities on the diagnosis and management of cardiovascular diseases, focusing on innovations such as CCTA, PET/CT, and their integration with machine learning and artificial intelligence (AI) to improve diagnostic accuracy and patient outcomes.

Methods: The review synthesizes findings from recent studies on advanced cardiac imaging, highlighting their technological advancements, clinical utility, and comparison to traditional diagnostic methods. Key modalities explored include CCTA, cardiac PET/CT, and their role in coronary artery disease (CAD) management, along with the role of machine learning in enhancing diagnostic accuracy.

Results: Advanced imaging technologies, especially CCTA and PET/CT, offer high diagnostic sensitivity and specificity, significantly improving patient outcomes in CVD management. Innovations such as CT-derived fractional flow reserve (FFR) and AI-based image analysis further enhance diagnostic precision, enabling more accurate risk stratification and treatment decisions. Cardiac PET/CT, despite being less common, has shown superior performance in detecting coronary artery blockages and offers reduced radiation exposure compared to SPECT.

Conclusion: Advanced imaging modalities like CCTA and PET/CT are transforming the diagnosis and management of cardiovascular diseases, providing more precise, non-invasive alternatives to traditional methods. Their integration with AI and machine learning will likely lead to improved risk prediction and personalized treatment plans, contributing to better patient outcomes and reducing healthcare costs.