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Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging
Published on: December 9, 2021
Updates on Imaging Modalities for the Diagnosis of Aortic Stenosis
Hadi Itani1, Mohamad B Moumneh1, Ahmed Zayed1
1From the Department of Internal Medicine.
Insights
Accurate diagnosis of aortic stenosis (AS) requires advanced imaging. Integrating echocardiography, CT, CMR, and new technologies improves risk stratification and personalized treatment for this common elderly heart valve disease.
Area of Science:
- Cardiology
- Medical Imaging
- Valvular Heart Disease
Background:
- Aortic stenosis (AS) is a prevalent degenerative valvular disease in the elderly, associated with significant morbidity and mortality.
- Accurate diagnosis and risk stratification are crucial for managing AS effectively.
- Transthoracic echocardiography, the standard tool, faces limitations in grading AS severity due to flow-dependent parameters, particularly in specific AS subtypes.
Purpose of the Study:
- To review advanced imaging modalities for diagnosing and assessing aortic stenosis.
- To highlight the role of integrated imaging approaches in improving diagnostic accuracy and patient management.
- To discuss emerging technologies that enhance the evaluation of AS severity and prognosis.
Main Methods:
- Review of advanced echocardiographic techniques (3D imaging, stress echo, Doppler indices).
- Evaluation of computed tomography (CT) for flow-independent assessment and anatomical detail.
- Inclusion of cardiac magnetic resonance (CMR) for prognostic information on myocardial remodeling and fibrosis.
- Consideration of emerging technologies like positron emission tomography (PET) and artificial intelligence (AI).
Main Results:
- Advanced echocardiography improves AS evaluation and clinical decision-making.
- CT offers flow-independent assessment and detailed anatomy crucial for procedural planning (e.g., TAVR).
- CMR provides prognostic insights into myocardial changes related to AS outcomes.
- Emerging PET and AI tools expand diagnostic capabilities for AS detection and classification.
Conclusions:
- Integrating multiple imaging modalities (echocardiography, CT, CMR, PET, AI) addresses diagnostic uncertainty in AS.
- A comprehensive approach refines AS subtype classification.
- Personalized intervention strategies can be informed by integrated diagnostic findings.
Abstract:
Aortic stenosis (AS) is the most common degenerative valvular disease in elderly patients and is linked to high morbidity and mortality. Accurate diagnosis and risk stratification are critical for effective management. Transthoracic echocardiography is the standard diagnostic tool, but its reliance on flow-dependent parameters can lead to inconsistent grading, especially in low-flow, low-gradient, or normal-flow, low-gradient AS. Advanced echocardiographic methods, such as 3D imaging, stress echocardiography, and Doppler indices, such as the mean gradient-to-effective orifice area ratio, improve the evaluation of AS severity and assist in clinical decision-making. Computed tomography provides a flow-independent evaluation of AS. It uses noncontrast calcium scoring with sex-specific thresholds, along with contrast-enhanced angiography, for detailed anatomical assessment. These modalities are essential for procedural planning, particularly for transcatheter aortic valve replacement. Cardiac magnetic resonance (CMR) provides additional prognostic information. It quantifies myocardial remodeling and fibrosis, which are associated with outcomes and recovery potential. Emerging technologies are expanding diagnostic capabilities in AS. Examples include 18F-sodium fluoride positron emission tomography for detecting microcalcification, artificial intelligence-based ECG and echocardiography for early diagnosis, and 4D flow CMR. Integration of echocardiography, computed tomography, CMR, and emerging positron emission tomography and artificial intelligence-based approaches can help address diagnostic uncertainty. This integration helps refine AS subtype classification and inform individualized intervention strategies.
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