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Related Concept Videos

Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Diagnosing acute coronary syndrome or ACS begins with a thorough patient history. Notable symptoms include central, crushing chest pain radiating to the left arm, neck, jaw, or back, along with shortness of breath, sweating (diaphoresis), nausea, vomiting, dizziness, and palpitations.It is crucial to note any history of cardiac illnesses and assess risk factors, including age, gender, smoking, hypertension, diabetes, hyperlipidemia, and a sedentary lifestyle.During physical examination, vital...
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Related Experiment Video

Updated: Jan 13, 2026

Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
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Novel deep learning CCTA-FFR for detecting functionally significant coronary stenosis: Comparison with iFR.

Mona P Roshan1, Grayson V Gigliotti1, Jeffrey Gonzalez1

  • 1Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA.

Journal of Cardiovascular Computed Tomography
|January 10, 2026
PubMed
Summary

A novel deep learning algorithm for coronary CT angiography-derived fractional flow reserve (CT-FFR) shows high accuracy in identifying lesion-specific ischemia. This noninvasive CT-FFR approach offers significant diagnostic improvement over traditional CT angiography alone.

Keywords:
Coronary CT Angiography (CCTA)Deep learning algorithmFractional flow reserve derived from CT (FFRCT)Instantaneous wave-free ratio (iFR)Lesion-specific ischemianon-invasive functional assessment

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Noninvasive Cardiology

Background:

  • Deep learning-based fractional flow reserve (CT-FFR) from coronary CT angiography (CCTA) allows noninvasive assessment of lesion-specific ischemia.
  • Onsite CT-FFR systems offer near-real-time physiologic evaluation, potentially reducing invasive procedures.

Purpose of the Study:

  • To evaluate the diagnostic performance of a novel onsite deep learning CT-FFR algorithm.
  • To compare the CT-FFR algorithm against invasive instantaneous wave-free ratio (iFR).

Main Methods:

  • Retrospective analysis of 44 patients (44 lesions) with CCTA and invasive iFR.
  • CT-FFR values generated using an onsite deep learning algorithm (cFFR v6) distal to stenoses.
  • Diagnostic performance metrics calculated and compared with CCTA stenosis thresholds and iFR.

Main Results:

  • CT-FFR demonstrated high accuracy (81.8%) with an AUC of 0.79, showing good agreement with iFR.
  • The algorithm maintained favorable performance in moderate (AUC 0.73) and severe (AUC 0.84) stenoses.
  • CT-FFR significantly improved sensitivity, specificity, and AUC compared to CCTA alone.

Conclusions:

  • The onsite deep learning CT-FFR algorithm shows good diagnostic agreement with invasive iFR.
  • It provides incremental diagnostic value over CCTA alone and is feasible for rapid workstation integration.
  • Larger multicenter studies are needed to confirm clinical utility and role.