A 2024 study published in European Radiology that validates a novel stepwise strategy integrating CCTA, Deep Learning FFRCT, and CT-MPI to improve diagnostic accuracy for stable CAD.

Lyu, L., Pan, J., Li, D. et al. A stepwise strategy integrating dynamic stress CT myocardial perfusion and deep learning–based FFRCT in the work-up of stable coronary artery disease. Eur Radiol (2024).


Accurate diagnostic tests are essential for identifying patients who need revascularization. Fractional flow reserve (FFR) is the gold standard for this, but its invasive nature and high costs limit its use [1, 2, 3, 4]. Noninvasive alternatives, such as coronary computed tomography angiography (CCTA), have high negative predictive value but cannot assess the physiological significance of coronary artery stenosis [5–11]. To overcome this, computed tomography–derived fractional flow reserve (FFRCT) and computed tomography myocardial perfusion imaging (CT-MPI) have been developed. Despite their potential, handling FFRCT gray zone values (around 0.80) remains challenging [12–25]. This blog post discusses a recent study from European Radiology (2024) that validates a novel stepwise strategy integrating CCTA, FFRCT, and CT-MPI to improve diagnostic accuracy for stable CAD.

Key Insights:

  • Stepwise CCTA + FFRCT + CT-MPI Strategy: This approach holds promise as a viable method to reduce the need for invasive diagnostic catheterization while maintaining high agreement with ICA/FFR.
  • Superior Performance: The strategy performed better than both CCTA + FFRCT and CCTA + CT-MPI strategies.
  • Reduced Invasiveness: It minimizes unnecessary invasive diagnostic catheterization and helps clinicians make more confident decisions about referral or deferral for ICA/FFR.

Clinical Impact:

This novel stepwise strategy facilitates greater confidence and accuracy when clinicians decide on interventional coronary angiography (ICA) referral or deferral, reducing the burden of invasive investigations on patients.



To validate a stepwise strategy using FFRCT for intermediate stenosis on CCTA and CT-MPI for vessels with gray zone FFRCT values.

Materials and Methods:

This retrospective, single-center, prognostic study included 87 patients (58 ± 10 years; 70% male) who underwent CCTA, dynamic CT-MPI, interventional coronary angiography (ICA), and FFR. FFRCT were calculated using commercially available machine learning-based FFRct software (DEEPVESSEL FFR, Keya Medical). The diagnostic performance of three strategies (CCTA + FFRCT + CT-MPI, CCTA + FFRCT, and CCTA + CT-MPI) was evaluated using ICA/FFR as the reference. The net reclassification index (NRI) was calculated to compare models.

Details of the FFRCT algorithm are provided in the Supplemental Methods.


The CCTA + FFRCT + CT-MPI strategy resulted in the lowest proportion of vessels needing additional ICA/FFR measurement compared to the CCTA + FFRCT and CCTA + CT-MPI strategies (12%, 22%, and 24% respectively). It exhibited the highest accuracy for ruling out (91%, 84%, and 85%) and ruling in (90%, 85%, and 85%) functionally significant lesions. All strategies had comparable sensitivity for ruling out and specificity for ruling in significant lesions (p > 0.05). The NRI indicated that the CCTA + FFRCT + CT-MPI strategy outperformed both the CCTA + FFRCT (NRI = 0.238, p < 0.001) and the CCTA + CT-MPI strategies (NRI = 0.233, p < 0.001).


The CCTA + FFRCT + CT-MPI stepwise strategy was superior to the CCTA + FFRCT and CCTA + CT-MPI strategies by minimizing unnecessary invasive diagnostic catheterization without compromising agreement with ICA/FFR.


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DEEPVESSEL FFR is a software medical device that uses deep learning technology to perform a non-invasive physiological functional assessment of the coronary arteries using coronary computed tomography angiography (CCTA). The software processes coronary CTA images semi-automatically, generates a three-dimensional model of the coronary artery tree, and estimates CT FFR values. DEEPVESSEL FFR is FDA-Cleared, CE-Marked, and NMPA-Cleared.

About Keya Medical

Keya Medical is an international medical technology company developing deep learning-based medical devices for disease diagnosis and treatment. The company is committed to creating solutions that deliver clinical value at all stages in the patient care process, covering specialties including cardiology, neurology, pulmonology, pathology, and surgery. Keya Medical has four centers of excellence in Beijing, Shanghai, Shenzhen, and Seattle. Follow us on LinkedIn, Twitter, and Facebook.

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