Automated coronary analysis in ultrahigh-spatial resolution photon-counting detector CT angiography: Clinical validation and intra-individual comparison with energy-integrating detector CT.
Authors
Affiliations (12)
Affiliations (12)
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany; Quantitative Imaging Laboratory Bonn (QILaB), Bonn, Germany.
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Freiburg, Germany.
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA. Electronic address: [email protected].
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA.
- Computed Tomography, Siemens Healthineers, Forchheim, Germany.
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Siemens Medical Solutions, Malvern, PA, USA.
- Siemens Healthineers, Princeton, NJ, USA.
- Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Italy.
- Department of Cardiology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany.
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany; German Center for Cardiovascular Research (DZHK), Partner-Site Rhine-Main, Mainz, Germany.
- Department of Radiology and Radiological Science, Medical University of South Carolina, SC, USA; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome, Italy.
Abstract
To evaluate a deep-learning algorithm for automated coronary artery analysis on ultrahigh-resolution photon-counting detector coronary computed tomography (CT) angiography and compared its performance to expert readers using invasive coronary angiography as reference. Thirty-two patients (mean age 68.6 years; 81 % male) underwent both energy-integrating detector and ultrahigh-resolution photon-counting detector CT within 30 days. Expert readers scored each image using the Coronary Artery Disease-Reporting and Data System classification, and compared to invasive angiography. After a three-month wash-out, one reader reanalyzed the photon-counting detector CT images assisted by the algorithm. Sensitivity, specificity, accuracy, inter-reader agreement, and reading times were recorded for each method. On 401 arterial segments, inter-reader agreement improved from substantial (κ = 0.75) on energy-integrating detector CT to near-perfect (κ = 0.86) on photon-counting detector CT. The algorithm alone achieved 85 % sensitivity, 91 % specificity, and 90 % accuracy on energy-integrating detector CT, and 85 %, 96 %, and 95 % on photon-counting detector CT. Compared to invasive angiography on photon-counting detector CT, manual and automated reads had similar sensitivity (67 %), but manual assessment slightly outperformed regarding specificity (85 % vs. 79 %) and accuracy (84 % vs. 78 %). When the reader was assisted by the algorithm, specificity rose to 97 % (p < 0.001), accuracy to 95 %, and reading time decreased by 54 % (p < 0.001). This deep-learning algorithm demonstrates high agreement with experts and improved diagnostic performance on photon-counting detector CT. Expert review augmented by the algorithm further increases specificity and dramatically reduces interpretation time.