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Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniques

  • Domenico Mastrodicasa
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA

    Department of Radiology, Division of Cardiovascular Imaging, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA, USA

    Department of Neuroscience and Imaging, Section of Diagnostic Imaging and Therapy - Radiology Division, SS. Annunziata Hospital, “G. d’Annunzio” University, Chieti, Italy
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  • Moritz H. Albrecht
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA

    Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
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  • U. Joseph Schoepf
    Correspondence
    Corresponding author. Heart & Vascular Center, Medical University of South Carolina Ashley River Tower 25 Courtenay Drive, Charleston, SC 29425-2260, USA.
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA

    Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
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  • Akos Varga-Szemes
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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  • Brian E. Jacobs
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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  • Sebastian Gassenmaier
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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  • Domenico De Santis
    Affiliations
    Department of Radiological Sciences, Oncology and Pathology, University of Rome “Sapienza”, Rome, Italy
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  • Marwen H. Eid
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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  • Marly van Assen
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA

    Center for Medical Imaging - North East Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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  • Chris Tesche
    Affiliations
    Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany
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  • Cesare Mantini
    Affiliations
    Department of Neuroscience and Imaging, Section of Diagnostic Imaging and Therapy - Radiology Division, SS. Annunziata Hospital, “G. d’Annunzio” University, Chieti, Italy
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  • Carlo N. De Cecco
    Affiliations
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA

    Department of Radiology & Imaging Sciences, Emory University, Atlanta, GA, USA
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Published:October 25, 2018DOI:https://doi.org/10.1016/j.jcct.2018.10.026

      Abstract

      Background

      The influence of computed tomography (CT) reconstruction algorithms on the performance of machine-learning-based CT-derived fractional flow reserve (CT-FFRML) has not been investigated. CT-FFRML values and processing time of two reconstruction algorithms were compared using an on-site workstation.

      Methods

      CT-FFRML was computed on 40 coronary CT angiography (CCTA) datasets that were reconstructed with both iterative reconstruction in image space (IRIS) and filtered back-projection (FBP) algorithms. CT-FFRML was computed on a per-vessel and per-segment basis as well as distal to lesions with ≥50% stenosis on CCTA. Processing times were recorded. Significant flow-limiting stenosis was defined as invasive FFR and CT-FFRML values ≤ 0.80. Pearson's correlation, Wilcoxon, and McNemar statistical testing were used for data analysis.

      Results

      Per-vessel analysis of IRIS and FBP reconstructions demonstrated significantly different CT-FFRML values (p ≤ 0.05). Correlation of CT-FFRML values between algorithms was high for the left main (r = 0.74), left anterior descending (r = 0.76), and right coronary (r = 0.70) arteries. Proximal and middle segments showed a high correlation of CT-FFRML values (r = 0.73 and r = 0.67, p ≤ 0.001, respectively), despite having significantly different averages (p ≤ 0.05). No difference in diagnostic accuracy was observed (both 81.8%, p = 1.000). Of the 40 patients, 10 had invasive FFR results. Per-lesion correlation with invasive FFR values was moderate for IRIS (r = 0.53, p = 0.117) and FBP (r = 0.49, p = 0.142). Processing time was significantly shorter using IRIS (15.9 vs. 19.8 min, p ≤ 0.05).

      Conclusion

      CT reconstruction algorithms influence CT-FFRML analysis, potentially affecting patient management. Additionally, iterative reconstruction improves CT-FFRML post-processing speed.

      Keywords

      Abbreviations:

      BMI (Body mass index), CAD (Coronary artery disease), CCTA (Coronary CT angiography), CT-FFR (CT-derived fractional flow reserve), CT-FFRML (Machine-learning-based coronary CT-derived fractional flow reserve), CX (Left circumflex artery), FBP (Filtered back-projection), FFR (Fractional flow reserve), ICA (Invasive catheter angiography), IRIS (Iterative reconstruction in image space), LAD (Left anterior descending artery), LM (Left main artery), RCA (Right coronary artery), SD (Standard deviation)
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