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

Affiliations

¹ 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.
² 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.
³ 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. Electronic address: schoepf@musc.edu.
⁴ Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
⁵ Department of Radiological Sciences, Oncology and Pathology, University of Rome "Sapienza", Rome, Italy.
⁶ 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.
⁷ Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany.
⁸ Department of Neuroscience and Imaging, Section of Diagnostic Imaging and Therapy - Radiology Division, SS. Annunziata Hospital, "G. d'Annunzio" University, Chieti, Italy.
⁹ 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.

PMID: 30391256
DOI: 10.1016/j.jcct.2018.10.026

Comparative Study

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

Domenico Mastrodicasa et al. J Cardiovasc Comput Tomogr. 2019 Nov-Dec.

. 2019 Nov-Dec;13(6):331-335.

doi: 10.1016/j.jcct.2018.10.026. Epub 2018 Oct 26.

Authors

Affiliations

¹ 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.
² 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.
³ 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. Electronic address: schoepf@musc.edu.
⁴ Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
⁵ Department of Radiological Sciences, Oncology and Pathology, University of Rome "Sapienza", Rome, Italy.
⁶ 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.
⁷ Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany.
⁸ Department of Neuroscience and Imaging, Section of Diagnostic Imaging and Therapy - Radiology Division, SS. Annunziata Hospital, "G. d'Annunzio" University, Chieti, Italy.
⁹ 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.

PMID: 30391256
DOI: 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-FFR_ML) has not been investigated. CT-FFR_ML values and processing time of two reconstruction algorithms were compared using an on-site workstation.

Methods: CT-FFR_ML 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-FFR_ML 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-FFR_ML 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-FFR_ML values (p ≤ 0.05). Correlation of CT-FFR_ML 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-FFR_ML 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-FFR_ML analysis, potentially affecting patient management. Additionally, iterative reconstruction improves CT-FFR_ML post-processing speed.

Keywords: Coronary artery disease; Coronary computed tomography angiography; Filtered back-projection; Fractional flow reserve; Iterative reconstruction.

Published by Elsevier Inc.

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

Affiliations

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

Authors

Affiliations

Abstract

Publication types

MeSH terms

LinkOut - more resources

Full Text Sources

Medical

Miscellaneous