Domenico Mastrodicasa1, Moritz H Albrecht2, U Joseph Schoepf3, Akos Varga-Szemes4, Brian E Jacobs4, Sebastian Gassenmaier4, Domenico De Santis5, Marwen H Eid4, Marly van Assen6, Chris Tesche7, Cesare Mantini8, Carlo N De Cecco9. 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.
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. Published by Elsevier Inc.
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. Published by Elsevier Inc.
Authors: Simon S Martin; Domenico Mastrodicasa; Marly van Assen; Carlo N De Cecco; Richard R Bayer; Christian Tesche; Akos Varga-Szemes; Andreas M Fischer; Brian E Jacobs; Pooyan Sahbaee; L Parkwood Griffith; Andrew J Matuskowitz; Thomas J Vogl; U Joseph Schoepf Journal: Radiol Cardiothorac Imaging Date: 2020-06-25
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