Cargando…

Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve

OBJECTIVES: We evaluated the influence of image reconstruction kernels on the diagnostic accuracy of CT-derived fractional flow reserve (FFR(CT)) compared to invasive FFR in patients with coronary artery disease. METHODS: Sixty-nine patients, in whom coronary CT angiography was performed and who wer...

Descripción completa

Detalles Bibliográficos
Autores principales: Ammon, Fabian, Moshage, Maximilian, Smolka, Silvia, Goeller, Markus, Bittner, Daniel O., Achenbach, Stephan, Marwan, Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921129/
https://www.ncbi.nlm.nih.gov/pubmed/34735608
http://dx.doi.org/10.1007/s00330-021-08348-0
_version_ 1784669271085809664
author Ammon, Fabian
Moshage, Maximilian
Smolka, Silvia
Goeller, Markus
Bittner, Daniel O.
Achenbach, Stephan
Marwan, Mohamed
author_facet Ammon, Fabian
Moshage, Maximilian
Smolka, Silvia
Goeller, Markus
Bittner, Daniel O.
Achenbach, Stephan
Marwan, Mohamed
author_sort Ammon, Fabian
collection PubMed
description OBJECTIVES: We evaluated the influence of image reconstruction kernels on the diagnostic accuracy of CT-derived fractional flow reserve (FFR(CT)) compared to invasive FFR in patients with coronary artery disease. METHODS: Sixty-nine patients, in whom coronary CT angiography was performed and who were further referred for invasive coronary angiography with FFR measurement via pressure wire, were retrospectively included. CT data sets were acquired using a third-generation dual-source CT system and rendered with medium smooth (Bv40) and sharp (Bv49) reconstruction kernels. FFR(CT) was calculated on-site using prototype software. Coronary stenoses with invasive FFR ≤ 0.80 were classified as significant. Agreement between FFR(CT) and invasive FFR was determined for both reconstruction kernels. RESULTS: One hundred analyzed vessels in 69 patients were included. Twenty-five vessels were significantly stenosed according to invasive FFR. Using a sharp reconstruction kernel for FFR(CT) resulted in a significantly higher correlation with invasive FFR (r = 0.74, p < 0.01 vs. r = 0.58, p < 0.01; p = 0.04) and a higher AUC in ROC curve analysis to correctly identify/exclude significant stenosis (AUC = 0.92 vs. AUC = 0.82 for sharp vs. medium smooth kernel, respectively, p = 0.02). A FFR(CT) value of ≤ 0.8 using a sharp reconstruction kernel showed a sensitivity of 88% and a specificity of 92% for detecting ischemia-causing lesions, resulting in a diagnostic accuracy of 91%. The medium smooth reconstruction kernel performed worse (sensitivity 60%, specificity 89%, accuracy 82%). CONCLUSION: Compared to invasively measured FFR, FFR(CT) using a sharp image reconstruction kernel shows higher diagnostic accuracy for detecting lesions causing ischemia, potentially altering decision-making in a clinical setting. KEY POINTS: • Image reconstruction parameters influence the diagnostic accuracy of simulated fractional flow reserve derived from coronary computed tomography angiography. • Using a sharp kernel image reconstruction algorithm delivers higher diagnostic accuracy compared to medium smooth kernel image reconstruction (gold standard invasive fractional flow reserve).
format Online
Article
Text
id pubmed-8921129
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-89211292022-03-17 Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve Ammon, Fabian Moshage, Maximilian Smolka, Silvia Goeller, Markus Bittner, Daniel O. Achenbach, Stephan Marwan, Mohamed Eur Radiol Cardiac OBJECTIVES: We evaluated the influence of image reconstruction kernels on the diagnostic accuracy of CT-derived fractional flow reserve (FFR(CT)) compared to invasive FFR in patients with coronary artery disease. METHODS: Sixty-nine patients, in whom coronary CT angiography was performed and who were further referred for invasive coronary angiography with FFR measurement via pressure wire, were retrospectively included. CT data sets were acquired using a third-generation dual-source CT system and rendered with medium smooth (Bv40) and sharp (Bv49) reconstruction kernels. FFR(CT) was calculated on-site using prototype software. Coronary stenoses with invasive FFR ≤ 0.80 were classified as significant. Agreement between FFR(CT) and invasive FFR was determined for both reconstruction kernels. RESULTS: One hundred analyzed vessels in 69 patients were included. Twenty-five vessels were significantly stenosed according to invasive FFR. Using a sharp reconstruction kernel for FFR(CT) resulted in a significantly higher correlation with invasive FFR (r = 0.74, p < 0.01 vs. r = 0.58, p < 0.01; p = 0.04) and a higher AUC in ROC curve analysis to correctly identify/exclude significant stenosis (AUC = 0.92 vs. AUC = 0.82 for sharp vs. medium smooth kernel, respectively, p = 0.02). A FFR(CT) value of ≤ 0.8 using a sharp reconstruction kernel showed a sensitivity of 88% and a specificity of 92% for detecting ischemia-causing lesions, resulting in a diagnostic accuracy of 91%. The medium smooth reconstruction kernel performed worse (sensitivity 60%, specificity 89%, accuracy 82%). CONCLUSION: Compared to invasively measured FFR, FFR(CT) using a sharp image reconstruction kernel shows higher diagnostic accuracy for detecting lesions causing ischemia, potentially altering decision-making in a clinical setting. KEY POINTS: • Image reconstruction parameters influence the diagnostic accuracy of simulated fractional flow reserve derived from coronary computed tomography angiography. • Using a sharp kernel image reconstruction algorithm delivers higher diagnostic accuracy compared to medium smooth kernel image reconstruction (gold standard invasive fractional flow reserve). Springer Berlin Heidelberg 2021-11-04 2022 /pmc/articles/PMC8921129/ /pubmed/34735608 http://dx.doi.org/10.1007/s00330-021-08348-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Cardiac
Ammon, Fabian
Moshage, Maximilian
Smolka, Silvia
Goeller, Markus
Bittner, Daniel O.
Achenbach, Stephan
Marwan, Mohamed
Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
title Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
title_full Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
title_fullStr Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
title_full_unstemmed Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
title_short Influence of reconstruction kernels on the accuracy of CT-derived fractional flow reserve
title_sort influence of reconstruction kernels on the accuracy of ct-derived fractional flow reserve
topic Cardiac
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921129/
https://www.ncbi.nlm.nih.gov/pubmed/34735608
http://dx.doi.org/10.1007/s00330-021-08348-0
work_keys_str_mv AT ammonfabian influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve
AT moshagemaximilian influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve
AT smolkasilvia influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve
AT goellermarkus influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve
AT bittnerdanielo influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve
AT achenbachstephan influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve
AT marwanmohamed influenceofreconstructionkernelsontheaccuracyofctderivedfractionalflowreserve