Literature DB >> 25403173

Coronary CT angiography-derived fractional flow reserve correlated with invasive fractional flow reserve measurements--initial experience with a novel physician-driven algorithm.

Stefan Baumann1, Rui Wang, U Joseph Schoepf, Daniel H Steinberg, James V Spearman, Richard R Bayer, Christian W Hamm, Matthias Renker.   

Abstract

OBJECTIVES: The present study aimed to determine the feasibility of a novel fractional flow reserve (FFR) algorithm based on coronary CT angiography (cCTA) that permits point-of-care assessment, without data transfer to core laboratories, for the evaluation of potentially ischemia-causing stenoses.
METHODS: To obtain CT-based FFR, anatomical coronary information and ventricular mass extracted from cCTA datasets were integrated with haemodynamic parameters. CT-based FFR was assessed for 36 coronary artery stenoses in 28 patients in a blinded fashion and compared to catheter-based FFR. Haemodynamically relevant stenoses were defined by an invasive FFR ≤0.80. Time was measured for the processing of each cCTA dataset and CT-based FFR computation. Assessment of cCTA image quality was performed using a 5-point scale.
RESULTS: Mean total time for CT-based FFR determination was 51.9 ± 9.0 min. Per-vessel analysis for the identification of lesion-specific myocardial ischemia demonstrated good correlation (Pearson's product-moment r = 0.74, p < 0.0001) between the prototype CT-based FFR algorithm and invasive FFR. Subjective image quality analysis resulted in a median score of 4 (interquartile ranges, 3-4).
CONCLUSIONS: Our initial data suggest that the CT-based FFR method for the detection of haemodynamically significant stenoses evaluated in the selected population correlates well with invasive FFR and renders time-efficient point-of-care assessment possible. KEY POINTS: • CT-based FFR computation is a promising novel non-invasive application. • A novel prototype algorithm permits time-efficient point-of-care CT-based FFR assessment. • Initial results of the CT-based FFR prototype algorithm compare favourably with FFR.

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Year:  2014        PMID: 25403173     DOI: 10.1007/s00330-014-3482-5

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  19 in total

1.  Diagnostic accuracy of combined coronary angiography and adenosine stress myocardial perfusion imaging using 320-detector computed tomography: pilot study.

Authors:  Arthur Nasis; Brian S Ko; Michael C Leung; Paul R Antonis; Dee Nandurkar; Dennis T Wong; Leo Kyi; James D Cameron; John M Troupis; Ian T Meredith; Sujith K Seneviratne
Journal:  Eur Radiol       Date:  2013-02-21       Impact factor: 5.315

Review 2.  Coronary artery computed tomography scanning.

Authors:  Carlo Nicola De Cecco; Felix G Meinel; Salvatore A Chiaramida; Philip Costello; Fabian Bamberg; U Joseph Schoepf
Journal:  Circulation       Date:  2014-03-25       Impact factor: 29.690

3.  In-vivo flow simulation in coronary arteries based on computed tomography datasets: feasibility and initial results.

Authors:  Thomas Frauenfelder; Evangelos Boutsianis; Thomas Schertler; Lars Husmann; Sebastian Leschka; Dimos Poulikakos; Borut Marincek; Hatem Alkadhi
Journal:  Eur Radiol       Date:  2006-10-24       Impact factor: 5.315

4.  Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) study.

Authors:  Bon-Kwon Koo; Andrejs Erglis; Joon-Hyung Doh; David V Daniels; Sanda Jegere; Hyo-Soo Kim; Allison Dunning; Tony DeFrance; Alexandra Lansky; Jonathan Leipsic; James K Min
Journal:  J Am Coll Cardiol       Date:  2011-11-01       Impact factor: 24.094

5.  Effects of adenosine on human coronary arterial circulation.

Authors:  R F Wilson; K Wyche; B V Christensen; S Zimmer; D D Laxson
Journal:  Circulation       Date:  1990-11       Impact factor: 29.690

6.  Coronary CT angiography versus conventional cardiac angiography for therapeutic decision making in patients with high likelihood of coronary artery disease.

Authors:  Antonio Moscariello; Rozemarijn Vliegenthart; U Joseph Schoepf; John W Nance; Peter L Zwerner; Mathias Meyer; Jacob C Townsend; Valerian Fernandes; Daniel H Steinberg; Christian Fink; Matthijs Oudkerk; Lorenzo Bonomo; Terrence X O'Brien; Thomas Henzler
Journal:  Radiology       Date:  2012-08-08       Impact factor: 11.105

7.  Diagnostic accuracy of fractional flow reserve from anatomic CT angiography.

Authors:  James K Min; Jonathon Leipsic; Michael J Pencina; Daniel S Berman; Bon-Kwon Koo; Carlos van Mieghem; Andrejs Erglis; Fay Y Lin; Allison M Dunning; Patricia Apruzzese; Matthew J Budoff; Jason H Cole; Farouc A Jaffer; Martin B Leon; Jennifer Malpeso; G B John Mancini; Seung-Jung Park; Robert S Schwartz; Leslee J Shaw; Laura Mauri
Journal:  JAMA       Date:  2012-09-26       Impact factor: 56.272

8.  Coronary artery plaque formation at coronary CT angiography: morphological analysis and relationship to hemodynamics.

Authors:  Benedetta Enrico; Pal Suranyi; Christian Thilo; Lorenzo Bonomo; Philip Costello; U Joseph Schoepf
Journal:  Eur Radiol       Date:  2008-11-15       Impact factor: 5.315

9.  A framework for personalization of coronary flow computations during rest and hyperemia.

Authors:  Puneet Sharma; Lucian Itu; Xudong Zheng; Ali Kamen; Dominik Bernhardt; Constantin Suciu; Dorin Comaniciu
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

Review 10.  Modeling of fractional flow reserve based on coronary CT angiography.

Authors:  Gilat L Grunau; James K Min; Jonathon Leipsic
Journal:  Curr Cardiol Rep       Date:  2013-01       Impact factor: 2.931

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  19 in total

1.  Interference with MCP-1 gene expression by vector generated triple helix-forming RNA oligonucleotides.

Authors:  K Kautz; M Schwarz; H H Radeke
Journal:  Cell Mol Life Sci       Date:  2005-02       Impact factor: 9.261

2.  Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR.

Authors:  Xiuhua Hu; Minglei Yang; Lu Han; Yujiao Du
Journal:  Int J Cardiovasc Imaging       Date:  2018-07-30       Impact factor: 2.357

3.  Is FFR-CT a "game changer" in the diagnostic management of stable coronary artery disease?

Authors:  W A Leber
Journal:  Herz       Date:  2016-08       Impact factor: 1.443

4.  Fractional flow reserve based on computed tomography: an overview.

Authors:  Francesco Secchi; Marco Alì; Elena Faggiano; Paola Maria Cannaò; Marco Fedele; Silvia Tresoldi; Giovanni Di Leo; Ferdinando Auricchio; Francesco Sardanelli
Journal:  Eur Heart J Suppl       Date:  2016-04-29       Impact factor: 1.803

5.  Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in ischemia-causing coronary stenosis: a meta-analysis.

Authors:  Aimin Ding; Guoqing Qiu; Wensheng Lin; Ling Hu; Guangliang Lu; Xiang Long; Xin Hong; Yaohua Chen; Xiaoping Luo; Qinqin Tang; Dongqin Deng
Journal:  Jpn J Radiol       Date:  2016-10-28       Impact factor: 2.374

Review 6.  Myocardial blood flow quantification for evaluation of coronary artery disease by computed tomography.

Authors:  Filippo Cademartiri; Sara Seitun; Alberto Clemente; Ludovico La Grutta; Patrizia Toia; Giuseppe Runza; Massimo Midiri; Erica Maffei
Journal:  Cardiovasc Diagn Ther       Date:  2017-04

7.  The effect of blood pressure on non-invasive fractional flow reserve derived from coronary computed tomography angiography.

Authors:  Akira Kurata; Adriaan Coenen; Marisa M Lubbers; Koen Nieman; Teruhito Kido; Tomoyuki Kido; Natsumi Yamashita; Kouki Watanabe; Gabriel P Krestin; Teruhito Mochizuki
Journal:  Eur Radiol       Date:  2016-08-19       Impact factor: 5.315

Review 8.  [Computed tomography in patients with chronic stable angina : Fractional flow reserve measurement].

Authors:  M Renker; U J Schoepf; T Becher; N Krampulz; W Kim; A Rolf; H Möllmann; C W Hamm; T Henzler; M Borggrefe; I Akin; S Baumann
Journal:  Herz       Date:  2016-06-02       Impact factor: 1.443

9.  The impact of image resolution on computation of fractional flow reserve: coronary computed tomography angiography versus 3-dimensional quantitative coronary angiography.

Authors:  Lili Liu; Wenjie Yang; Yasuomi Nagahara; Yingguang Li; Saeb R Lamooki; Takashi Muramatsu; Pieter Kitslaar; Masayoshi Sarai; Yukio Ozaki; Peter Barlis; Fuhua Yan; Johan H C Reiber; Shengxian Tu
Journal:  Int J Cardiovasc Imaging       Date:  2015-10-27       Impact factor: 2.357

10.  The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls.

Authors:  Jongmin Seo; Daniele E Schiavazzi; Andrew M Kahn; Alison L Marsden
Journal:  Int J Numer Method Biomed Eng       Date:  2020-06-25       Impact factor: 2.747

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