Literature DB >> 35237785

A Noninvasive Assessment of Flow Based on Contrast Dispersion in Computed Tomography Angiography: A Computational and Experimental Phantom Study.

Parastou Eslami1, Jung-Hee Seo2, Amir Ali Rahsepar3, Asim Shafique4, Shirley F Rollison5, Albert C Lardo6, Rajat Mittal7, Marcus Y Chen5.   

Abstract

Transluminal attenuation gradient (TAG), defined as the gradient of the contrast agent attenuation drop along the vessel, is an imaging biomarker that indicates stenosis in the coronary arteries. The transluminal attenuation flow encoding (TAFE) equation is a theoretical platform that quantifies blood flow in each coronary artery based on computed tomography angiography (CTA) imaging. This formulation couples TAG (i.e., contrast dispersion along the vessel) with fluid dynamics. However, this theoretical concept has never been validated experimentally. The aim of this proof-of-principle phantom study is to validate TAFE based on CTA imaging. Dynamic CTA images were acquired every 0.5 s. The average TAFE estimated flow rates were compared against four predefined pump values in a straight (20, 25, 30, 35, and 40 ml/min) and a tapered phantom (25, 35, 45, and 55 ml/min). Using the TAFE formulation with no correction, the flow rates were underestimated by 33% and 81% in the straight and tapered phantoms, respectively. The TAFE formulation was corrected for imaging artifacts focusing on partial volume averaging and radial variation of contrast enhancement. After corrections, the flow rates estimated in the straight and tapered phantoms had an excellent Pearson correlation of r = 0.99 and 0.87 (p < 0.001), respectively, with only a 0.6%±0.2 mL/min difference in estimation of the flow rate. In this proof-of-concept phantom study, we corrected the TAFE formulation and showed a good agreement with the actual pump values. Future clinical validations are needed for feasibility of TAFE in clinical use.
Copyright © 2022 by ASME.

Entities:  

Keywords:  contrast agent; coronary computed tomography; noninvasive flow rate; time density; transluminal attenuation gradient; transluminal flow encoding

Mesh:

Year:  2022        PMID: 35237785      PMCID: PMC8990739          DOI: 10.1115/1.4053997

Source DB:  PubMed          Journal:  J Biomech Eng        ISSN: 0148-0731            Impact factor:   1.899


  23 in total

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Journal:  J Magn Reson Imaging       Date:  2004-06       Impact factor: 4.813

2.  Blood velocity calculated from volumetric dynamic computed tomography angiography.

Authors:  Joe J Barfett; Jorn Fierstra; David J Mikulis; Timo Krings
Journal:  Invest Radiol       Date:  2010-12       Impact factor: 6.016

3.  Active contours without edges.

Authors:  T F Chan; L A Vese
Journal:  IEEE Trans Image Process       Date:  2001       Impact factor: 10.856

4.  A computational approach to edge detection.

Authors:  J Canny
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1986-06       Impact factor: 6.226

5.  Association of coronary plaque burden with fractional flow reserve: should we keep attempting to derive physiology from anatomy?

Authors:  Thura T Abd; Richard T George
Journal:  Cardiovasc Diagn Ther       Date:  2015-02

6.  Diagnostic performance of intracoronary gradient-based methods by coronary computed tomography angiography for the evaluation of physiologically significant coronary artery stenoses: a validation study with fractional flow reserve.

Authors:  Jin-Ho Choi; Bon-Kwon Koo; Yeonyee E Yoon; James K Min; Young-Bin Song; Joo-Yong Hahn; Seung-Hyuk Choi; Hyeon-Cheol Gwon; Yeon Hyeon Choe
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2012-07-15       Impact factor: 6.875

7.  Transluminal attenuation gradient in coronary computed tomography angiography is a novel noninvasive approach to the identification of functionally significant coronary artery stenosis: a comparison with fractional flow reserve.

Authors:  Dennis T L Wong; Brian S Ko; James D Cameron; Nitesh Nerlekar; Michael C H Leung; Yuvaraj Malaiapan; Marcus Crossett; Darryl P Leong; Stephen G Worthley; John Troupis; Ian T Meredith; Sujith K Seneviratne
Journal:  J Am Coll Cardiol       Date:  2013-02-13       Impact factor: 24.094

8.  Noninvasive FFR Derived From Coronary CT Angiography: Management and Outcomes in the PROMISE Trial.

Authors:  Michael T Lu; Maros Ferencik; Rhonda S Roberts; Kerry L Lee; Alexander Ivanov; Elizabeth Adami; Daniel B Mark; Farouc A Jaffer; Jonathon A Leipsic; Pamela S Douglas; Udo Hoffmann
Journal:  JACC Cardiovasc Imaging       Date:  2017-04-12

9.  Identification of High-Risk Plaques Destined to Cause Acute Coronary Syndrome Using Coronary Computed Tomographic Angiography and Computational Fluid Dynamics.

Authors:  Joo Myung Lee; Gilwoo Choi; Bon-Kwon Koo; Doyeon Hwang; Jonghanne Park; Jinlong Zhang; Kyung-Jin Kim; Yaliang Tong; Hyun Jin Kim; Leo Grady; Joon-Hyung Doh; Chang-Wook Nam; Eun-Seok Shin; Young-Seok Cho; Su-Yeon Choi; Eun Ju Chun; Jin-Ho Choi; Bjarne L Nørgaard; Evald H Christiansen; Koen Niemen; Hiromasa Otake; Martin Penicka; Bernard de Bruyne; Takashi Kubo; Takashi Akasaka; Jagat Narula; Pamela S Douglas; Charles A Taylor; Hyo-Soo Kim
Journal:  JACC Cardiovasc Imaging       Date:  2018-03-14

10.  Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractional flow reserve in patients with stable angina.

Authors:  W Bob Meijboom; Carlos A G Van Mieghem; Niels van Pelt; Annick Weustink; Francesca Pugliese; Nico R Mollet; Eric Boersma; Eveline Regar; Robert J van Geuns; Peter J de Jaegere; Patrick W Serruys; Gabriel P Krestin; Pim J de Feyter
Journal:  J Am Coll Cardiol       Date:  2008-08-19       Impact factor: 24.094

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