Literature DB >> 24614352

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT.

Michael Bindschadler1, Dimple Modgil, Kelley R Branch, Patrick J La Riviere, Adam M Alessio.   

Abstract

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)(-1), cardiac output = 3, 5, 8 L min(-1)). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that there is no particular advantage between quantitative estimation methods nor to performing dose reduction via tube current reduction compared to temporal sampling reduction. These data are important for optimizing implementation of cardiac dynamic CT in clinical practice and in prospective CT MBF trials.

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Year:  2014        PMID: 24614352      PMCID: PMC4057043          DOI: 10.1088/0031-9155/59/7/1533

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  47 in total

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2.  Quantification of myocardial perfusion using dynamic 64-detector computed tomography.

Authors:  Richard T George; Michael Jerosch-Herold; Caterina Silva; Kakuya Kitagawa; David A Bluemke; Joao A C Lima; Albert C Lardo
Journal:  Invest Radiol       Date:  2007-12       Impact factor: 6.016

3.  Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI.

Authors:  Steven Sourbron; Michael Ingrisch; Axel Siefert; Maximilian Reiser; Karin Herrmann
Journal:  Magn Reson Med       Date:  2009-07       Impact factor: 4.668

4.  On the physical and stochastic representation of an indicator dilution curve as a gamma variate.

Authors:  M Mischi; J A den Boer; H H M Korsten
Journal:  Physiol Meas       Date:  2008-02-11       Impact factor: 2.833

5.  Multidetector computed tomography myocardial perfusion imaging during adenosine stress.

Authors:  Richard T George; Caterina Silva; Marco A S Cordeiro; Anthony DiPaula; Douglas R Thompson; William F McCarthy; Takashi Ichihara; Joao A C Lima; Albert C Lardo
Journal:  J Am Coll Cardiol       Date:  2006-06-21       Impact factor: 24.094

6.  Adenosine-stress dynamic myocardial CT perfusion imaging: initial clinical experience.

Authors:  Gorka Bastarrika; Luis Ramos-Duran; Michael A Rosenblum; Doo Kyoung Kang; Garrett W Rowe; U Joseph Schoepf
Journal:  Invest Radiol       Date:  2010-06       Impact factor: 6.016

7.  Quantitative tumor perfusion assessment with multidetector CT: are measurements from two commercial software packages interchangeable?

Authors:  Vicky Goh; Steve Halligan; Clive I Bartram
Journal:  Radiology       Date:  2007-03       Impact factor: 11.105

8.  Incremental value of adenosine-induced stress myocardial perfusion imaging with dual-source CT at cardiac CT angiography.

Authors:  Jose A Rocha-Filho; Ron Blankstein; Leonid D Shturman; Hiram G Bezerra; David R Okada; Ian S Rogers; Brian Ghoshhajra; Udo Hoffmann; Gudrun Feuchtner; Wilfred S Mamuya; Thomas J Brady; Ricardo C Cury
Journal:  Radiology       Date:  2010-02       Impact factor: 11.105

9.  Beam hardening correction in CT myocardial perfusion measurement.

Authors:  Aaron So; Jiang Hsieh; Jian-Ying Li; Ting-Yim Lee
Journal:  Phys Med Biol       Date:  2009-04-27       Impact factor: 3.609

10.  Optimal medical therapy with or without percutaneous coronary intervention to reduce ischemic burden: results from the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Evaluation (COURAGE) trial nuclear substudy.

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Journal:  Circulation       Date:  2008-02-11       Impact factor: 29.690

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  27 in total

1.  Simulation Evaluation of Quantitative Myocardial Perfusion Assessment from Cardiac CT.

Authors:  Michael Bindschadler; Dimple Modgil; Kelley R Branch; Patrick J La Riviere; Adam M Alessio
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2014-03-19

2.  Quantitative myocardial perfusion from static cardiac and dynamic arterial CT.

Authors:  Michael Bindschadler; Kelley R Branch; Adam M Alessio
Journal:  Phys Med Biol       Date:  2018-05-21       Impact factor: 3.609

Review 3.  Advances in myocardial CT perfusion imaging technology.

Authors:  Yan Yi; Zheng-Yu Jin; Yi-Ning Wang
Journal:  Am J Transl Res       Date:  2016-11-15       Impact factor: 4.060

4.  Variable temporal sampling and tube current modulation for myocardial blood flow estimation from dose-reduced dynamic computed tomography.

Authors:  Dimple Modgil; Michael D Bindschadler; Adam M Alessio; Patrick J La Rivière
Journal:  J Med Imaging (Bellingham)       Date:  2017-05-13

5.  The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT.

Authors:  Brendan L Eck; Raymond F Muzic; Jacob Levi; Hao Wu; Rachid Fahmi; Yuemeng Li; Anas Fares; Mani Vembar; Amar Dhanantwari; Hiram G Bezerra; David L Wilson
Journal:  Phys Med Biol       Date:  2018-09-13       Impact factor: 3.609

6.  Mixed Confidence Estimation for Iterative CT Reconstruction.

Authors:  David S Perlmutter; Soo Mee Kim; Paul E Kinahan; Adam M Alessio
Journal:  IEEE Trans Med Imaging       Date:  2016-03-17       Impact factor: 10.048

7.  [Redundancy information-induced image reconstruction for low-dose myocardial perfusion computed tomography].

Authors:  Jiahui Lin; Zhaoying Bian; Jianhua Ma; Jing Huang; Xi Tao; Dong Zeng; Hong Guo
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2018-01-30

8.  Accuracy of Myocardial Blood Flow Estimation From Dynamic Contrast-Enhanced Cardiac CT Compared With PET.

Authors:  Adam M Alessio; Michael Bindschadler; Janet M Busey; William P Shuman; James H Caldwell; Kelley R Branch
Journal:  Circ Cardiovasc Imaging       Date:  2019-06-14       Impact factor: 7.792

9.  Evaluation of static and dynamic perfusion cardiac computed tomography for quantitation and classification tasks.

Authors:  Michael Bindschadler; Dimple Modgil; Kelley R Branch; Patrick J La Riviere; Adam M Alessio
Journal:  J Med Imaging (Bellingham)       Date:  2016-05-02

10.  Sinogram smoothing techniques for myocardial blood flow estimation from dose-reduced dynamic computed tomography.

Authors:  Dimple Modgil; Adam M Alessio; Michael D Bindschadler; Patrick J La Rivière
Journal:  J Med Imaging (Bellingham)       Date:  2014-11-03
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