Literature DB >> 25521666

Comparison of the Diagnostic Performance of Four Quantitative Myocardial Perfusion Estimation Methods Used in Cardiac MR Imaging: CE-MARC Substudy.

John D Biglands1, Derek R Magee, Steven P Sourbron, Sven Plein, John P Greenwood, Aleksandra Radjenovic.   

Abstract

PURPOSE: To compare the diagnostic performance of four tracer kinetic analysis methods to quantify myocardial perfusion from magnetic resonance (MR) imaging cardiac perfusion data sets in terms of their ability to lead to the diagnosis of myocardial ischemia.
MATERIALS AND METHODS: The study was approved by the regional ethics committee, and all patients gave written consent. A representative sample of 50 patients with suspected ischemic heart disease was retrospectively selected from the Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease trial data set. Quantitative myocardial blood flow (MBF) was estimated from rest and adenosine stress MR imaging perfusion data sets by using four established methods. A matching diagnosis of both an inducible defect as assessed with single photon emission computed tomography and a luminal stenosis of 70% or more as assessed with quantitative x-ray angiography was used as the reference standard for the presence of myocardial ischemia. Diagnostic performance was evaluated with receiver operating characteristic (ROC) curve analysis for each method, with stress MBF and myocardial perfusion reserve (MPR) serving as continuous measures.
RESULTS: Area under the ROC curve with stress MBF and MPR as the outcome measures, respectively, was 0.86 and 0.92 for the Fermi model, 0.85 and 0.87 for the uptake model, 0.85 and 0.80 for the one-compartment model, and 0.87 and 0.87 for model-independent deconvolution. There was no significant difference between any of the models or between MBF and MPR, except that the Fermi model outperformed the one-compartment model if MPR was used as the outcome measure (P = .02).
CONCLUSION: Diagnostic performance of quantitative myocardial perfusion estimates is not affected by the tracer kinetic analysis method used. (©) RSNA

Entities:  

Mesh:

Year:  2014        PMID: 25521666      PMCID: PMC4455679          DOI: 10.1148/radiol.14140433

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  29 in total

1.  Prebolus quantitative MR heart perfusion imaging.

Authors:  Herbert Köstler; Christian Ritter; Michael Lipp; Meinrad Beer; Dietbert Hahn; Jörn Sandstede
Journal:  Magn Reson Med       Date:  2004-08       Impact factor: 4.668

2.  Absolute myocardial perfusion in canines measured by using dual-bolus first-pass MR imaging.

Authors:  Timothy F Christian; Dan W Rettmann; Anthony H Aletras; Steve L Liao; Joni L Taylor; Robert S Balaban; Andrew E Arai
Journal:  Radiology       Date:  2004-07-29       Impact factor: 11.105

3.  Accurate assessment of the arterial input function during high-dose myocardial perfusion cardiovascular magnetic resonance.

Authors:  Peter D Gatehouse; Andrew G Elkington; Nicholas A Ablitt; Guang-Zhong Yang; Dudley J Pennell; David N Firmin
Journal:  J Magn Reson Imaging       Date:  2004-07       Impact factor: 4.813

4.  Clinical validation of SPECT attenuation correction using x-ray computed tomography-derived attenuation maps: multicenter clinical trial with angiographic correlation.

Authors:  Yasmin Masood; Yi-Hwa Liu; Gordon Depuey; Raymond Taillefer; Luis I Araujo; Steven Allen; Dominique Delbeke; Frank Anstett; Aharon Peretz; Mary-Jo Zito; Vera Tsatkin; Frans J Th Wackers
Journal:  J Nucl Cardiol       Date:  2005 Nov-Dec       Impact factor: 5.952

5.  Quantification of myocardial perfusion by MRI after coronary occlusion.

Authors:  J P Vallée; H D Sostman; J R MacFall; T R DeGrado; J Zhang; L Sebbag; F R Cobb; T Wheeler; L W Hedlund; T G Turkington; C E Spritzer; R E Coleman
Journal:  Magn Reson Med       Date:  1998-08       Impact factor: 4.668

6.  Myocardial perfusion and function in dogs with moderate coronary stenosis.

Authors:  D L Kraitchman; N Wilke; E Hexeberg; M Jerosch-Herold; Y Wang; T B Parrish; C N Chang; Y Zhang; R J Bache; L Axel
Journal:  Magn Reson Med       Date:  1996-05       Impact factor: 4.668

7.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

Authors:  E R DeLong; D M DeLong; D L Clarke-Pearson
Journal:  Biometrics       Date:  1988-09       Impact factor: 2.571

8.  Magnetic resonance quantification of the myocardial perfusion reserve with a Fermi function model for constrained deconvolution.

Authors:  M Jerosch-Herold; N Wilke; A E Stillman
Journal:  Med Phys       Date:  1998-01       Impact factor: 4.071

9.  Quantification of myocardial perfusion with FAST sequence and Gd bolus in patients with normal cardiac function.

Authors:  J P Vallée; F Lazeyras; L Kasuboski; P Chatelain; N Howarth; A Righetti; D Didier
Journal:  J Magn Reson Imaging       Date:  1999-02       Impact factor: 4.813

10.  Diagnostic performance of noninvasive myocardial perfusion imaging using single-photon emission computed tomography, cardiac magnetic resonance, and positron emission tomography imaging for the detection of obstructive coronary artery disease: a meta-analysis.

Authors:  Caroline Jaarsma; Tim Leiner; Sebastiaan C Bekkers; Harry J Crijns; Joachim E Wildberger; Eike Nagel; Patricia J Nelemans; Simon Schalla
Journal:  J Am Coll Cardiol       Date:  2012-05-08       Impact factor: 24.094

View more
  23 in total

1.  Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction.

Authors:  Constantine Zakkaroff; John D Biglands; John P Greenwood; Sven Plein; Roger D Boyle; Aleksandra Radjenovic; Derek R Magee
Journal:  J Med Imaging (Bellingham)       Date:  2016-05-13

Review 2.  Cardiovascular Imaging Techniques to Assess Microvascular Dysfunction.

Authors:  Roshin C Mathew; Jamieson M Bourque; Michael Salerno; Christopher M Kramer
Journal:  JACC Cardiovasc Imaging       Date:  2019-10-11

Review 3.  Quantitative Assessment of Coronary Microvascular Function: Dynamic Single-Photon Emission Computed Tomography, Positron Emission Tomography, Ultrasound, Computed Tomography, and Magnetic Resonance Imaging.

Authors:  Attila Feher; Albert J Sinusas
Journal:  Circ Cardiovasc Imaging       Date:  2017-08       Impact factor: 7.792

Review 4.  Cardiovascular imaging in cardio-oncology.

Authors:  Amir Abbas Mahabadi; Christoph Rischpler
Journal:  J Thorac Dis       Date:  2018-12       Impact factor: 2.895

Review 5.  Myocardial perfusion echocardiography and coronary microvascular dysfunction.

Authors:  Giuseppe Barletta; Maria Riccarda Del Bene
Journal:  World J Cardiol       Date:  2015-12-26

6.  Update on Computed Tomography Myocardial Perfusion Imaging.

Authors:  Amita Singh; Victor Mor-Avi; Amit R Patel
Journal:  Curr Cardiovasc Imaging Rep       Date:  2016-05-05

Review 7.  Vasomotor Dysfunction in Patients with Ischemia and Non-Obstructive Coronary Artery Disease: Current Diagnostic and Therapeutic Strategies.

Authors:  Amr Abouelnour; Tommaso Gori
Journal:  Biomedicines       Date:  2021-11-26

8.  Multimodality quantitative assessments of myocardial perfusion using dynamic contrast enhanced magnetic resonance and 15O-labelled water positron emission tomography imaging.

Authors:  G Papanastasiou; M C Williams; M R Dweck; S Mirsadraee; N Weir; A Fletcher; C Lucatelli; D Patel; E J R van Beek; D E Newby; S I K Semple
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2018-01-23

9.  Quantitative assessment of myocardial blood flow in coronary artery disease by cardiovascular magnetic resonance: comparison of Fermi and distributed parameter modeling against invasive methods.

Authors:  Giorgos Papanastasiou; Michelle C Williams; Marc R Dweck; Shirjel Alam; Annette Cooper; Saeed Mirsadraee; David E Newby; Scott I Semple
Journal:  J Cardiovasc Magn Reson       Date:  2016-09-13       Impact factor: 5.364

10.  Clinical associations with stage B heart failure in adults with type 2 diabetes.

Authors:  Gaurav S Gulsin; Emer Brady; Anna-Marie Marsh; Gareth Squire; Zin Z Htike; Emma G Wilmot; John D Biglands; Peter Kellman; Hui Xue; David R Webb; Kamlesh Khunti; Tom Yates; Melanie J Davies; Gerry P McCann
Journal:  Ther Adv Endocrinol Metab       Date:  2021-07-17       Impact factor: 3.565

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.