Literature DB >> 17228107

Reference tissue quantification of DCE-MRI data without a contrast agent calibration.

Simon Walker-Samuel1, Martin O Leach, David J Collins.   

Abstract

The quantification of dynamic contrast-enhanced (DCE) MRI data conventionally requires a conversion from signal intensity to contrast agent concentration by measuring a change in the tissue longitudinal relaxation rate, R(1). In this paper, it is shown that the use of a spoiled gradient-echo acquisition sequence (optimized so that signal intensity scales linearly with contrast agent concentration) in conjunction with a reference tissue-derived vascular input function (VIF), avoids the need for the conversion to Gd-DTPA concentration. This study evaluates how to optimize such sequences and which dynamic time-series parameters are most suitable for this type of analysis. It is shown that signal difference and relative enhancement provide useful alternatives when full contrast agent quantification cannot be achieved, but that pharmacokinetic parameters derived from both contain sources of error (such as those caused by differences between reference tissue and region of interest proton density and native T(1) values). It is shown in a rectal cancer study that these sources of uncertainty are smaller when using signal difference, compared with relative enhancement (15 +/- 4% compared with 33 +/- 4%). Both of these uncertainties are of the order of those associated with the conversion to Gd-DTPA concentration, according to literature estimates.

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Year:  2007        PMID: 17228107     DOI: 10.1088/0031-9155/52/3/004

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


  10 in total

Review 1.  Dynamic contrast-enhanced magnetic resonance imaging and pharmacokinetic models in prostate cancer.

Authors:  Tobias Franiel; Bernd Hamm; Hedvig Hricak
Journal:  Eur Radiol       Date:  2010-12-24       Impact factor: 5.315

2.  Response-Derived Input Function Estimation for Dynamic Contrast-Enhanced MRI Demonstrated by Anti-DLL4 Treatment in a Murine U87 Xenograft Model.

Authors:  Matthew D Silva; Brittany Yerby; Jodi Moriguchi; Albert Gomez; H Toni Jun; Angela Coxon; Sharon E Ungersma
Journal:  Mol Imaging Biol       Date:  2017-10       Impact factor: 3.488

3.  Quantification of massive allograft healing with dynamic contrast enhanced-MRI and cone beam-CT: a pilot study.

Authors:  Nicole Ehrhart; Susan Kraft; David Conover; Randy N Rosier; Edward M Schwarz
Journal:  Clin Orthop Relat Res       Date:  2008-06-10       Impact factor: 4.176

4.  Effects of flip angle uncertainty and noise on the accuracy of DCE-MRI metrics: comparison between standard concentration-based and signal difference methods.

Authors:  Ping Wang; Yiqun Xue; Xia Zhao; Jiangsheng Yu; Mark Rosen; Hee Kwon Song
Journal:  Magn Reson Imaging       Date:  2014-10-13       Impact factor: 2.546

5.  Quantitative analysis of clinical dynamic contrast-enhanced MR imaging for evaluating treatment response in human breast cancer.

Authors:  Yanming Yu; Quan Jiang; Yanwei Miao; Jun Li; Shanglian Bao; Haoyu Wang; Chunxue Wu; Xiaoying Wang; Jiong Zhu; Yi Zhong; E Mark Haacke; Jiani Hu
Journal:  Radiology       Date:  2010-08-16       Impact factor: 11.105

6.  Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data.

Authors:  Ya Wang; Wei Huang; David M Panicek; Lawrence H Schwartz; Jason A Koutcher
Journal:  Magn Reson Med       Date:  2008-05       Impact factor: 4.668

7.  Quantitative vascular neuroimaging of the rat brain using superparamagnetic nanoparticles: New insights on vascular organization and brain function.

Authors:  Codi A Gharagouzloo; Liam Timms; Ju Qiao; Zihang Fang; Joseph Nneji; Aniket Pandya; Praveen Kulkarni; Anne L van de Ven; Craig Ferris; Srinivas Sridhar
Journal:  Neuroimage       Date:  2017-09-06       Impact factor: 6.556

8.  Arterial input functions (AIFs) measured directly from arteries with low and standard doses of contrast agent, and AIFs derived from reference tissues.

Authors:  Shiyang Wang; Xiaobing Fan; Milica Medved; Federico D Pineda; Ambereen Yousuf; Aytekin Oto; Gregory S Karczmar
Journal:  Magn Reson Imaging       Date:  2015-10-30       Impact factor: 2.546

9.  Distribution of Intravascular and Extravascular Extracellular Volume Fractions by Total Area under Curve for Neovascularization Assessment by Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Authors:  Yi Sun
Journal:  J Med Signals Sens       Date:  2014-07

10.  Average arterial input function for quantitative dynamic contrast enhanced magnetic resonance imaging of neck nodal metastases.

Authors:  Amita Shukla-Dave; Nancy Lee; Hilda Stambuk; Ya Wang; Wei Huang; Howard T Thaler; Snehal G Patel; Jatin P Shah; Jason A Koutcher
Journal:  BMC Med Phys       Date:  2009-04-07
  10 in total

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