Literature DB >> 28786402

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments.

Dianning He1, Marta Zamora, Aytekin Oto, Gregory S Karczmar, Xiaobing Fan.   

Abstract

Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (K trans and v e) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then K trans and v e for the ROI was extracted. The simulations were run 100 times for each ROI with new K trans and v e generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n  =  6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (<5 s) of Gd-DTPA for a total imaging duration of ~10 min. K trans and v e were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting averaged C(t) to extract K trans and v e. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged K trans was ~25% to ~50% larger (p  <  0.01) than the ROI-averaged K trans. At higher noise levels, the pixel-averaged K trans was greater than the 'true' K trans, but the ROI-averaged K trans was lower than the 'true' K trans. The ROI-averaged K trans was closer to the true K trans than pixel-averaged K trans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged K trans was ~15% larger than the ROI-averaged K trans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

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Year:  2017        PMID: 28786402      PMCID: PMC5736132          DOI: 10.1088/1361-6560/aa84d6

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


  29 in total

1.  Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI.

Authors:  Brian M Dale; John A Jesberger; Jonathan S Lewin; Claudia M Hillenbrand; Jeffrey L Duerk
Journal:  J Magn Reson Imaging       Date:  2003-11       Impact factor: 4.813

Review 2.  Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies.

Authors:  James P B O'Connor; Alan Jackson; Geoff J M Parker; Caleb Roberts; Gordon C Jayson
Journal:  Nat Rev Clin Oncol       Date:  2012-02-14       Impact factor: 66.675

3.  The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data.

Authors:  M Heisen; X Fan; J Buurman; N A W van Riel; G S Karczmar; B M ter Haar Romeny
Journal:  Phys Med Biol       Date:  2010-08-03       Impact factor: 3.609

4.  Pharmacokinetic analysis of prostate cancer using independent component analysis.

Authors:  Hatef Mehrabian; Michael Da Rosa; Masoom A Haider; Anne L Martel
Journal:  Magn Reson Imaging       Date:  2015-08-20       Impact factor: 2.546

5.  Reproducibility and correlation between quantitative and semiquantitative dynamic and intrinsic susceptibility-weighted MRI parameters in the benign and malignant human prostate.

Authors:  Roberto Alonzi; N Jane Taylor; J James Stirling; James A d'Arcy; David J Collins; Michele I Saunders; Peter J Hoskin; Anwar R Padhani
Journal:  J Magn Reson Imaging       Date:  2010-07       Impact factor: 4.813

6.  The use of the Levenberg-Marquardt curve-fitting algorithm in pharmacokinetic modelling of DCE-MRI data.

Authors:  T S Ahearn; R T Staff; T W Redpath; S I K Semple
Journal:  Phys Med Biol       Date:  2005-04-13       Impact factor: 3.609

7.  Optimized Fast Dynamic Contrast-Enhanced Magnetic Resonance Imaging of the Prostate: Effect of Sampling Duration on Pharmacokinetic Parameters.

Authors:  Ahmed E Othman; Florian Falkner; Petros Martirosian; Christina Schraml; Christian Schwentner; Dominik Nickel; Konstantin Nikolaou; Mike Notohamiprodjo
Journal:  Invest Radiol       Date:  2016-02       Impact factor: 6.016

8.  Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging.

Authors:  M O Leach; B Morgan; P S Tofts; D L Buckley; W Huang; M A Horsfield; T L Chenevert; D J Collins; A Jackson; D Lomas; B Whitcher; L Clarke; R Plummer; I Judson; R Jones; R Alonzi; T Brunner; D M Koh; P Murphy; J C Waterton; G Parker; M J Graves; T W J Scheenen; T W Redpath; M Orton; G Karczmar; H Huisman; J Barentsz; A Padhani
Journal:  Eur Radiol       Date:  2012-05-07       Impact factor: 5.315

9.  DCE-MRI pixel-by-pixel quantitative curve pattern analysis and its application to osteosarcoma.

Authors:  Jun-Yu Guo; Wilburn E Reddick
Journal:  J Magn Reson Imaging       Date:  2009-07       Impact factor: 4.813

Review 10.  Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols.

Authors:  P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff
Journal:  J Magn Reson Imaging       Date:  1999-09       Impact factor: 4.813

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  1 in total

1.  Test-Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts.

Authors:  Xia Ge; James D Quirk; John A Engelbach; G Larry Bretthorst; Shunqiang Li; Kooresh I Shoghi; Joel R Garbow; Joseph J H Ackerman
Journal:  Tomography       Date:  2019-09
  1 in total

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