Premium
DCE‐MRI pixel‐by‐pixel quantitative curve pattern analysis and its application to osteosarcoma
Author(s) -
Guo JunYu,
Reddick Wilburn E.
Publication year - 2009
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21785
Subject(s) - flip angle , repeatability , parametric statistics , in vivo , magnetic resonance imaging , correlation , pixel , curve fitting , dynamic contrast , signal (programming language) , nuclear medicine , biomedical engineering , materials science , mathematics , statistics , physics , computer science , medicine , radiology , optics , geometry , microbiology and biotechnology , biology , programming language
Purpose To present a novel curve pattern analysis (CPA) method to characterize and quantify signal curves from the dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) data without any prerequisites such as arterial input function (AIF) or T 1 measurement. Materials and Methods CPA parameters represent characteristics of the scaled DCE signal curve. Simulations were performed to investigate the dependence of CPA parameters on T 1 , TR, and flip angle. In vivo studies were performed on five pediatric patients with osteosarcoma. Parametric maps were generated using the CPA method and a pharmacokinetic model‐based method for comparison. Results Simulations show that CPA parameters varied less than 2% when T 1 changed from 300 msec to 1500 msec, and less than 10% when the flip angle changed from 30° to 40°. Various curve patterns can be qualitatively identified and recognized from CPA parameter maps. Simulation and in vivo studies showed that the CPA parameter had a strong correlation with k ep , with correlation coefficients of 0.9983 in the simulation and 0.95 in the in vivo studies. Conclusion A novel CPA method is presented. Simulations and in vivo studies showed that the CPA method provides a feasible alternative to quantifying DCE‐MRI studies with possibly higher repeatability by minimizing variations potentially induced by AIF and T 1 estimations and model dependence. J. Magn. Reson. Imaging 2009;30:177–184. © 2009 Wiley‐Liss, Inc.