Jun-Yu Guo1, Wilburn E Reddick. 1. Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee 38105-3678, USA. junyu.guo@stjude.org
Abstract
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 degrees to 40 degrees. 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. (c) 2009 Wiley-Liss, Inc.
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 degrees to 40 degrees. 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. (c) 2009 Wiley-Liss, Inc.
Authors: F Rodjan; P de Graaf; P van der Valk; A C Moll; J P A Kuijer; D L Knol; J A Castelijns; P J W Pouwels Journal: AJNR Am J Neuroradiol Date: 2012-05-24 Impact factor: 3.825
Authors: Antonella Petrillo; Roberta Fusco; Mario Petrillo; Vincenza Granata; Paolo Delrio; Francesco Bianco; Biagio Pecori; Gerardo Botti; Fabiana Tatangelo; Corradina Caracò; Luigi Aloj; Antonio Avallone; Secondo Lastoria Journal: Oncotarget Date: 2017-01-31