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

Jun Yu Guo, Wilburn E. Reddick

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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 T1 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 T1 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 kep, 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 T1 estimations and model dependence.

Original languageEnglish (US)
Pages (from-to)177-184
Number of pages8
JournalJournal of Magnetic Resonance Imaging
Issue number1
StatePublished - Jul 2009
Externally publishedYes


  • Curve pattern analysis
  • Dynamic contrast-enhanced (DCE) MRI
  • Tumor perfusion

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'DCE-MRI pixel-by-pixel quantitative curve pattern analysis and its application to osteosarcoma'. Together they form a unique fingerprint.

Cite this