Calculation of intravascular signal in dynamic contrast enhanced-MRI using adaptive complex independent component analysis

Hatef Mehrabian, Rajiv Chopra, Anne L. Martel

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Assessing tumor response to therapy is a crucial step in personalized treatments. Pharmacokinetic (PK) modeling provides quantitative information about tumor perfusion and vascular permeability that are associated with prognostic factors. A fundamental step in most PK analyses is calculating the signal that is generated in the tumor vasculature. This signal is usually inseparable from the extravascular extracellular signal. It was shown previously using in vivo and phantom experiments that independent component analysis (ICA) is capable of calculating the intravascular time-intensity curve in dynamic contrast enhanced (DCE)-MRI. A novel adaptive complex independent component analysis (AC-ICA) technique is developed in this study to calculate the intravascular time-intensity curve and separate this signal from the DCE-MR images of tumors. The use of the complex-valued DCE-MRI images rather than the commonly used magnitude images satisfied the fundamental assumption of ICA, i.e., linear mixing of the sources. Using an adaptive cost function in ICA through estimating the probability distribution of the tumor vasculature at each iteration resulted in a more robust and accurate separation algorithm. The AC-ICA algorithm provided a better estimate for the intravascular time-intensity curve than the previous ICA-based method.

Original languageEnglish (US)
Article number6380622
Pages (from-to)699-710
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number4
StatePublished - 2013


  • Adaptive complex independent component analysis (AC-ICA)
  • arterial input function (AIF)
  • intravascular signal intensity
  • pharmacokinetic modeling

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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