A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation |
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| Authors: | Andriy Fedorov Jacob Fluckiger Gregory D. Ayers Xia Li Sandeep N. Gupta Clare Tempany Robert Mulkern Thomas E. Yankeelov Fiona M. Fennessy |
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| Affiliation: | 1. Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115;2. Department of Radiology, Northwestern University, Chicago, Illinois 60611;3. Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37212;4. Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37212;5. Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee 37212;6. General Electric Global Research, Niskayuna, New York 12309;g Department of Radiology, Children’s Hospital Boston, Harvard Medical School, Boston, Massachusetts 02115;h Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee 37212;i Department of Physics, Vanderbilt University, Nashville, Tennessee 37212;j Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee 37212 |
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| Abstract: | Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. |
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| Keywords: | Prostate cancer DCE-MRI Arterial Input Function Pharmacokinetic modeling Quantitative imaging |
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