Anuradha Chakravarthy
Faculty Member
Last active: 3/30/2020

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer.

Li X, Welch EB, Arlinghaus LR, Chakravarthy AB, Xu L, Farley J, Loveless ME, Mayer IA, Kelley MC, Meszoely IM, Means-Powell JA, Abramson VG, Grau AM, Gore JC, Yankeelov TE
Phys Med Biol. 2011 56 (17): 5753-69

PMID: 21841212 · PMCID: PMC3176673 · DOI:10.1088/0031-9155/56/17/018

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIF(ind), and compute a population-averaged AIF, AIF(pop). The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for K(trans), v(p) and v(e) as estimated by AIF(ind) and AIF(pop) are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e).

MeSH Terms (15)

Algorithms Axillary Artery Breast Neoplasms Computer Simulation Contrast Media Female Gadolinium DTPA Humans Image Enhancement Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Models, Biological Perfusion Imaging Reproducibility of Results Sensitivity and Specificity

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