Blind estimation of pharmacokinetic parameters in cardiac DCE-MRI
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BioMed Central
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Poster presentation
Blind estimation of pharmacokinetic parameters in cardiac DCE-MRI Jacob Fluckiger*, Matthias Schabel and Edward DiBella Address: University of Utah, Salt Lake City, UT, USA * Corresponding author
from 13th Annual SCMR Scientific Sessions Phoenix, AZ, USA. 21-24 January 2010 Published: 21 January 2010 Journal of Cardiovascular Magnetic Resonance 2010, 12(Suppl 1):P117
doi:10.1186/1532-429X-12-S1-P117
Abstracts of the 13th Annual SCMR Scientific Sessions - 2010
Meeting abstracts - A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/files/pdf/1532-429X-11-S1-infoThis abstract is available from: http://jcmr-online.com/content/12/S1/P117 © 2010 Fluckiger et al; licensee BioMed Central Ltd.
Introduction
Results
Myocardial blood flow estimation in DCE-MRI requires measuring the time course of contrast agent concentration in both the blood pool and myocardial tissues. The differences in signal enhancement in these two regions can complicate the imaging process. This problem has been overcome by performing two studies (dual bolus) with differing contrast agent concentrations.
Figure 1 shows the estimated AIF is slightly lower and more dispersed in time with respect to the scaled low-concentration AIF. This dispersion may be due to dispersion as the CA travels from the LV blood pool to the myocardial tissue and/or flow effects. As seen in Fig 2, the blindly
Purpose This work presents a novel application of the alternating minimization with model (AMM) method to cardiac perfusion data. We estimate the AIF directly from myocardial tissue curves, eliminating the need for perfusion data acquisition at two different concentration levels.
Methods Dual bolus dynamic cardiac MR data was obtained with low (0.004 mmol/kg) and higher (0.02 mmol/kg) doses of Gd-BOPTA. The images were registered spatially and myocardial voxels were identified manually to obtain a set of tissue activity curves (TACs). The TACs were clustered into 12 curves and input to the AMM method to estimate a parameterized AIF. The estimated AIF was scaled such that the average value of the final three data points was equivalent to the measured high-dose AIF. The extended Tofts-Kety model was used to calculate kinetic parameters pixel-wise in the myocardium using both the directly measured AIF from the low-concentration scan and the AMM estimated AIF.
Figure AIFs high concentration measured 1 from(blue low squares) concentration doses (red in cardiac circles), DCE-MRI and AIFs measured from low concentration (red circles), and high concentration (blue squares) doses in cardiac DCE-MRI. AIF measurements for both scans were obtained from an ROI in the LV blood pool. The AIF estimated from the AMM algorthm is also shown (green asterisks).
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Journal of Cardiovascular Magnetic Resonance 2010, 12(Suppl 1):P117
http://jcmr-online.com/content/12/S1/P117
Figure A from and kernel the the2 AMM-
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