ISBE-AstraZeneca Strategic Alliance Project # 42 Modelling DCE-MRI Arterial Input Functions in rats Deirdre McGrath, Strategic Alliance meeting 1 st July.

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ISBE-AstraZeneca Strategic Alliance Project # 42 Modelling DCE-MRI Arterial Input Functions in rats Deirdre McGrath, Strategic Alliance meeting 1 st July 2005

ISBE-AstraZeneca Strategic Alliance Project # 42 Project Aim Identify a suitable model function of the population of Arterial Input Functions (AIFs) acquired in DCE-MRI studies of rats at Alderley Park Develop an approach to parameterise this function on new data

ISBE-AstraZeneca Strategic Alliance Project # 42 Initial data set

ISBE-AstraZeneca Strategic Alliance Project # 42 Example AIFs

ISBE-AstraZeneca Strategic Alliance Project # 42 Signals from tumours

ISBE-AstraZeneca Strategic Alliance Project # 42 Solve for DCE-MRI Kinetic Parameters Using extended Kety Model: Where: C tissue is the concentration of contrast agent in the tissue C AIF that in the artery V p fractional blood plasma volume K trans volume transfer constant V e fractional extravascular, extracellular space (EES) volume

ISBE-AstraZeneca Strategic Alliance Project # 42 Possible Approaches Approach 1: Develop analytical model to fit population Candidates: –Exponential –Linear peak followed by exponential –Gamma-variate, the log normal function or the lagged normal density curve –Sum of 2 Gaussians + exponential modulated by sigmoid: (Parker et al, ISMRM, 2005)

ISBE-AstraZeneca Strategic Alliance Project # 42 Possible Approaches contd. Approach 2: generate a model based on Principal Component Analysis (PCA): »Generate a Point Distribution Model (PDM) (Bookstein, 1991) »Using the Minimum Description Length (MDL) correspondence points (Davies et al, IEEE Trans Med Img, 2002) »Limit modes of variation to remove effects of noise ->Either new data sets are projected onto first N axes of PDM to get noise reduced AIF ->Or calculate Maximum Likelihood mode coefficients -> Output noise reduced (and possibly higher resolution) AIF -> Could potentially correlate physiology with PDM modes to generate simulated AIFs

ISBE-AstraZeneca Strategic Alliance Project # 42 Approach 1: Fit Analytical Model Sum of 2 Gaussians + exponential modulated by sigmoid

ISBE-AstraZeneca Strategic Alliance Project # 42 Reproducibility of extended Kety model parameters Calculated the within-subject coefficient of variation, wCV (Galbraith et al, NMR Biomed, 2002) Where msd is the mean squared difference and mean the overall mean for the parameter And d is the difference in parameters between scans and n the number of subjects

ISBE-AstraZeneca Strategic Alliance Project # 42 Approach 2: Generate PDM Generate a Point Distribution Model (PDM) using raw data as training set. Out of 27 modes, 20 have a significant influence

ISBE-AstraZeneca Strategic Alliance Project # 42 Remove noise using Analytical Fit Performing a pre-fit of the data to remove noise Reduces no. of significant modes to 8

ISBE-AstraZeneca Strategic Alliance Project # 42 Future direction Optimisation of automatically generated MDL Manually registered MDLs Correlation of physiological factors with PDM modes Use Factor Analysis as opposed to PCA – can better handle noisy input Reproducibility study incorporating all AIF models, including simpler models (exponential etc.)