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Abstract #3487

Diffusion weighted imaging of prostate cancer xenografts: comparison of bayesian modeling and independent least squares fitting

Parisa Movahedi1, Hanne Hakkarainen2, Harri Merisaari1, Heidi Liljenbäck1, Helena Virtanen1, Hannu Juhani Aronen1, Heikki Minn1, Matti Poutanen1, Anne Roivainen1, Timo Liimatainen2, and Ivan Jambor1

1University of Turku, Turku, Finland, 2A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland

Tumor growth in mice preclinical prostate cancer model (human prostate cancer cells, PC-3) was followed for 4 weeks by weekly DWI in control group (n=10) and treatment group (n=9) receiving Docetaxel. DWI data sets were acquired using 15 b-values in the range of 0-500s/mm2 and 12 b-values the range of 0-2000 s/mm2. The DWI signal decays were fitted using monoexponential, biexponential, kurtosis and stretched exponential models/functions. Bayesian shrinkage prior method and independent least squares fitting have been applied and fitting quality evaluated by corrected Akaike Information Criteria. Bayesian modeling improved quality of DWI parametric maps derived using high b-value DWI data sets. Our result does not support the use of biexponential, kurtosis and stretched exponential models/functions for low b value DWI data sets of PC-3 mice preclinical prostate cancer model.

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