Abstract #5388

Spatially constrained mathematical models for diffusion weighted imaging of prostate cancer xenografts in mice: evaluation of therapy response

Parisa Movahedi^1,2, Harri Merisaari¹, Hanne Laakso ³, Ileana Montoya Perez^1,2, Heidi Liljenbäck⁴, Hannu Aronen², Heikki Minn⁵, Anne Roivainen⁴, Timo Liimatainen³, and Ivan Jambor^1,2

¹Department of Future Technologies, University of Turku, Turku, Finland, ²Department of Diagnostic Radiology, University of Turku, Turku, Finland, ³A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland, ⁴Turku PET center, Turku, Finland, ⁵Department of Oncology and Radiotherapy, Turku University Hospital, Turku, 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 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. Independent least squares fitting and spatially constrained Maximum Penalized Likelihood Estimation have been applied. The spatially constrained Maximum Penalized Likelihood Estimation revealed the effect of treatment in mice subjects, while conventional LSQ fitting failed to reveal significant difference between control and treatment.

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