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Abstract #1717

Evaluation of breast cancer using IVIM-Kurtosis and compartmental tracer kinetic models through semi-automated segmentation

Natalia Noemí Massaccesi Bove1,2, Trinidad González Padin1,2, Daniel Fino1,2,3, Nicolás Moyano Brandi1,2, Clara Lisazo1,2, Leandro Enrique Salcedo1,2,4, Federico Julián González Nicolini2,3,5, Pedro Pablo Ariza1,2, Roberto Andres Isoardi2,3,5, María Cecilia Lorca1,2, Cristian Allard6, and Verónica R. Saucedo1,2
1MRI Department, Fundación Argentina para el Desarrollo en Salud, Mendoza, Argentina, 2MRI Department, Fundación Escuela de Medicina Nuclear, Mendoza, Argentina, 3Instituto Balseiro, Universidad Nacional de Cuyo, San Carlos de Bariloche, Argentina, 4Universidad de Mendoza, Mendoza, Argentina, 5GQNYCS, Comisión Nacional de Energía Atómica, CABA, Argentina, 6GE Healthcare, Santiago de Chile, Chile


This study aims to evaluate the correlation between the IVIM-DKI models (which describe pure and pseudo diffusion) and the quantitative and semiquantitative parameters from dynamic contrast-enhanced (DCE) MRI (that analyze vascular permeability and pharmacokinetics). A Python algorithm was implemented to adjust the intensities of 12 b-values DWI to a biexponential function modeling the IVIM and DKI. Then, the statistical association between these parameters and the DCE MRI was evaluated with a Pearson’s correlation coefficient. The f and D* factors are both good biomarkers for the evaluation of perfusion properties, according to the examined correlation between all the parameters.

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