Abstract #3465

Non-invasive quantification of prostate cancer using AMICO framework for VERDICT MRI

Elisenda Bonet-Carne¹, Alessandro Daducci^2,3, Eleftheria Panagiotaki¹, Edward Johnston⁴, Nicola Stevens⁴, David Atkinson⁴, Shonit Punwani⁴, and Daniel C Alexander¹

¹Centre for Medical Image Computing, University College London, London, United Kingdom, ²Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ³University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ⁴Centre for Medical Imaging, University College London, London, United Kingdom

The aim of this study is to extend the AMICO framework to the VERDICT model-based diffusion-weighted MRI (DW-MRI) technique and to evaluate its performance to prostate cancer imaging. DW-MRI was acquired for 4 subjects and the VERDICT model was fitted to the data using both fitting procedures. In both cases similar differences in parameter values between tumour and normal tissue were found. The AMICO formulation reduces the computation time for VERDICT and produces parameter maps that are more homogeneous than those obtained with the original fitting. The AMICO formulation reflects the microstructural differences in a clinically practical time.

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