Section: New Results

Model and parameter identification combining stochastic and deterministic approaches in nonlocal and multi-scale models

Data assimilation and stochastic modelling for protein aggregation

Following Carola Kruse's post-doc  [57], in collaboration with Tom Banks, Aurora Armiento's Ph.D [1], co-supervised with Philippe Moireau, was devoted to the question of adapting data assimilation strategies to the specific context and difficulties of protein aggregation.

In parallel with the statistical approach to growth and division processes, the deterministic approach has been continued in collaboration with Magali Tournus [35].

Estimating cellularity and tumour heterogeneity from Diffusion-Weighted MRI based on histological data

In [25] we developed, in close collaboration with the University of Heidelberg and DKFZ, together with I. Vignon-Clementel (Inria team REO), a procedure to estimate tumour heterogeneity and cellularity from Diffusion-Weighted Imaging (DWI) with calibration using histological data. The estimate is based on the intravoxel incoherent motion (IVIM) model that relates the DWI signal to water diffusion within each image voxel, as well as on an image processing and analysis procedure we developed for automated cell counting in large histological samples after tumour removal. We recently showed that biopsies routinely taken are likely to be sufficient to construct a calibration curve to relate DWI diffusion coefficient to cell density, and thus to infer the whole tumour heterogeneity. The biopsies have to be taken in regions of largely different diffusion values.