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Modelling and simulation

In order to master accurately and optimise the dose delivered to the patient, List institute models the complex physical systems used in this field (medical linear accelerators, imaging devices) and the interactions between material and radiation. Our research provides control software for treatment quality.

List develops also advanced statistical methods for CT, PET or MRI scanners image optimisation and thus, provide clinicians more accurate and informative images: tumour’s shaping, patient’s anatomy etc.

Among our academic partners

INSERM (France), IRSN (France), Frédéric-Joliot Hospital (Orsay, France)

Assets

  • Expertise on a Monte-Carlo code easy to integrate into standard market software for industrial transfers
  • Expertise on specific methods in variance reduction for mainly calculation time reduction
  • Simulations’ metrologic validation via experimental measures thanks to DOSEO platform technical means

Major technologies

Dose calculation for interventional radiology

Description

Interventional radiology gives the surgeon the possibility to rely on radiological images all along the surgery operation. Virtual reality technologies combined to Monte Carlo dose calculation methods are being applied to medical imaging in order to acknowledge the dose delivered to the patient in real-time. The same method will be applied to the practitioner too.

Applications

Dose optimisation on interventional radiology

Publications

  • D. Patin, J. Garcia-Hernandez, M. Agelou, C. Le Loirec, B. Poumarède, C. Van Ngoc Ty, J. Coulot, G. Bonniaud, F. Lavielle, B. Bodin, and D. Lazaro, « Développement et validation d’une plateforme interactive de calcul de dose en radiologie interventionnelle », prés. aux Journées Françaises de Radiologie, Paris, France, 2014.

Dose reduction on PET imaging

Description

A statistical algorithm for PET images reconstruction based on a non-parametric Bayesian approach has been developed in order to reduce the radiotracer’s dose injected to the patient. This algorithm in based on an evaluation of both the tracer’s spatio-temporal distribution activity (4D) and the associated uncertainty. The algorithm helps both capturing complex data structures and imposing an assumption on the quantities that have to be rebuilt in this specific complex issue. It will look for solutions in the spatio-temporal probabilities’ distribution whole density. This approach made it possible to obtain clinical images of standard quality with a dose divided by 10.

Applications

Dose reduction in positron emission tomography imaging (PET)

Major projects

  • ESTEBAN Project

Publications

  • E. Barat, C. Comtat, T. Dautremer, T. Montagu, M.D. Fall, A. Mohammad-Djafari, R. Trébossen, “Nonparametric Bayesian spatial reconstruction for positron emission tomography”, in Proc. of 10th International meeting on fully three-dimensional image reconstruction in radiology and nuclear medicine, Pékin, Chine, 2009, p. 207-210.
  • M.D. Fall, E. Barat, C. Comtat, T. Dautremer, T. Montagu, A. Mohammad-Djafari, “A discrete-continuous Bayesian model for emission tomography”, in Proc. of IEEE International Conference on Image Processing, Bruxelles, Belgique, 2011. 

Dose calculation in proton therapy

protontherapieDescription

Used against tumours localised on the optic nerve, brain, backbone and others, the proton therapy requests a high level of dosimetric precision. The Monte Carlo transport code actually being developed at List includes a more precise and accurate calculation of efficient sections (physical interactions between protons and living tissues). This code is being compared to other codes and to experimental results obtained in a proton therapy clinical centre. Sharper than analytic codes, this one targets a few hours’ calculation time which is completely adapted to a clinical routine usage and will be implemented in a treatment planification software (TPS).

Applications

Proton therapy in clinical conditions treatment planification

  • ANR project PROUESSE (2010-2013) 
  • INSERM-Physicancer project DEDIPRO (2014-2017)