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ERC

RealTCut Report Summary

Project ID: 279578
Funded under: FP7-IDEAS-ERC
Country: Luxembourg

Final Report Summary - REALTCUT (Towards real time multiscale simulation of cutting in non-linear materials with applications to surgical simulation and computer guided surgery)

The aim of RealTCut was to develop some of the fundamental building blocks necessary to build new computer simulators with which surgeons can train to perform operations more safely, rehearse complicated procedures, or to guide them during surgery. The challenges associated with achieving this are:
Human organs have complex geometries: those need to be represented in the computer, as accurately
as possible from medical images, with minimal intervention of the user.
Simulations must be real time: at least they must be completed within clinical time scales. For operations such as cutting, tearing and needle insertion, this is difficult to achieve without sacrificing accuracy.
Numerical methods are not exact: it is thus critical to provide quality control and error estimates to the surgeon so that they can assess the realism of the simulation.
The behaviour of human tissue varies greatly: it is critical to provide surgeons with computational tools enabling them to assess the effects of uncertainties in the models they use and the parameters they choose. If the uncertainty is too large, surgeons should be given as much information as possible to decide what tests or medical examinations would increase their confidence in the simulation results.
Information on human tissue is limited: in particular, it is either impossible or unethical to perform experiments on human tissue, so that the behaviour of such materials is generally discovered during the operation/treatment itself, making impossible predictions from models which would have been derived a priori.
Thanks to RealTCut (pronounced Reality Cut), we have now developed some fundamental tools to help overcome these major challenges. From medical images, and within a fully open and free software platform, researchers and practitioners are now able to generate models in a simple and effective way, which can then be used by the patient.
Using these geometrical models, we are now able to simulate in a computer, in real time RealT(ime)
and with error control (realistically, RealT pronounced reality) the interaction of a surgeon or interventional radiologist with heterogeneous soft tissues whilst controlling the error committed during the simulation. These methods are exciting advances because they can, in particular conditions, provide the surgeon with estimates of the reliability and quality of these computer simulations, as they incise soft tissue or insert needles.
Given specific experimental data on a patient, the user can select the most adequate model which best fits the patient. Given this model the user is able to quantify the importance of each of the model parameters on quantities that she/he is interested in. This provides the user with the required building blocks required to control the uncertainties in the predictions given by the model and has the potential to provide surgeons confidence intervals or trust regions on quantities of medical interest.
The team will now work on the acceleration of some of the algorithms proposed above, to enable them to be used during an actual intervention. They will generalise their approach to more general settings, in particular with respect to the types and complexity of the models being used. They will follow, in particular an exciting direction of work which lies in the design of computational approaches to identify the material properties of patients in real time, through live data feed and extend the work performed during RealTCut for medical diagnostics.
To facilitate uptake by future users, the team will develop user-friendly pieces of software for surgeons and clinical experts to give feedback on the design of the simulators.

Reported by

UNIVERSITE DU LUXEMBOURG
Luxembourg
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