From ERCIM Working Group IM2IM
Jump to: navigation, search

Context & Goals

As medical and surgical operations tend to involve precise and complex techniques, doctors yearn more and more for simulation tools with which to train themselves, plan operations and ensure detailed follow-up on the state of their patients. High precision medicine and surgery, with as little invasion as possible, are required to improve patient comfort and reduce hospitalization costs.

Many operations are supposed to be minimally-invasive, safe, and cheap procedures.

Interventional medicine consists in placing a small effective and reliable medical device inside a blood vessel or any other anatomical canal. Such an endoluminal catheter-based therapy is used, for example, for a high-degree stenosis, that is to say a pathological narrowing of the arterial lumen of at least 70 %. The medical implantable device used for such a constriction is the so-called stent. A stent is a cylindrical metallic truss supplied on a catheter. The catheter is placed into the arterial tree in order to advance the pre-mounted stent across the obstruction. Once on site, a memory-alloy stent expands, whereas a pre-mounted balloon stent is delivered by inflating balloon to artery-lumen physiological size. The stent is set into place in order to keep the canal open. During such operations, the operator is assisted by imaging systems that display all gestures on screen in real time. Similarly, more and more often, the operation makes use of miniaturized robotized instruments.

Applied mathematics and computer science come into play in many ways in these advances in medical techniques. Image processing, computer graphics, and virtual reality, modeling and simulation of the behavior of biological tissues and robotics are all involved.

New operational techniques demand acquisition of new gestures, as screen interface does not provide a direct, three-dimensional view. Computational tools aim at learning gestures, plan them, train, operate, or follow-up the post-operation evolution. However, one of the main difficulties in achieving a sufficiently realistic reproduction not only of the visual aspects, but also of the tactile aspects of the situation, is to correctly model the mechanical behavior of the various involved organs and anatomical tissues. Knowledge and data is still lacking, especially rheological properties and, at the nanoscale, kinetic and transport coefficients that govern drug delivery and biochemical processes. Overcoming these difficulties is an important challenge.

Common objective of computer-aided medicine tools are:

1. to visualize the diseased region and its surroundings;

2. to model and simulate physiological and pathological processes at lowest possible cost;

3. to give a pretherapeutic training of device travel and deployment;

4. to optimize treatment strategy, thereby minimizing the risk of complications;

5. to project the long term evolution (prognosis tool).


Development of computer-aided medical tools requires several tasks:

1. three-dimensional reconstruction of the region of interest from medical images and computational mesh generation;

2. input data collection and control parameter selection;

3. coupling of interacting, reacting soft tissues and medical device;

4. setting of appropriate boundary conditions;

5. error estimation and physics-based dynamical mesh adaptation;

6. to speed up the computational time;

7. result processing and physical entity field visualisation;

8. validation.