Abstract

Our purpose is to provide an augmented reality system for Radio-Frequency guidance that could superimpose a 3D model of the liver, its vessels and tumors (reconstructed from CT images) on external video images of the patient. In this paper, we point out that clinical us- ability not only need the best affordable registration accuracy, but also a certification that the required accuracy is met, since clinical condi- tions change from one intervention to the other. Beginning by address- ing accuracy performances, we show that a 3D/2D registration based on radio-opaque fiducials is more adapted to our application constraints than other methods. Then, we outline a lack in their statistical assump- tions which leads us to the derivation of a new extended 3D/2D criterion. Careful validation experiments on real data show that an accuracy of 2 mm can be achieved in clinically relevant conditions, and that our new criterion is up to 9% more accurate, while keeping a computation time compatible with real-time at 20 to 40 Hz. After the ful?llment of our statistical hypotheses, we turn to safety issues. Propagating the data noise through both our criterion and the classical one, we obtain an explicit formulation of the registration error. As the real conditions do not always fit the theory, it is critical to validate our prediction with real data. Thus, we perform a rigorous incremental validation of each assumption using successively: synthetic data, real video images of a precisely known ob ject, and finally real CT and video images of a soft phantom. Results point out that our error prediction is fully valid in our application range. Eventually, we provide an accurate Augmented Reality guidance system that allows the automatic detection of potentially inaccurate guidance.