Thermal model for optimization of vascular laser tissue soldering

Laser tissue soldering (LTS) is a promising technique for tissue fusion based on a heat-denaturation process of proteins. Thermal damage of the fused tissue during the laser procedure has always been an important and challenging problem. Particularly in LTS of arterial blood vessels strong heating of the endothelium should be avoided to minimize the risk of thrombosis. A precise knowledge of the temperature distribution within the vessel wall during laser irradiation is inevitable. The authors developed a finite element model (FEM) to simulate the temperature distribution within blood vessels during LTS. Temperature measurements were used to verify and calibrate the model. Different parameters such as laser power, solder absorption coefficient, thickness of the solder layer, cooling of the vessel and continuous vs. pulsed energy deposition were tested to elucidate their impact on the temperature distribution within the soldering joint in order to reduce the amount of further animal experiments. A pulsed irradiation with high laser power and high absorbing solder yields the best results.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.