The macroscopic mechanical behaviour of granular materials is governed by microscopic features at the particle scale. Photoelasticimetry is a powerful method for measuring shear stresses in particles made from birefringent materials. As a complementary method, we here identify the hydrostatic stress networks through thermoelastic stress analysis using infrared thermographic measurements. Experiments are performed on two-dimensional cohesionless monodisperse granular materials composed of about 1200 cylinders comprising two constitutive materials. We show that the experimental hydrostatic stress distributions follow statistical laws which are in agreement with simulations performed using molecular dynamics, except in one case exhibiting piecewise periodic stacking. Polydisperse cases are then processed. The measurement of hydrostatic stress networks using this technique opens new prospects for the analysis of granular materials.

The accuracy of biopsy sampling and the related tumor localization are major issues for prostate cancer diagnosis and therapy. However, the ability to navigate accurately to biopsy targets faces several difficulties coming from both transrectal ultrasound (TRUS) image guidance properties and prostate motion or deformation. To reduce inaccuracy and exam duration, the main objective of this study is to develop a real-time navigation assistance. The aim is to provide the current probe position and orientation with respect to the deformable organ and the next biopsy targets. We propose a deep learning real-time 2D/3D registration method based on Convolutional Neural Networks (CNN) to localize the current 2D US image relative to the available 3D TRUS reference volume. We experiment several scenarii combining different input data including: pair of successive 2D US images, the optical flow between them and current probe tracking information. The main novelty of our study is to consider prior navigation trajectory information by introducing previous registration result. This model is evaluated on clinical data through simulated biopsy trajectories. The results highlight significant improvement by exploiting trajectory information especially through prior registration results and probe tracking parameters. With such trajectory information, we achieve an average registration error of 2.21 mm ± 2.89. The network demonstrates efficient generalization capabilities on new patients and new trajectories, which is promising for successful continuous tracking during biopsy procedure.