Estimation of urinary stone composition by automated processing of CT images

The objective of this article was developing an automated tool for routine clinical practice to estimate urinary stone composition from CT images based on the density of all constituent voxels. A total of 118 stones for which the composition had been determined by infrared spectroscopy were placed in a helical CT scanner. A standard acquisition, low-dose and high-dose acquisitions were performed. All voxels constituting each stone were automatically selected. A dissimilarity index evaluating variations of density around each voxel was created in order to minimize partial volume effects: stone composition was established on the basis of voxel density of homogeneous zones. Stone composition was determined in 52% of cases. Sensitivities for each compound were: uric acid: 65%, struvite: 19%, cystine: 78%, carbapatite: 33.5%, calcium oxalate dihydrate: 57%, calcium oxalate monohydrate: 66.5%, brushite: 75%. Low-dose acquisition did not lower the performances (P < 0.05). This entirely automated approach eliminates manual intervention on the images by the radiologist while providing identical performances including for low-dose protocols

Intérêt du guidage 3D et de la localisation des biopsies de prostate par voie endorectale

International audienceLa réalisation de biopsies de prostate, le plus souvent par voie endorectale, est primordiale pour le diagnostic et l'évaluation du pronostic du cancer. La précision de la localisation des biopsies est sujette à caution. Le développement de systèmes informatiques permet d'enregistrer avec précision leur localisation et de les guider pour améliorer leur distribution. Ces mêmes dispositifs permettent de fusionner images échographiques et images IRM et de fusionner différentes séries de biopsies. L'ensemble de ces données pourrait être transféré dans les systèmes échographiques de type HIFU dans le but de rendre les protocoles de traitements focalisés plus précis