  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.
