Urinary Stones Clustering on each layer based on Hounsfield Units Values from Micro CT-SkyScan Images

Abstract

Abstract The appropriate treatment method for the patient with urinary stones can be determined from the information of the mineral composition of urinary stones. The prediction of the stone type could improve the selection of the interventional modalities. The study aimed to determine the type of the urinary stone for each layer based on the value of Housefield Units (HU) from micro CT-SkyScan images. Five samples were cleaned with 75% alcohol and distilled water. Micro skyScan 1173 was used to scan urinary stones with applied current and voltage of 66 mA and 120 kV respectively. NRecon software was used to reconstruct the projected image. Region of Interest (ROI) was set at each layer and analyzed both qualitatively and quantitatively. The determination of chemical constituents of stones/fragments was performed using Energy Dispersive X-Ray Spectroscopy. The chemical compositions of calcium oxalate monohydrate, struvite, a mix of calcium oxalate and calcium phosphate, a mix of calcium oxalate and struvite and calcium phosphate, uric acid were accurately identified based on the micro SkyScan images with the mean HU. Micro SkyScan images could predict the chemical composition for each layer of urinary stones. However, more samples are needed for clustering various types of urinary stones based on HU value.