Renal Stone Assessment with Dual-Energy Multidetector CT and Advanced Postprocessing Techniques: Improved Characterization of Renal Stone Composition-Pilot Study

Purpose: To prospectively evaluate the capability of noninvasive, simultaneous dual-energy (DE) multidetector computed tomography (CT) to improve characterization of human renal calculi in an anthropomorphic DE renal phantom by introducing advanced postprocessing techniques, with ex vivo renal stone spectroscopy as the reference standard. Materials and Methods: Fifty renal calculi were assessed: Thirty stones were of pure crystalline composition (uric acid, cystine, struvite, calcium oxalate, calcium phosphate, brushite), and 20 were of polycrystalline composition. DE CT was performed with a 64-detector CT unit. A postprocessing algorithm (DECTSlope) was proposed as a pixel-by-pixel approach to generate Digital Imaging and Communications in Medicine dataset gray-scale-encoding ratios of relative differences in attenuation values of low- and high-energy DE CT. Graphic analysis, in which clusters of equal composition were identified, was performed by sorting attenuation values of color composition-encoded calculi in an ascending sequence. Multivariate general linear model analysis was used to determine level of significance to differentiate composition on native and postprocessed DE CT images. Results: Graphic analysis of native DE CT images was used to identify clusters for uric acid (453-629 HU for low- energy CT, 443-615 HU for high-energy CT), cystine (725-832 HU for low- energy CT, 513-747 HU for high-energy CT), and struvite (1337-1530 HU for low- energy CT, 1007-1100 HU for high-energy CT) stones; high-energy clusters showed attenuation value overlap. Polycrystalline calcium oxalate and calcium phosphate calculi were found throughout the entire spectrum, and dense brushite had attenuation values of more than 1500 HU for low- energy CT and more than 1100 HU for high-energy CT. The DE CT algorithm was used to generate specific identifiers for uric acid (77-80 U-Slope, one outlier), cystine (70-71 U-Slope), struvite (56-60 U-Slope), calcium oxalate and calcium phosphate (17-59 USlope), and brushite (4-15 U-Slope) stones. Statistical analysis showed that all compositions were identified unambiguously with the DECTSlope algorithm. Conclusion: DE multidetector CT with advanced postprocessing techniques improves characterization of renal stone composition beyond that achieved with single-energy multidetector CT acquisitions with basic attenuation assessment. (c) RSNA, 2009