Split-filter DECT, sequential-spiral DECT and dual-source DECT all allow for the acquisition of DER to classify urinary stones.
Split-filter DECT provided higher DERs for uric acid stones, when compared with dual-source and sequential-spiral DECT, and lower DERs for calcified stones when compared with dual-source DECT (p < 0.001 for both), leading to a decreased accuracy for material differentiation. As reference standard, infrared spectroscopy was used to determine urinary stone composition.Īll three DECT techniques significantly differentiated between uric acid and calcium stones by attenuation values and DERs (p < 0.001 for all). Urinary stone categorization was based on dual energy ratio (DER) using an automated 3D segmentation. Scans were performed with a CTDIvol of 7.3mGy in all protocols. Additional DE scans were acquired at 80 and 140kVp (tin filter) with a dual-source CT scanner (SOMATOM Definition FLASH, Siemens Healthineers). Thirty-four urinary stones (volume 89.0 ± 77.4 mm³ 17 calcium stones, 17 uric acid stones) were scanned in a water-filled phantom using a split-filter equipped CT scanner (SOMATOM Definition Edge, Siemens Healthineers, Forchheim, Germany) in split-filter mode at 120kVp and sequential-spiral mode at 80 and 140kVp. All rights reserved.To assess accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from calcium urinary stones in dual-energy split filter vs sequential-spiral vs dual-source acquisition. Copyright © 2016 Wolters Kluwer Health, Inc. Noise texture deviation is a quantitative measure reflecting IR-specific artifacts and is reduced in CT images with ADMIRE compared with SAFIRE. Only lesion conspicuity was superior with SAFIRE and ADMIRE compared with filtered back projection (all, P < 0.001). Noise texture deviation in ADMIRE was reduced compared with corresponding strength levels of SAFIRE (all, P 0.05). Noise power spectra were similar at corresponding strength levels of SAFIRE and ADMIRE (all, P > 0.05). Image noise was significantly reduced at increasing IR levels ( P 0.05).
Two blinded readers evaluated all image data regarding IR-specific artifacts (plastic-like, blotchy appearance) patient data were evaluated regarding conspicuity and confidence for detecting hypodense liver lesions.
Noise power spectra and noise texture deviation were calculated in the phantom image noise was measured in the phantom and in patients. Images were reconstructed with filtered back projection, with the second-generation IR algorithm SAFIRE and with the third-generation IR algorithm ADMIRE. In the institutional review board–approved in vivo study part, 40 consecutive patients (mean age, 63 years) with hypodense liver lesions undergoing abdominal CT in the portal-venous phase were included. In the ex vivo study part, an abdominal phantom was used. The aims of this study were to introduce the measure noise texture deviation as quantitative parameter for evaluating iterative reconstruction (IR)–specific artifacts in computed tomography (CT) images and to test whether IR-specific artifacts, quantified through this measure, are reduced in advanced modeled IR (ADMIRE) as compared with sinogram-affirmed IR (SAFIRE) images of the liver ex vivo and in patients with hypodense liver lesions.