PURPOSE: To assess the non-inferiority of dual-layer spectral detector CT (SDCT) compared to dual-source dual-energy CT (dsDECT) in discriminating uric acid (UA) from non-UA stones. METHODS: Fifty-seven extracted urinary calculi were placed in a cylindrical phantom in a water bath and scanned on a SDCT scanner (IQon, Philips Healthcare) and second- and third-generation dsDECT scanners (Somatom Flash and Force, Siemens Healthcare) under matched scan parameters. For SDCT data, conventional images and virtual monoenergetic reconstructions were created. A customized 3D growing region segmentation tool was used to segment each stone on a pixel-by-pixel basis for statistical analysis. Median virtual monoenergetic ratios (VMRs) of 40/200, 62/92, and 62/100 for each stone were recorded. For dsDECT data, dual-energy ratio (DER) for each stone was recorded from vendor-specific postprocessing software (Syngo Via) using the Kidney Stones Application. The clinical reference standard of X-ray diffraction analysis was used to assess non-inferiority. Area under the receiver-operating characteristic curve (AUC) was used to assess diagnostic performance of detecting UA stones. RESULTS: Six pure UA, 47 pure calcium-based, 1 pure cystine, and 3 mixed struvite stones were scanned. All pure UA stones were correctly separated from non-UA stones using SDCT and dsDECT (AUC = 1). For UA stones, median VMR was 0.95-0.99 and DER 1.00-1.02. For non-UA stones, median VMR was 1.4-4.1 and DER 1.39-1.69. CONCLUSION: SDCT spectral reconstructions demonstrate similar performance to those of dsDECT in discriminating UA from non-UA stones in a phantom model.
PURPOSE: To assess the non-inferiority of dual-layer spectral detector CT (SDCT) compared to dual-source dual-energy CT (dsDECT) in discriminating uric acid (UA) from non-UA stones. METHODS: Fifty-seven extracted urinary calculi were placed in a cylindrical phantom in a water bath and scanned on a SDCT scanner (IQon, Philips Healthcare) and second- and third-generation dsDECT scanners (Somatom Flash and Force, Siemens Healthcare) under matched scan parameters. For SDCT data, conventional images and virtual monoenergetic reconstructions were created. A customized 3D growing region segmentation tool was used to segment each stone on a pixel-by-pixel basis for statistical analysis. Median virtual monoenergetic ratios (VMRs) of 40/200, 62/92, and 62/100 for each stone were recorded. For dsDECT data, dual-energy ratio (DER) for each stone was recorded from vendor-specific postprocessing software (Syngo Via) using the Kidney Stones Application. The clinical reference standard of X-ray diffraction analysis was used to assess non-inferiority. Area under the receiver-operating characteristic curve (AUC) was used to assess diagnostic performance of detecting UA stones. RESULTS: Six pure UA, 47 pure calcium-based, 1 pure cystine, and 3 mixed struvite stones were scanned. All pure UA stones were correctly separated from non-UA stones using SDCT and dsDECT (AUC = 1). For UA stones, median VMR was 0.95-0.99 and DER 1.00-1.02. For non-UA stones, median VMR was 1.4-4.1 and DER 1.39-1.69. CONCLUSION:SDCT spectral reconstructions demonstrate similar performance to those of dsDECT in discriminating UA from non-UA stones in a phantom model.
Entities:
Keywords:
Dual-energy CT; Material separation; Spectral CT; Uric acid; Urolithiasis
Authors: Vasiliki Chatzaraki; Alessandra Bolsi; Rahel A Kubik-Huch; Bernhard Schmidt; Antony John Lomax; Damien C Weber; Michael Thali; Tilo Niemann Journal: In Vivo Date: 2022 Mar-Apr Impact factor: 2.155
Authors: S C Brandelik; S Skornitzke; T Mokry; S Sauer; W Stiller; J Nattenmüller; H U Kauczor; T F Weber; T D Do Journal: Eur Radiol Date: 2021-03-30 Impact factor: 5.315