ArticleUrolithiasis2023
Optimal dual-energy computed tomography scan parameters to detect small-sized urinary stones and their composition.
Article in Urolithiasis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 1 citations in OpenAlex.
- Artificial intelligence model based on CT imaging for predicting infected upper urinary tract calculi.Frontiers in surgery · 2026Article
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6 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
To investigate the optimal scanning parameters of dual-energy computed tomography (DECT), which can accurately determine sensitivity (the detectability of urinary stones) and accuracy (the composition matching of urinary stones), and to apply them to clinical trials. Fifteen urinary stones were chemically analyzed, and their chemical compositions were considered a reference standard with which we compared the uric acid (UA) and non-UA compositions determined using DECT. The urinary stones were placed inside a bolus and scanned with a dual-source CT scanner under various selected dual-energy conditions (A to X) using various solid water phantom thicknesses. These datasets were analyzed using the Siemens syngo.via software tool (integrated into the CT system) for matching the sensitivity and accuracy assessments. This study showed that 80% of the highest sensitivity (detection of urinary stones) and 92% of the highest accuracy (composition matching of urinary stones) were achieved under condition A (a collimation beam width setting of 2 × 32 × 0.6 mm, an automatic exposure control setting of 80/sn140 peak kilovoltage, and a slice thickness of 0.5/0.5 mm) (P < 0.05). Application of the DECT energy parameters presented in the study will help identify the sensitivity and accuracy of UA and non-UA stone analysis, even in patients with small-sized urinary stones and in conditions difficult for analysis.
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