Purpose: Dual-energy CT can readily differentiate uric acid (UA) renal stones from non-uric acid (NUA) elemental compositions using CT number ratio (CTR). However, classification of NUA subtypes is difficult because the CTR changes with patient size and acquisition protocol. In this work, we developed a generalizable, vendor neutral framework to estimate CTR for different types of stones and protocols, and validated the results on a dual-energy CT system.
Methods: Our model assumes generic x-ray spectra and estimates the added filtration to match measured HVLs at different kVs. Using known chemical composition and assumed patient attenuation, the CTR of 5 stone types were estimated for each tube potential setting. For validation, 75 human urinary stones were placed in anthropomorphic water phantoms with lateral dimensions of 35 cm and 50 cm and were scanned on a dual-source scanner at 90/Sn150 kV and 100/Sn150 kV, respectively. The CTDIvol used matches our routine clinical practice for each phantom size. Each stone was segmented using a quantitative stone analysis software (qSAS, version 1.4). The simulated CTR was compared with the mean CT number ratio for all stones included in the experimental data.
Results: The simulated CTRs for different renal stone types were consistent with the experimentally measured values, with average absolute errors of 6.2% and 2.5% for the 35 cm phantom and the 50 cm phantom respectively, and maximum error of 10%. Small positive bias in CTR estimations were observed in the smaller phantom results.
Conclusion: The developed framework is able to estimate renal stone CTRs for different compositions. Its potential for NUA subtype differentiation could improve on the existing tools to enable better clinical care. Moreover, the generalizability of the model allows for its application with other dual-energy CT systems and possible extension to clinical applications beyond renal stones.
Funding Support, Disclosures, and Conflict of Interest: Dr. McCollough receives industry grant support from Siemens Healthcare. All other authors have no conflicts of interest to disclose.