(Wednesday, 7/15/2020) 1:00 PM - 2:00 PM [Eastern Time (GMT-4)]
Room: Track 1
Purpose: Unenhanced x-ray computed tomography (CT) is the imaging modality of choice for patients presenting to the emergency department with acute flank pain. In patients with a history of renal stones, low radiation dose protocols are sufficient to detect and quantify newly symptomatic stones. In this work, we investigated the potential for further radiation dose reduction in renal stone imaging by combining photon-counting-detector CT (PCD-CT) with an additional 0.4 mm tin (Sn) filter.
Methods: 25 stones of known composition were embedded in porcine gelatin and placed inside an anthropomorphic water phantom to model a medium-large patient (45 cm lateral width). The phantom was scanned on a commercial CT scanner (Somatom Flash) equipped with energy integrating detectors (EID), using the routine stone protocol in our institution (120kV, CTDIvol=17 mGy). The phantom was also scanned on the PCD-CT using 100kV and the Sn filter at 75%, 50% and 25% of the routine dose, respectively. Reconstruction parameters were identical for all scans. Each dataset was processed by an in-house stone analysis tool (qSAS1.3, Mayo Clinic) to detect and quantify each stone. Stone-CNR was computed for uric acid (UA, N=10) and calcium oxalate stones (CaOx, N=10) as the ratio of the average CT number of the stone and the noise in an adjacent region, and used as a surrogate metric for stone detectability.
Results: An increase in stone CNR was measured for both stone types when 75% of the routine dose was used. Up to 35% dose reduction was shown to be feasible regardless of stone composition, without negatively impacting the stone detection performance.
Conclusion: Phantom studies demonstrated that more than 30% dose reduction can be achieved for renal stone imaging by using PCD-CT with 100 kV and an additional Sn filter, compared to current clinical EID-CT, without compromising detectability performance.
Funding Support, Disclosures, and Conflict of Interest: This project was supported by grant numbers EB028591 and EB016966 from the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health. Dr. Cynthia McCollough receives research support from Siemens Healthcare
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