The use of artificial intelligence and deep learning reconstruction in urological computed tomography: Dose reduction at ghost level

Abdul Rauf; Saqib Javed; Bhargavi Chandrasekar; Saiful Miah; Margaret Lyttle; Mamoon Siraj; Rono Mukherjee; Christopher M McLeavy; Hazem Alaaraj; Richard Hawkins

doi:10.4103/ua.ua_73_23

The use of artificial intelligence and deep learning reconstruction in urological computed tomography: Dose reduction at ghost level

Urol Ann. 2023 Oct-Dec;15(4):417-423. doi: 10.4103/ua.ua_73_23. Epub 2023 Oct 20.

Authors

Affiliations

¹ Department of Urology, Addenbrookes Hospital, Cambridge University Hospitals, Cambridge, United Kingdom.
² Salford Royal NHS Foundation Trust, Stott Lane Salford M6 8HD, United Kingdom.
³ Department of Radiology, Liverpool University Hospitals, NHS Foundation Trust, Liverpool, United Kingdom.
⁴ Department of Radiology, Mid Cheshire Hospitals, NHS Foundation Trust, Middlewich Road, Crewe CW1 4QJ, United Kingdom.

Abstract

Objective: The objective of the study is to demonstrate that with the use of artificial intelligence (AI) in computed tomography (CT), radiation doses of CT kidney-ureter-bladder (KUB) and CT urogram (CTU) can be reduced to less than that of X-ray KUB and CT KUB, respectively, while maintaining the good image quality.

Materials and methods: We reviewed all CT KUBs (n = 121) performed in September 2019 and all CTUs (n = 74) performed in December 2019 at our institution. The dose length product (DLP) of all CT KUBs and each individual phase of CTU were recorded. DLP of each scan done with new scanner (Canon Aquilion One Genesis with AiCE [CAOG]) which uses AI and deep learning reconstruction (DLR) were compared against traditional non-AI scanner (GE OPTIMA 660 [GEO-660]). We also compared DLPs of both scanners against the United Kingdom, National Diagnostic Reference Levels (NDRL) for CT.

Results: One hundred and twenty-one patient's CT KUBs and 74 patient's CTUs were reviewed. For CT KUB group, the mean DLP of 81/121 scans done using AI/DLR scanner (CAOG) was 77.8 mGy cm (1.16 mSv), while the mean DLP of 40/121 CT KUB done with GEO-660 was 317.1 mGy cm (4.75 mSv). For CTU group, the mean DLP for 46/74 scans done using AI/DLR scanner (CAOG) was 401.9 mGy cm (6 mSv), compared to mean DLP of 1352.6 mGy cm (20.2 mSv) from GEO-660.

Conclusion: We propose that CT scanners using AI/DLR method have the potential of reducing radiation doses of CT KUB and CTU to such an extent that it heralds the extinction of plain film XR KUB for follow-up of urinary tract stones. To the best of our knowledge, this is the first study comparing CT KUB and CTU doses from new scanners utilizing AI/DLR technology with traditional scanners using hybrid iterative reconstruction technology. Moreover, we have shown that this technology can markedly reduce the cumulative radiation burden in all urological patients undergoing CT examinations, whether this is CT KUB or CTU.

Keywords: Artificial intelligence; computed tomography scan of kidney-ureter and bladder; computed tomography-urogram; deep learning reconstruction; hybrid iterative reconstruction national diagnostic reference levels.