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Congress: ECR25
Poster Number: C-28044
Type: Poster: EPOS Radiologist (scientific)
Authorblock: S. Sauranen, T. Mäkelä, T. Kaasalainen, M. Kortesniemi; Helsinki/FI
Disclosures:
Sara Sauranen: Nothing to disclose
Teemu Mäkelä: Nothing to disclose
Touko Kaasalainen: Nothing to disclose
Mika Kortesniemi: Nothing to disclose
Keywords: Artificial Intelligence, Computer applications, Radiation physics, CT, Physics, Technology assessment, Quality assurance
References

[1] Diwakar M, Kumar M. A review on CT image noise and its denoising. Biomedical Signal Processing and Control. 2018;42:73-88. https://doi.org/10.1016/j.bspc.2018.01.010

[2] Mohammadinejad P, Mileto A, Yu L, Leng S, Guimaraes LS, Missert AD, Jensen CT, Gong H, McCollough CH, Fletcher JG. CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques RadioGraphics 2021;41(5):1493-1508. https://doi.org/10.1148/rg.2021200196

[3] Sadia RT, Chen J, Zhang J. CT image denoising methods for image quality improvement and radiation dose reduction. J Appl Clin Med Phys. 2024;25:e14270. https://doi.org/10.1002/acm2.14270

[4] ClariCT.AI – Unprecedented Image Clarity for Ultra-Low-Dose CT Scans. Accessed May 16, 2024. https://www.claripi.com/clarict-ai-2/

[5] Mäkelä T. Dual Energy CT QA Tool. Accessed February 5, 2025. https://github.com/tomakela/dectqatool
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