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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
Purpose Image noise is a distractor in radiological image reading and may hide subtle details especially at low contrast levels. Thus, image noise magnitude is a key descriptor of image quality. In computed tomography (CT), the magnitude of image noise is typically measured as the standard deviation of CT numbers in a uniform image area. Lowering noise is among the general targets when optimizing image quality. In addition to traditional and well-known conventional and dual-energy CT (DECT) noise reduction methods, many...
Read more Methods and materials A DECT phantom (Multi-Energy CT Phantom, Sun Nuclear Corporation, Melbourne, USA) was scanned using dual source CT (Somatom Force, Siemens Healthineers, Erlangen, Germany) with 90 kVp/Sn150 kVp and automatic tube current modulation at seven dose levels and with three repeats, resulting in 21 DECT images in total. The targeted dose levels were 1.8 (scanner minimum), 2.0, 4.0, 8.0, 16, 24 and 32 mGy; the realized doses were slightly lower. Aside from the dose levels, the DECT quality control scan protocol...
Read more Results The noise levels in all targets and maps decreased significantly (p < 0.05), by a mean reduction of 28 HU, in the two AI noise-reduced image sets compared to the images with no AI noise reduction. Noise reduction was greater, by 6 HU on average, in images in which AI noise reduction was applied to the X-ray tube A and B data than when it was applied to the DECT maps with a few exceptions across specific phantom targets and...
Read more Conclusion AI noise reduction and the timing of its application during post-processing had a significant impact on the contrast and noise of DECT maps. The DECT maps with AI noise reduction applied to X-ray tube A and B data showed the lowest noise levels. In addition to noise reduction, the contrast levels were slightly changed. An example of the three post-processing versions of the same image is shown in Figure 4.   [fig 4] Overall, the AI noise reduction was effective for phantom...
Read more 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...
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