Brenner DJ, Hall EJ. Computed tomography- An increasing source of radiation exposure. N Engl J Med. 2007;357:2277–84.
Article CAS PubMed Google Scholar
Elhamiasl M, Nuyts J. Low-dose x-ray CT simulation from an available higher-dose scan. Phys Med Biol. 2020;65: 135010.
Kim CW, Kim JH. Realistic simulation of reduced-dose CT with noise modelling and sonogram synthesis using DICOM CT images. Med Phys. 2014;41:011901–11.
Kalendar WA, Wolf H, Suess C, et al. Dose reduction in CT by on-line tube current control: principles and validation on phantoms and cadavers. Eur Radiol. 1999;9:323–8.
Singh S, Kalra MK, Moore MA, et al. Dose reduction and compliance with pediatric CT protocols adapted to patient size, clinical indication and number of prior studies. Radiology. 2009;252:200–8.
Karmazyn B, Frush DP, Applegate KE, et al. CT with a computer-simulated dose reduction technique for detection of pediatric nephroureterolithiasis: comparison of standard and reduced radiation doses. Am J Roentgenol. 2009;192:143–9.
Guimaraes LS, Fletcher JG, Harmsen WS, et al. Appropriate patient selection at abdominal dual-energy CT using 80 kV: relationship between patient size, image noise and image quality. Radiology. 2010;257:732–42.
Riederer SJ, Pelc NJ, Chesler DA. The noise power spectrum in computed X-ray tomography. Phys Med Biol. 1978;23:446–54.
Article CAS PubMed Google Scholar
Hanson KM. Detectability in computed tomographic images. Med Phys. 1979;6:441–51.
Article CAS PubMed Google Scholar
Kijewski MF, Judy PF. The noise power spectrum of CT images. Phys Med Biol. 1987;32:565–75.
Article CAS PubMed Google Scholar
Hsieh J. Nonstationary noise characteristics of the helical scan and its impact on image quality and artifacts. Med Phys. 1997;24:1375–84.
Article CAS PubMed Google Scholar
Wunderlich A, Noo F. Image covariance and lesion detectability in direct fan-beam X-ray computed tomography. Phys Med Biol. 2008;53:2471–93.
Article PubMed PubMed Central Google Scholar
Baek J, Pelc NJ. The noise power spectrum in CT with direct fan beam reconstruction. Med Phys. 2010;37:2074–81.
Article PubMed PubMed Central Google Scholar
Frush DP, Slack CC, Hollingsworth CL, et al. Computer-simulated radiation dose reduction for abdominal multidetector CT of pediatric patients. Am J Roentgenol. 2002;179:1107–13.
Kalendar WA, Buchenau S, Deak P, et al. Technical approaches to the optimisation of CT. Physica Med. 2008;24:71–9.
Hanson KM. Spectral analysis of non-stationary CT noise, International Symposium and Course on Computed Tomography, Los Alamos Scientific Laboratory, Las Vagas, [Los Alamos National Laboratory Web Site]. April 9, 1980. Available at https://kmh-lanl.hansonhub.com/talks/ct80.abs.html. Accessed August 31, 2021.17.
Britten AJ, Crotty M, Kiremidjian A, et al. The addition of computer simulated noise to investigate radiation dose and image quality in images with spatial correlation of statistical noise: an example application to X-ray CT of the brain. Br J Radiol. 2004;77:323–238.
Article CAS PubMed Google Scholar
Massoumzadeh P, Don S, Hildebolt CF, et al. Validation of CT dose-reduction simulation. Med Phys. 2009;36:174–89.
Joemai RM, Geleijns J, Veldkamp WJ. Development and validation of a low dose simulator for computed tomography. Eur Radiol. 2010;20:958–66.
Article CAS PubMed Google Scholar
Yu L, Shiung M, Jondal D, et al. Development and validation of a practical lower-dose-simulation tool for optimizing computed tomography scan protocols. J Comput Assist Tomogr. 2012;36:477–87.
Zabic S, Wang Q, Morton T, et al. A low-dose simulation tool for CT systems with energy integrating detectors. Med Phys. 2013;40: 031102.
Zeng D, Huang J, Bian Z, et al. A simple low-dose x-ray CT simulation from high-dose scan. IEEE Trans Nucl Sci. 2015;65:2226–33.
Takenaga T, Katsuragawa S, Goto M, et al. A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study. Radiol Phys Technol. 2016;9:44–52.
Naziroglu RE, van Ravesteijn VF, van Vliet LJ, et al. Simulation of scanner-and patient-specific low-dose CT imaging from existing CT images. Phys Medica. 2017;36:12–23.
Hsieh J. Computed tomography principles, design, artefacts and recent advances. Bellingham, Washington, USA: SPIE Press; 2003.
Bracewell RN, Imaging T-D. Englewood Cliffs. NJ: Prentice Hall; 1995. p. 505–37.
Lim JS, Signal T-D, Processing I. Englewood Cliffs. NJ: Prentice Hall; 1990. p. 42–5.
Boone JM, Brink JA, et al. Radiation dose and image-quality assessment in computed tomography, journ. ICRU. 2012;12:121–34.
Lathi BP. Modern Digital and Analog Communication Systems. New York: Holt Saunders; 1983.
Platten D, Understanding Imaging Performance (3); Artefacts, ImPACT course, [ImPACT CT scanner evaluation group Web Site]. Oct 2005. Available at http://www.impactscan.org/slides/impactcourse/artefacts/index.html. Accessed August 31, 2021.
Yang W, Zhang JY, Wu J, et al. Improving low-dose CT image using residual convolutional network. IEEE Spec Sect Adv Sign Process Methods Med Imag. 2017;5:24698–705.
Han Y, Framing YJC. U-Net via deep convolutional framelets: Application to sparse-view CT. IEEE Trans Med Imag. 2018;37:1418–29.
Liu B, Liu J. Overview of Image Denoising Based on Deep Learning. J Phys Conf Ser. 2019;1176:22010.
留言 (0)