[1]    Willemink M, Leiner T, de Jong PA, et al. Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality. 2013 European Radiology 23(6): 1632-1642. https://doi.org/10.1007/s00330-012-2764-z.

[2]    McCollough CH, Chen GH, Kalender W, et al.  Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. 2012 Radiology  264(2):567-580. https://pubs.rsna.org/doi/pdf/10.1148/radiol.12112265.

[3]    Solomon J, Wilson J, Samei E. Characteristic image quality of a third generation ual-source MDCT scanner: Noise, resolution, and detectability. 2015 Medical Physics 42(8):4941-4953. https://doi.org/10.1118/1.4923172.

[4]    Solomon J, Samei E. Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. 2014 Medical Physics 41(9):091908-1-091908-12.https://doi.org/10.1118/1.4893497.

[5]    Verdun FR, Racine D, Ott JG, et al. Image quality in CT: From physical measurement to model obervers. 2015 Physica Medica 31(8): 823-843. https://doi.org/10.1016/j.ejmp.2015.08.007.

[6]    Löve AO, Siemund ML, Stålhammar R, et al. Six iterative reconstruction algorithms in brain CT- A phantom study on image quality at different radiation doses. 2013 The British journal of radiology 86(1031): . https://doi.org/10.1259/bjr.20130388.

[7]    Morsbach F, Desbiolles L, Raupach R, et al. Noise Texture Deviation. A measure for quantifying artifacts in computed tomography images with iterative reconstructions. 2017 Investigative Radiology 52(2):87-92. DOI: 10.1097/RLI.0000000000000312

[8]    Dolly S, Chen H-C, Anastasio M, et al. Practical considerations for noise power spectra estimation for clinical CT scanners. 2016 Journal of Applied Clinical Medical Physics 17:392–407. http://dx.doi.org/10.1120/jacmp.v17i3.5841.

[9]    Siewerdsen JH, Cunningham IA, Jaffray DA. A framework for noise-power spectrum analysis of multidimensional images. 2002 Medical Physics 29(11):2655–2671.

          http://dx.doi.org/10.1118/1.1513158.

[10] Mieville FA, Bolard G, Bulling S, et al. Effects of computing parameters and measurement locations on the estimation of 3D NPS in non-stationary MDCT images. 2013 Physica Medica 29(6):684-694. http://dx.doi.org/10.1016/j.ejmp.2012.07.001.

[11]  Paruccini N, Villa R, Pasquali C, et al. Evaluation of a commercial Model Based Iterative reconstruction algorithm in computed tomography. 2017 Physica Medica 41:58-70. http://dx.doi.org/10.1016/j.ejmp.2017.05.066

[12]  Spadavecchia C, Villa R, Pasquali C, et al. A statistical method for low contrast detectability assessment in digital mammography. In A. Tingberg et al. (Eds.): IWDM 2016, LNCS 9699:532–539. DOI: 10.1007/978-3-319-41546-8 67

[13] Ott JG, Becce F, Monnin P, Schmidt S, Bochud FO, Verdun FR. Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms. 2014 Physics in Medicine & Biology 59(15).

[14] Morsbach F, Desbiolles L, Raupach R, Leschka S, Schmidt B, Alkadhi H. Noise Texture Deviation: A Measure for Quantifying Artifacts in Computed Tomography Images With Iterative Reconstructions. 2017 Investigative Radiology 52(2):87–94