[
[1] Sauter, A.P., Muenzel, D., Dangelmaier, J., Braren, R., Pfeiffer, F., Rummeny, E.J., Noël, P.B., Fingerle, A.A. (2018). Dual-layer spectral computed tomography: Virtual non-contrast in comparison to true non-contrast images. European Journal of Radiology, 104, 108-114. https://doi.org/10.1016/j.ejrad.2018.05.007
]Search in Google Scholar
[
[2] Hua, C.H., Shapira, N., Merchant, T.E., Klahr, P., Yagil, Y. (2018). Accuracy of electron density, effective atomic number, and iodine concentration determination with a dual-layer dual-energy computed tomography system. Medical Physics, 45 (6), 2486-2497. https://doi.org/10.1002/mp.12903
]Search in Google Scholar
[
[3] Si-Mohamed, S., Dupuis, N., Tatard-Leitman, V. et al. (2019). Virtual versus true non-contrast dual-energy CT imaging for the diagnosis of aortic intramural hematoma. European Radiology, 29, 6762-6771. https://doi.org/10.1007/s00330-019-06322-5
]Search in Google Scholar
[
[4] Fieselmann, A., Kowarschik, M., Ganguly, A., Hornegger, J., Fahrig, R. (2011). Deconvolution-based CT and MR brain perfusion measurement: Theoretical model revisited and practical implementation details. International Journal of Biomedical Imaging, 2011, 467563. https://doi.org/10.1155/2011/467563
]Search in Google Scholar
[
[5] Coche, E. (2019). Spectral CT Clinical Case Collection: Technical Aspects of Spectral CT. Philips Health System.
]Search in Google Scholar
[
[6] Murphy, A., Haouimi, A. (2020). Image reconstruction (CT). Radiopaedia.org. https://doi.org/10.53347/rID-51829
]Search in Google Scholar
[
[7] Klosowski, G., Rymarczyk, T., Kozlowski, E. (2019). Tomographic image correction with noise reduction algorithms. MATEC Web of Conferences, 252, 09001. https://doi.org/10.1051/matecconf/201925209001
]Search in Google Scholar
[
[8] Tan, L.-G., Xu, C., Wang, Y.-F., Wei, H.-N., Zhao, K., Song, S.-M. (2020). Gaussian recursive filter for nonlinear systems with finite-step correlated noises and packet dropout compensations. Measurement Science Review, 20 (2), 80-92. https://doi.org/10.2478/msr-2020-0011
]Search in Google Scholar
[
[9] Zou, X., Li, K., Pan, B. (2020). The effect of low-pass pre-filtering on subvoxel registration algorithms in digital volume correlation: A revisited study. Measurement Science Review, 20 (5), 202-209. https://doi.org/10.2478/msr-2020-0025
]Search in Google Scholar
[
[10] Andris, P., Frollo, I. (2020). Sensitivity analysis of the simply noise-matched receiving coil for NMR experiments. Measurement Science Review, 20 (5), 236-240. https://doi.org/10.2478/msr-2020-0030
]Search in Google Scholar
[
[11] Hsu, C.C.-T., Kwan, G.N.C., Singh, D., Pratap, J., Watkins, T.W. (2016). Principles and clinical application of dual-energy computed tomography in the evaluation of cerebrovascular disease. Journal of Clinical Imaging Science, 6 (27). https://doi.org/10.4103/2156-7514.185003
]Search in Google Scholar
[
[12] Nicolaou, S., Liang, T., Murphy, D.T., Korzan, J.R., Ouellette, H., Munk, P. (2012). Dual-energy CT: A promising new technique for assessment of the musculoskeletal system. American Journal of Roentgenology, 199 (5 Suppl), S78-S86. https://doi.org/10.2214/ajr.12.9117
]Search in Google Scholar
[
[13] Murphy, A., Worsley, C. (2021). Virtual non-contrast imaging. Radiopaedia.org. https://doi.org/10.53347/rID-66311
]Search in Google Scholar
[
[14] Ananthakrishnan, L., Rajiah, P., Ahn, R., Rassouli, N., Xi, Y., Soesbe, T.C., Lewis, M.A., Lenkinski, R.E., Leyendecker, J.R., Abbara, S. (2017). Spectral detector CT-derived virtual non-contrast images: Comparison of attenuation values with unenhanced CT. Abdominal Radiology, 42 (3), 702-709. https://doi.org/10.1007/s00261-016-1036-9
]Search in Google Scholar
[
[15] Toepker, M., Moritz, T., Krauss, B., Weber, M., Euller, G., Mang, T., Wolf, F., Herold, C.J., Ringl, H. (2012). Virtual non-contrast in second-generation, dual-energy computed tomography: Reliability of attenuation values. European Journal of Radiology, 81 (3), e398-e405. https://doi.org/10.1016/j.ejrad.2011.12.011
]Search in Google Scholar
[
[16] Yoo, S.Y., Kim, Y., Cho, H.H., Choi, M.J., Shim, S.S., Lee, J.K., Baek, S.Y. (2013). Dual-energy CT in the assessment of mediastinal lymph nodes: Comparative study of virtual non-contrast and true non-contrast images. Korean Journal of Radiology, 14 (3), 532-539. https://doi.org/10.3348/kjr.2013.14.3.532
]Search in Google Scholar
[
[17] Choi, W.-J., Choi, T.-S. (2013). Automated pulmonary nodule detection system in computed tomography images: A hierarchical block classification approach. Entropy, 15 (2), 507-523. https://doi.org/10.3390/e15020507
]Search in Google Scholar
[
[18] Bartusek, K., Gescheidtova, E., Mikulka, J. (2010). Data processing in studying biological tissues, using MR imaging techniques. In 33th International Conference on Telecommunications and Signal Processing, 171-175.
]Search in Google Scholar
[
[19] Mikulka, J., Burget, R., Říha, K., Gescheidtová, E. (2013). Segmentation of brain tumor parts in magnetic resonance images. In 2013 36th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 565-568. https://doi.org/10.1109/TSP.2013.6613997
]Search in Google Scholar
[
[20] Beigelman-Aubry, C., Hill, C., Guibal, A., Savatovsky, J., Grenier, P.A. (2005). Multi-detector row CT and postprocessing techniques in the assessment of diffuse lung disease. RadioGraphics, 25 (6), 1639-1652. https://doi.org/10.1148/rg.256055037
]Search in Google Scholar
[
[21] Flohr, T., Ohnesorge, B. (2007). Image visualization and post-processing techniques. In Multi-slice and Dual-source CT in Cardiac Imaging. Springer, 151-177. https://doi.org/10.1007/978-3-540-49546-8_6
]Search in Google Scholar
[
[22] Xiang, Z., Huang, F., Liang, C., Xu, X., Tan, L. (2008). Application of imaging postprocessing of spiral CT in the staging of lung cancer. The Chinese-German Journal of Clinical Oncology, 7 (5), 254-258. https://doi.org/10.1007/s10330-008-0026-y
]Search in Google Scholar
[
[23] Su, K.-H., Kuo, J.-W., Jordan, D.W. et al. (2018). Machine learning-based dual-energy CT parametric mapping. Physics in Medicine & Biology, 63 (12), 125001. https://doi.org/10.1088/1361-6560/aac711
]Search in Google Scholar
[
[24] Rassouli, N., Chalian, H., Rajiah, P., Dhanantwari, A., Landeras, L. (2017). Assessment of 70-keV virtual monoenergetic spectral images in abdominal CT imaging: A comparison study to conventional polychromatic 120-kVp images. Abdominal Radiology, 42, 2579-2586. https://doi.org/10.1007/s00261-017-1151-2
]Search in Google Scholar
[
[25] McCollough, C.H., Leng, S., Yu, L., Fletcher, J.G. (2015). Dual- and multi-energy CT: Principles, technical approaches, and clinical applications. Radiology, 276 (3), 637-653. https://doi.org/10.1148/radiol.2015142631
]Search in Google Scholar
[
[26] Padole, A., Singh, S., Lira, D., Blake, M.A., Pourjabbar, S., Khawaja, R.D.A., Choy, G., Saini, S., Do, S., Kalra, M.K. (2015). Assessment of filtered back projection, adaptive statistical, and model-based iterative reconstruction for reduced dose abdominal computed tomography. Journal of Computer Assisted Tomography, 39 (4), 462-467. https://doi.org/10.1097/rct.0000000000000231
]Search in Google Scholar
[
[27] Jamali, S., Michoux, N., Coche, E., Dragean, C.A. (2019). Virtual unenhanced phase with spectral dual-energy CT: Is it an alternative to conventional true unenhanced phase for abdominal tissues? Diagnostic and Interventional Imaging, 100 (9), 503-511. https://doi.org/10.1016/j.diii.2019.04.007
]Search in Google Scholar
[
[28] Lazar, M., Ringl, H., Baltzer, P. et al. (2020). Protocol analysis of dual-energy CT for optimization of kidney stone detection in virtual non-contrast reconstructions. European Radiology, 30, 4295-4305. https://doi.org/10.1007/s00330-020-06806-9
]Search in Google Scholar
