McCollough CH, Leng S, Yu L, Fletcher JG (2015) Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 276:637–653

Article  Google Scholar 

Botsikas D, Triponez F, Boudabbous S, Hansen C, Becker CD, Montet X (2014) Incidental adrenal lesions detected on enhanced abdominal dual-energy CT: can the diagnostic workup be shortened by the implementation of virtual unenhanced images? Eur J Radiol 83:1746–1751

Article  Google Scholar 

Xu JJ, Taudorf M, Ulriksen PS et al (2020) Gastrointestinal applications of iodine quantification using dual-energy CT: a systematic review. Diagnostics 10:814

CAS  Article  Google Scholar 

Hyodo T, Yada N, Hori M et al (2017) Multimaterial decomposition algorithm for the quantification of liver fat content by using fast-kilovolt-peak switching dual-energy CT: clinical evaluation. Radiology 283:108–118

Article  Google Scholar 

Lourenco PDM, Rawski R, Mohammed MF, Khosa F, Nicolaou S, McLaughlin P (2018) Dual-energy CT iodine mapping and 40-keV monoenergetic applications in the diagnosis of acute bowel ischemia. AJR Am J Roentgenol 211:564–570.

Article  Google Scholar 

Albrecht MH, Vogl TJ, Martin SS et al (2019) Review of clinical applications for virtual monoenergetic dual-energy CT. Radiology 293:260–271

Article  Google Scholar 

Husarik DB, Gordic S, Desbiolles L et al (2015) Advanced virtual monoenergetic computed tomography of hyperattenuating and hypoattenuating liver lesions: ex-vivo and patient experience in various body sizes. Invest Radiol 50:695–702

Article  Google Scholar 

De Cecco CN, Caruso D, Schoepf UJ et al (2018) A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions. Eur Radiol 28:3393–3404

Article  Google Scholar 

Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357

Article  Google Scholar 

Padole A, Ali Khawaja RD, Kalra MK, Singh S (2015) CT radiation dose and iterative reconstruction techniques. AJR Am J Roentgenol 204:W384–W392

Article  Google Scholar 

Chen L-H, Jin C, Li J-Y et al (2018) Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT. Br J Radiol 91:20170655

Article  Google Scholar 

Ren Z, Zhang X, Hu Z et al (2019) Application of adaptive statistical iterative reconstruction-V with combination of 80 kV for reducing radiation dose and improving image quality in renal computed tomography angiography for slim patients. Acad Radiol 26:e324–e332

Article  Google Scholar 

McCollough CH, Yu L, Kofler JM et al (2015) Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology 276:499–506

Article  Google Scholar 

Hsieh J, Liu E, Nett B, Tang J, Thibault JB, Sahney S (2019) A new era of image reconstruction: TrueFidelity TM Technical white paper on deep learning image reconstruction. Available via https://www.gehealthcare.com/-/jssmedia/040dd213fa89463287155151fdb01922.pdf

Google Scholar 

Sakabe D, Funama Y, Taguchi K et al (2018) Image quality characteristics for virtual monoenergetic images using dual-layer spectral detector CT: comparison with conventional tube-voltage images. Phys Med 49:5–10

Fernandez-Velilla Cepria E, González-Ballester MÁ, Quera Jordana J et al (2021) Determination of the optimal range for virtual monoenergetic images in dual-energy CT based on physical quality parameters. Med Phys 48:5085–5095

Article  Google Scholar 

Noda Y, Kawai N, Nagata S et al (2021) Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration. Eur Radiol. https://doi.org/10.1007/s00330-021-08121-3

Park C, Choo KS, Jung Y, Jeong HS, Hwang JY, Yun MS (2021) CT iterative vs deep learning reconstruction: comparison of noise and sharpness. Eur Radiol 31:3156–3164.

Article  Google Scholar 

Likert R (1932) A technique for the measurement of attitudes. Arch Psychol 22 140:55

Nam JG, Hong JH, Kim DS, Oh J, Goo JM (2021) Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Eur Radiol 31:5533–5543.

Article  Google Scholar 

Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH (2021) Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise. Korean J Radiol 22:131–138.

Article  Google Scholar 

Brown H, Prescott R (2015), Applied mixed models in medicine. Statistics in Practice, 3 edn, John Wiley & Sons Inc. C. https://doi.org/10.1002/978111877821

Cao L, Liu X, Li J et al (2021) A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions. Br J Radiol 94:20201086

Article  Google Scholar 

Njølstad T, Schulz A, Godt JC et al (2021) Improved image quality in abdominal computed tomography reconstructed with a novel deep learning image reconstruction technique - initial clinical experience. Acta Radiol open 10:20584601211008390–20584601211008390

Google Scholar 

Martin SS, Pfeifer S, Wichmann JL et al (2017) Noise-optimized virtual monoenergetic dual-energy computed tomography: optimization of kiloelectron volt settings in patients with gastrointestinal stromal tumors. Abdom Radiol (NY) 42:718–726

Lam S, Gupta R, Levental M, Yu E, Curtin HD, Forghani R (2015) Optimal virtual monochromatic images for evaluation of normal tissues and head and neck cancer using dual-energy CT. AJNR Am J Neuroradiol 36:1518 LP–1524

Majeed NF, Ali SM, Therrien J, Wald C, Wortman JR (2021) Virtual monoenergetic spectral detector CT for preoperative ct angiography in liver donors. Curr Probl Diagn Radiol. https://doi.org/10.1067/j.cpradiol.2021.10.001

Park J, Kim SH, Han JK (2019) Combined application of virtual monoenergetic high keV images and the orthopedic metal artifact reduction algorithm (O-MAR): effect on image quality. Abdom Radiol (NY) 44:756–765

Shao Y-H, Tsai K, Kim S, Wu YJ, Demissie K (2019) Exposure to tomographic scans and cancer risks. JNCI Cancer Spectr 4:pkz072

Sun J, Li H, Wang B et al (2021) Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection. BMC Med Imaging 21:108

Article  Google Scholar 

Yoshida R, Usui K, Katsunuma Y, Honda H, Hatakeyama K (2020) Reducing contrast dose using virtual monoenergetic imaging for aortic CTA. J Appl Clin Med Phys 21:272–277.

Article  Google Scholar 

Ghandour A, Sher A, Rassouli N, Dhanantwari A, Rajiah P (2018) Evaluation of virtual monoenergetic images on pulmonary vasculature using the dual-layer detector-based spectral computed tomography. J Comput Assist Tomogr 42:858–865.

Article  Google Scholar 

Szczykutowicz TP, Nett B, Cherkezyan L et al (2021) Protocol optimization considerations for implementing deep learning CT reconstruction. AJR Am J Roentgenol 216:1668–1677