Mera Fernández D, Santos Armentia E, Bustos Fiore A, Villanueva Campos AM, Utrera Pérez E, Souto Bayarri M: The utility of dual-energy CT for metal artifact reduction from intracranial clipping and coiling. Radiologia 60:312-319, 2018

Google Scholar 

Huber FA, Sprengel K, Müller L, Graf LC, Osterhoff G, Guggenberger R: Comparison of different CT metal artifact reduction strategies for standard titanium and carbon-fiber reinforced polymer implants in sheep cadavers. BMC medical imaging 21:29, 2021

Article  PubMed  PubMed Central  Google Scholar 

Martins LAC, Queiroz PM, Nejaim Y, Vasconcelos KF, Groppo FC, Haiter-Neto F: Evaluation of metal artefacts for two CBCT devices with a new dental arch phantom. Dentomaxillofac Radiol 49:20190385, 2020

Article  PubMed  PubMed Central  Google Scholar 

Nassi M, Brody WR, Medoff BP, Macovski A: Iterative reconstruction--reprojection: an algorithm for limited data cardiac-computed tomography. IEEE transactions on bio-medical engineering 29:333-341, 1982

Article  CAS  PubMed  Google Scholar 

Thibault JB, Sauer KD, Bouman CA, Hsieh J: A three-dimensional statistical approach to improved image quality for multislice helical CT. Medical physics 34:4526-4544, 2007

Article  PubMed  Google Scholar 

McCollough CH, Chen GH, Kalender W, Leng S, Samei E, Taguchi K, Wang G, Yu L, Pettigrew RI: Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. Radiology 264:567-580, 2012

Article  PubMed  PubMed Central  Google Scholar 

Fang J, Zhang D, Wilcox C, Heidinger B, Raptopoulos V, Brook A, Brook OR: Metal implants on CT: comparison of iterative reconstruction algorithms for reduction of metal artifacts with single energy and spectral CT scanning in a phantom model. Abdominal radiology (New York) 42:742-748, 2017

Article  PubMed  Google Scholar 

Troeltzsch D, Shnayien S, Heiland M, Kreutzer K, Raguse JD, Hamm B, Niehues SM: Detectability of Head and Neck Cancer via New Computed Tomography Reconstruction Tools including Iterative Reconstruction and Metal Artifact Reduction. Diagnostics (Basel, Switzerland) 11:2154, 2021

Tang H, Liu Z, Hu Z, He T, Li D, Yu N, Jia Y, Shi H: Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT. Br J Radiol 92:20180909, 2019

Article  PubMed  PubMed Central  Google Scholar 

Kim HG, Lee HJ, Lee SK, Kim HJ, Kim MJ: Head CT: Image quality improvement with ASIR-V using a reduced radiation dose protocol for children. Eur Radiol 27:3609-3617, 2017

Article  PubMed  Google Scholar 

Chen LH, Jin C, Li JY, Wang GL, Jia YJ, Duan HF, Pan N, Guo J: 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, 2018

Article  PubMed  PubMed Central  Google Scholar 

Willemink MJ, Noël PB: The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185-2195, 2019

Article  PubMed  Google Scholar 

Greffier J, Hamard A, Pereira F, Barrau C, Pasquier H, Beregi JP, Frandon J: Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30:3951-3959, 2020

Article  PubMed  Google Scholar 

Zhong J, Shen H, Chen Y, Xia Y, Shi X, Lu W, Li J, Xing Y, Hu Y, Ge X, Ding D, Jiang Z, Yao W: Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT. Journal of digital imaging 36:1390-1407, 2023

Article  PubMed  PubMed Central  Google Scholar 

Ichikawa Y, Kanii Y, Yamazaki A, Nagasawa N, Nagata M, Ishida M, Kitagawa K, Sakuma H: Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction. Japanese Journal of Radiology 39:598-604, 2021

Article  PubMed  Google Scholar 

Cao L, Liu X, Qu T, Cheng Y, Li J, Li Y, Chen L, Niu X, Tian Q, Guo J: Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT. Eur Radiol 33:1603-1611, 2023

Article  CAS  PubMed  Google Scholar 

Zeng W, Zeng LM, Xu X, Hu SX, Liu KL, Zhang JG, Peng WL, Xia CC, Li ZL: [Noise Reduction Effect of Deep-learning-based Image Reconstruction Algorithms in Thin-section Chest CT]. Sichuan Da Xue Xue Bao Yi Xue Ban 52:286-292, 2021

PubMed  Google Scholar 

Svalkvist A, Fagman E, Vikgren J, Ku S, Diniz MO, Norrlund RR, Johnsson AA: Evaluation of deep-learning image reconstruction for chest CT examinations at two different dose levels. J Appl Clin Med Phys 24:e13871, 2023

Article  PubMed  Google Scholar 

Wei Y, Jia F, Hou P, Zha K, Pu S, Gao J: Clinical application of multi-material artifact reduction (MMAR) technique in Revolution CT to reduce metallic dental artifacts. Insights into imaging 11:32, 2020

Article  PubMed  PubMed Central  Google Scholar 

Nascimento EHL, Fontenele RC, Santaella GM, Freitas DQ: Difference in the artefacts production and the performance of the metal artefact reduction (MAR) tool between the buccal and lingual cortical plates adjacent to zirconium dental implant. Dentomaxillofac Radiol 48:20190058, 2019

Article  PubMed  PubMed Central  Google Scholar 

Larbi A, Orliac C, Frandon J, Pereira F, Ruyer A, Goupil J, Macri F, Beregi JP, Greffier J: Detection and characterization of focal liver lesions with ultra-low dose computed tomography in neoplastic patients. Diagnostic and interventional imaging 99:311-320, 2018

Article  CAS  PubMed  Google Scholar 

Hsieh J, Liu E, Nett B, Tang J, Thibault J-B, Sahney S: A new era of image reconstruction: TrueFidelity™. White Paper (JB68676XX), GE Healthcare, 2019

Arndt C, Güttler F, Heinrich A, Bürckenmeyer F, Diamantis I, Teichgräber U: Deep Learning CT Image Reconstruction in Clinical Practice. RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin 193:252-261, 2021

Article  PubMed  Google Scholar 

Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, van der Molen AJ, Fleischmann D, Willemink MJ: Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects. Radiology 306:e221257, 2023

Article  PubMed  Google Scholar