Kutanzi KR, Lumen A, Koturbash I, Miousse IR (2016) Pediatric exposures to ionizing radiation: carcinogenic considerations. Int J Environ Res Public Health 13:1057. https://doi.org/10.3390/ijerph13111057
Article CAS PubMed PubMed Central Google Scholar
Nagy E, Tschauner S, Schramek C, Sorantin E (2023) Paediatric CT made easy. Pediatr Radiol 53:581–588. https://doi.org/10.1007/s00247-022-05526-0
Bernhardt P, Lendl M, Deinzer F (2006) New technologies to reduce pediatric radiation doses. Pediatr Radiol 36:212–215. https://doi.org/10.1007/s00247-006-0212-4
Article PubMed PubMed Central Google Scholar
Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357. https://doi.org/10.1148/radiol.2015132766
den Harder AM, Willemink MJ, Budde RP et al (2015) Hybrid and model-based iterative reconstruction techniques for pediatric CT. AJR Am J Roentgenol 204:645–653. https://doi.org/10.2214/AJR.14.12590
Gomi T, Sakai R, Goto M et al (2016) Comparison of reconstruction algorithms for decreasing the exposure dose during digital tomosynthesis for arthroplasty: a phantom study. J Digit Imaging 29:488–495. https://doi.org/10.1007/s10278-016-9876-y
Article PubMed PubMed Central Google Scholar
Nagayama Y, Sakabe D, Goto M et al (2021) Deep learning-based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics 41:1936–1953. https://doi.org/10.1148/rg.2021210105
Cao J, Bache S, Schwartz FR, Frush D (2023) Pediatric applications of photon-counting detector CT. AJR Am J Roentgenol 220:580–589. https://doi.org/10.2214/AJR.22.28391
Willemink MJ, Persson M, Pourmorteza A et al (2018) Photon-counting CT: technical principles and clinical prospects. Radiology 289:293–312. https://doi.org/10.1148/radiol.2018172656
Calderoni F, Campanaro F, Colombo PE et al (2019) Analysis of a multicentre cloud-based CT dosimetric database: preliminary results. Eur Radiol Exp 3:27. https://doi.org/10.1186/s41747-019-0105-6
Article PubMed PubMed Central Google Scholar
Nagayama Y, Oda S, Nakaura T et al (2018) Radiation dose reduction at pediatric CT: use of low tube voltage and iterative reconstruction. Radiographics 38:1421–1440. https://doi.org/10.1148/rg.2018180041
Willemink MJ, Noel PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195. https://doi.org/10.1007/s00330-018-5810-7
Atri PK, Sodhi KS, Bhatia A et al (2021) Model-based iterative reconstruction in paediatric head computed tomography: a pilot study on dose reduction in children. Pol J Radiol 86:e504–e510. https://doi.org/10.5114/pjr.2021.108884
Article PubMed PubMed Central Google Scholar
Southard RN, Bardo DME, Temkit MH et al (2019) Comparison of iterative model reconstruction versus filtered back-projection in pediatric emergency head CT: dose, image quality, and image-reconstruction times. AJNR Am J Neuroradiol 40:866–871. https://doi.org/10.3174/ajnr.A6034
Article CAS PubMed PubMed Central Google Scholar
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539
Article CAS PubMed Google Scholar
Yamashita R, Nishio M, Do RKG, Togashi K (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611–629. https://doi.org/10.1007/s13244-018-0639-9
Article PubMed PubMed Central Google Scholar
Battleday RM, Peterson JC, Griffiths TL (2021) From convolutional neural networks to models of higher-level cognition (and back again). Ann N Y Acad Sci 1505:55–78. https://doi.org/10.1111/nyas.14593
Article PubMed PubMed Central Google Scholar
Vaishnav JY, Jung WC, Popescu LM et al (2014) Objective assessment of image quality and dose reduction in CT iterative reconstruction. Med Phys 41:071904. https://doi.org/10.1118/1.4881148
Article CAS PubMed Google Scholar
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. https://doi.org/10.1186/s12880-021-00637-w
Article PubMed PubMed Central Google Scholar
Li Y, Liu X, Zhuang XH et al (2022) Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V). BMC Med Imaging 22:106. https://doi.org/10.1186/s12880-022-00834-1
Article PubMed PubMed Central Google Scholar
Hee Kim K, Choo KS, Jin Nam K et al (2022) Cardiac CTA image quality of adaptive statistical iterative reconstruction-V versus deep learning reconstruction “TrueFidelity” in children with congenital heart disease. Medicine (Baltimore) 101:e31169. https://doi.org/10.1097/MD.0000000000031169
Article CAS PubMed Google Scholar
Zhang K, Shi X, Xie SS et al (2022) Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose. Quant Imaging Med Surg 12:3238–3250. https://doi.org/10.21037/qims-21-936
Article PubMed PubMed Central Google Scholar
Su B, Wen Y, Liu Y et al (2022) A deep learning method for eliminating head motion artifacts in computed tomography. Med Phys 49:411–419. https://doi.org/10.1002/mp.15354
Han T, Gong X, Feng F et al (2023) Privacy-preserving multi-source domain adaptation for medical data. IEEE J Biomed Health Inform 27:842–853. https://doi.org/10.1109/JBHI.2022.3175071
Thian YL, Ng DW, Hallinan J et al (2022) Effect of training data volume on performance of convolutional neural network pneumothorax classifiers. J Digit Imaging 35:881–892. https://doi.org/10.1007/s10278-022-00594-y
Article PubMed PubMed Central Google Scholar
Ghosh A, Jana ND, Mallik S, Zhao Z (2022) Designing optimal convolutional neural network architecture using differential evolution algorithm. Patterns (N Y) 3:100567. https://doi.org/10.1016/j.patter.2022.100567
Zech JR, Badgeley MA, Liu M et al (2018) Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med 15:e1002683. https://doi.org/10.1371/journal.pmed.1002683
Article PubMed PubMed Central Google Scholar
Gerke S, Yeung S, Cohen IG (2020) Ethical and legal aspects of ambient intelligence in hospitals. JAMA 323:601–602. https://doi.org/10.1001/jama.2019.21699
Bartlett DJ, Koo CW, Bartholmai BJ et al (2019) High-resolution chest computed tomography imaging of the lungs: impact of 1024 matrix reconstruction and photon-counting detector computed tomography. Invest Radiol 54:129–137. https://doi.org/10.1097/RLI.0000000000000524
Article PubMed PubMed Central Google Scholar
Understanding the technology behind photon-counting CT. https://www.siemens-healthineers.com/tr/computed-tomography/technologies-and-innovations/photon-counting-ct. Accessed 12 November 2023
Tsiflikas I, Thater G, Ayx I et al (2023) Low dose pediatric chest computed tomography on a photon counting detector system - initial clinical experience. Pediatr Radiol 53:1057–1062. https://doi.org/10.1007/s00247-022-05584-4
Article PubMed PubMed Central Google Scholar
Horst KK, Yu L, McCollough CH et al (2023) Potential benefits of photon counting detector computed tomography in pediatric imaging. Br J Radiol. https://doi.org/10.1259/bjr.20230189
Esquivel A, Ferrero A, Mileto A et al (2022) Photon-counting detector CT: key points radiologists should know. Korean J Radiol 23:854–865. https://doi.org/10.3348/kjr.2022.0377
Article PubMed PubMed Central Google Scholar
Sandfort V, Persson M, Pourmorteza A et al (2021) Spectral photon-counting CT in cardiovascular imaging. J Cardiovasc Comput Tomogr 15:218–225. https://doi.org/10.1016/j.jcct.2020.12.005
Rajendran K, Voss BA, Zhou W et al (2020) Dose reduction for sinus and temporal bone imaging using photon-counting detector CT with an additional tin filter. Invest Radiol 55:91–100. https://doi.org/10.1097/RLI.0000000000000614
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