Assessment of Radiation Dose Optimization Techniques in Pediatric CT Scanning
DOI:
https://doi.org/10.63682/jns.v14i4S.7295Keywords:
Gender equality, Property rights, Bodo tribal community, Customary law, Inheritance, Legal frameworks, Patriarchy, Economic dependency, Women’s empowerment, Land ownershipAbstract
The main purpose of this study is to use new methods to reduce radiation exposure for children when conducting CT imaging, but maintaining all diagnostic requirements. Using scan statistics and reviewing past CT images from different places, the study pays attention to dose measurements that include CTDIvol and DLP. Staff from the radiology area are interviewed and surveyed to assess their knowledge as well as their actions in line with protocols and use of technologies for dose reduction, for example, AEC, IR, and AI. A study found a big difference between the CT procedures for kids and demonstrated that AEC and IR can help protect children from exposure to excess radiation. Still, the fact that training is different for some staff and equipment is not the same for everyone makes it harder to manage the correct dose. The research reveals that AI may soon help with altering protocols and monitoring dosages. It sums up its findings by stating that proper guidelines, additional study for staff, and more technology are necessary for boosting tub for kids safety. They support the creation of safer approaches to using radiation on children in the field of radiology.
Downloads
Metrics
References
Alanazi, M., Kench, P., Taba, S. and Ekpo, E., 2024. Evaluating the impact of dose monitoring software alerts on radiation dose reduction in computed tomography: A systematic review. European Journal of Radiology, p.111892.
Alexander, R., Waite, S., Bruno, M.A., Krupinski, E.A., Berlin, L., Macknik, S. and Martinez-Conde, S., 2022. Mandating limits on workload, duty, and speed in radiology. Radiology, 304(2), pp.274-282.
Arfat, M., Haq, A., Beg, T. and Jaleel, G., 2024. Optimization of CT radiation dose: Insight into DLP and CTDI. Future Health, 2(2), pp.148-152.
Beysang, A., Villani, N., Boubaker, F., Puel, U., Eliezer, M., Hossu, G., Haioun, K., Blum, A., Teixeira, P.A.G., Parietti-Winkler, C. and Gillet, R., 2024. Ultra-high-resolution CT of the temporal bone: comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction. Diagnostic and Interventional Imaging, 105(6), pp.233-242.
Brady, S.L., Trout, A.T., Somasundaram, E., Anton, C.G., Li, Y. and Dillman, J.R., 2021. Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction. Radiology, 298(1), pp.180-188.
Chekmeyan, M., Baccei, S.J. and Garwood, E.R., 2023. Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans. Journal of the American College of Radiology, 20(12), pp.1225-1230.
Crowley, C., Ekpo, E.U., Carey, B.W., Joyce, S., Kennedy, C., Grey, T., Duffy, B., Kavanagh, R., James, K., Moloney, F. and Normoyle, B., 2021. Radiation dose tracking in computed tomography: Red alerts and feedback. Implementing a radiation dose alert system in CT. Radiography, 27(1), pp.67-74.
Francone, M., Gimelli, A., Budde, R.P., Caro-Dominguez, P., Einstein, A.J., Gutberlet, M., Maurovich-Horvat, P., Miller, O., Nagy, E., Natale, L. and Peebles, C., 2022. Radiation safety for cardiovascular computed tomography imaging in paediatric cardiology: a joint expert consensus document of the EACVI, ESCR, AEPC, and ESPR. European Heart Journal-Cardiovascular Imaging, 23(8), pp.e279-e289.
Ghorbanizadeh, S., Raziani, Y., Amraei, M. and Heydarian, M., 2021. Care and precautions in performing CT Scans in children. Journal of Pharmaceutical Negative Results, 12(1), p.54.
Golbus, A.E., Schuzer, J.L., Steveson, C., Rollison, S.F., Matthews, J., Henry-Ellis, J., Razeto, M. and Chen, M.Y., 2024. Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure. European Journal of Radiology Open, 13, p.100578.
Gould, S.M., Mackewn, J., Chicklore, S., Cook, G.J., Mallia, A. and Pike, L., 2021. Optimisation of CT protocols in PET-CT across different scanner models using different automatic exposure control methods and iterative reconstruction algorithms. EJNMMI physics, 8, pp.1-15.
Han, M., Kim, H.J., Choi, J.W., Park, D.Y. and Han, J.G., 2022. Diagnostic usefulness of cone‐beam computed tomography versus multi‐detector computed tomography for sinonasal structure evaluation. Laryngoscope Investigative Otolaryngology, 7(3), pp.662-670.
Irsal, M., Mukhtar, A.N., Winarno, G. and Sari, G., 2022. The Effect of Kilovoltage and Milliampere-Second Parameters on CT Number: Study Phantom Quality Control CT Scan. SANITAS J. Teknol. dan Seni Kesehat, 13(2), pp.237-244.
Kadavigere, R., Sukumar, S. and Pendem, S., 2021. Diagnostic reference levels for computed tomography examinations in pediatric population-A systematic review. Journal of Cancer Research and Therapeutics, 17(4), pp.845-852.
Kamal, N.N.H.N. and Ming, E.L., 2024. Assessment of Scattered Radiation Dose for Different CT Imaging Protocols In 128-slice And 512-slice Multi-detector Computed Tomography (MDCT) Scanners. Semarak International Journal of Public Health and Primary Care, 1(1), pp.55-61.
Kim, S.H., 2023. Dose Reduction Method for Chest CT using a Combination of Examination Condition Control and Iterative Reconstruction. Journal of the Korean Society of Radiology, 17(7), pp.1025-1031.
Kleefeld, C., Lopez, J.P.C., Costa, P.R., Fitton, I., Mohamed, A., Pesznyak, C., Ruggeri, R., Tsalafoutas, I., Tsougos, I., Wong, J.H.D. and Zdesar, U., 2024. Automated Quality Control Solution for Radiographic Imaging of Lung Diseases. Journal of Clinical Medicine, 13(16), p.4967.
Li, Y., Liu, X., Zhuang, X.H., Wang, M.J. and Song, X.F., 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 Medical Imaging, 22(1), p.106.
MENDONÇA, R.P.D., Estrela, C., Bueno, M.R., Carvalho, T.C.A.S.G., ESTRELA, L.R.D.A. and Chilvarquer, I., 2025. Principles of radiological protection and application of ALARA, ALADA, and ALADAIP: a critical review. Brazilian Oral Research, 39, p.e14.
Morgan, T., Ku, M., Berg, M. and Halkett, G.K., 2023. Australian medical radiation practitioners perspectives of continuing professional development: An online cross‐sectional study. Journal of Medical Radiation Sciences, 70(3), pp.270-282.
Orellana García, A., Vega Izaguirre, L., Ceruto Marrero, G. and Méndez Mederos, A., 2023. Plataforma XAVIA PACS-RIS y su contribución al diagnóstico por imágenes médicas digitales en Cuba. Revista Cubana de Ciencias Informáticas, 17(4).
Pendem, S., Priya, P.S., Chacko, C. and Kadavigere, R., 2024. Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain-a pilot study. F1000Research, 13, p.691.
Samber, B.D., Renders, J., Elberfeld, T., Maris, Y., Sanctorum, J., Six, N., Liang, Z., Beenhouwer, J.D. and Sijbers, J., 2021. FleXCT: a flexible X-ray CT scanner with 10 degrees of freedom. Optics Express, 29(3), pp.3438-3457.
Schulz, R.A., Stein, J.A. and Pelc, N.J., 2021. How CT happened: the early development of medical computed tomography. Journal of Medical Imaging, 8(5), pp.052110-052110.
Singh, S. and Sukkala, R., 2021. Evaluation and comparison of performance of low-dose 128-slice CT scanner with different mAs values: a phantom study. Journal of Carcinogenesis, 20, p.13.
Sulieman, A., Adam, H., Elnour, A., Tamam, N., Alhaili, A., Alkhorayef, M., Alghamdi, S., Khandaker, M.U. and Bradley, D.A., 2021. Patient radiation dose reduction using a commercial iterative reconstruction technique package. Radiation Physics and Chemistry, 178, p.108996.
Tadia, V.K., 2021. Evaluation of Radiology Information Systems including PACS at Two Tertiary Hospitals in India from User’s Perspective. Prof.(Dr) RK Sharma, 21(1), p.1171.
Tsuda, M., Yunaga, H., Murakami, A. and Yata, S., 2021. Adaptive statistical iterative reconstruction for computed tomography of the spine. Radiography, 27(3), pp.768-772.
Ungania, S., Solivetti, F.M., D’Arienzo, M., Quagliani, F., Sperduti, I., Morrone, A., de Mutiis, C., Bruzzaniti, V. and Guerrisi, A., 2023. New-generation ASiR-V for dose reduction while maintaining image quality in CT: a phantom study. Applied Sciences, 13(9), p.5639.
Withers, P.J., Bouman, C., Carmignato, S., Cnudde, V., Grimaldi, D., Hagen, C.K., Maire, E., Manley, M., Du Plessis, A. and Stock, S.R., 2021. X-ray computed tomography. Nature Reviews Methods Primers, 1(1), p.18.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
