Abstract
One approach to the design, experimentation, parameter setting, and assembly of an X-ray generator get performed to reduce radiation emission (microgray \(\mu Gy\)) without neglecting the power of the beam emission (KVp). A combination gets made in the manufacture of the transformer coil between turns of the secondary winding \(S_{k}\), with the number of turns of the filament \(F_{i}\). As a result, we obtained a transformer with an emissivity between the desired parameters and the optimum beam power. We classify feasible and infeasible cases in the assembly of X-ray devices using support vector machines. In addition, the tools used for statistical inference were non-parametric tests, such as the modified Friedman test and post-hoc Quade and Conover tests. Finally, we obtained a good image with an excellent resolution without exposing people to high radiation emissions.
Data Availability
We provide the link where data supporting reported results can get found datasets analyzed or generated during the study: https://cutt.ly/PIyx1TI
References
Ahn S, Fessler JA (2003) Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms. IEEE Trans Med Imaging 22(5):613–626
Bamberg F, Hinkel R, Schwarz F, Sandner TA, Baloch E, Marcus R, Becker A, Kupatt C, Wintersperger BJ, Johnson TR et al (2012) Accuracy of dynamic computed tomography adenosine stress myocardial perfusion imaging in estimating myocardial blood flow at various degrees of coronary artery stenosis using a porcine animal model. Invest Radiol 47:71–77
Cassi, I., Salvatelli, A., Bizai, G., Hadad, A., Arduh, D. R., Drozdowicz, B. (2017) . Images Digitization and Characterization of Surface and Fundus obtained through a Slit Lamp Adapted. In VII Latin American Congress on Biomedical Engineering CLAIB (2016) Bucaramanga, Santander, Colombia, October 26th-28th, 2016. Springer, Singapore, pp 137–140
Cuadros Angela P, Restrepo Carlos M, Noël P, Arce Gonzalo R (2022) Static coded illumination strategies for low-dose X-ray material decomposition. Appl Opt 61:C107–C115
Divel SE, Pelc NJ (2019) Accurate image domain noise insertion in CT images. IEEE Trans Med Imaging 39(6):1906–1916
Hernandez LE (2010), Un acercamiento a la deconvolucion ciega usando el algoritmo de Lucy-Richardson, tesis de maestria UAM-I
Flohr TG, McCollough CH, Bruder H, Petersilka M, Gruber K, Suss C, Grasruck M, Stierstorfer K, Krauss B, Raupach R et al (2006) First performance evaluation of a dual-source CT (DSCT) system. Eur Radiol 16:256–268
Fan F, Shan H, Kalra MK, Singh R, Qian G, Getzin M et al (2020) Quadratic autoencoder (Q-AE) for low-dose CT denoising. IEEE Trans Med Imag 39(6):2035–2050
Grieco LA, Boggia G, Piro G, Jararweh Y, Campolo C, (2020) Ad-Hoc, Mobile and Wireless Networks. In: 19th international conference on AD-HOC networks and wireless. Bari, Italy, October 19-21
Jones JG, Mills CN, Mogensen MA, Lee CI (2012) Radiation dose from medical imaging: a primer for emergency physicians. West J Emerg Med 13(2):202–210. https://doi.org/10.5811/westjem.2011.11.6804
Koesters T, Knoll F, Sodickson A, Sodickson DK, Otazo R (2017). SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction. In Medical Imaging 2017: Physics of Medical Imaging. International Society for Optics and Photonics, Vol. 10132, p. 101320Q
Langland O, Plangais R, Preece J (2002). Principles of dental imaging. Publisher Jones and Bartlett Learning; 2nd edition (June 8)
Liang JZ, La Riviere PJ, El Fakhri G, Glick SJ, Siewerdsen J (2017) Guest editorial low-dose CT: what has been done, and what challenges remain? IEEE Trans Med Imaging 36:2409–2416
Lippincott W, Wilkins YJ, Howerton LJ (2021). Dental radiography-E-book: principles and techniques. Elsevier Health Sciences
McCollough CH, Chen GH, Kalender W, Leng S, Samei E, Taguchi K, Wang G, Yu L, Pettigrew RI (2012) Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. Radiology 264:567–580
Medina M, Hernandez E (2010) Deconvolution, parameter estimation and image recovering. In First symposium on inverse problems and its applications, Ixtapa, pp 83-91
Park HS, Baek J, You SK, Choi JK, Seo JK (2019) Unpaired image denoising using a generative adversarial network in X-ray CT. IEEE Access 7:110414–110425. https://doi.org/10.1109/ACCESS.2019.2934178
Ridley EL (2019) AI converts low-dose CT scans into high quality scans. Phys World (2019) https://physicsworld.com/a/ai-converts-low-dose-ct-images-to-high-quality-scans/, Accessed 23th Feb 2022
Shan H, Padole A, Homayounieh F, Kruger U, Khera RD, Nitiwarangkul C et al (2019) Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. Nat Mach Intell 1:269–276
Stayman JW, Otake Y, Prince JL, Khanna AJ, Siewerdsen JH (2012) Model-based tomographic reconstruction of objects containing known components. IEEE Trans Med Imaging 31:1837–1848
The National Academies Press Health risks from exposure to low levels of ionizing radiation: BEIR VII phase 2
Toth Thomas L, Cesmeli E, Ikhlef A, Horiuchi T (2005) Image quality and dose optimization using novel x-ray source filters tailored to patient size, 5745, Medical Imaging 2005: Physics of Medical Imaging, Michael. J. Flynn, International Society for Optics and Photonics, SPIE 283–291. https://doi.org/10.1117/12.595465
Yu L, Liu X, Leng S, Kofler JM, Ramirez-Giraldo JC, Qu M, Christner J, Fletcher JG, McCollough CH (2009) Radiation dose reduction in computed tomography: techniques and future perspective. Imag Med 1(1):65–84. https://doi.org/10.2217/iim.09.5
Zhao T, McNitt-Gray M, Ruan D (2019) A convolutional neural network for ultra-low-dose CT denoising and emphysema screening. Med Phys 46(9):3941–3950
Funding
The authors did not receive support from any organization for the submitted work.
Author information
Authors and Affiliations
Contributions
Conceptualization, Eymard Hernández-López, Emilio Pérez-Pérez and Giovanni Wences ; methodology, Emilio Pérez-Pérez and Giovanni Wences; software, Eymard Hernández-López; validation, Giovanni Wences and Eymard Hernández-López ; formal analysis, Giovanni Wences; Investigation, Eymard Hernández-López; resources, Emilio Pérez-Pérez; data curation, Emilio Pérez-Pérez; writing original draft preparation, Eymard Hernández-López; writing-review and editing, Giovanni Wences; visualization, Emilio Pérez-Pérez; supervision, Eymard Hernández-López. All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethics approval
The study is original and has not to get submitted to any other journal/conference.
Consent to participate
The authors give their consent to participate in this article
Consent for publication
The authors give their consent for the publication of this article
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hernández-López, E., Pérez-Pérez, E. & Wences, G. Design and manufacture of an X-ray generator by support vector machines. Evol. Intel. 17, 1235–1244 (2024). https://doi.org/10.1007/s12065-022-00754-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12065-022-00754-7