Background The purpose of this study was to assess scan parameters and to propose strategies to optimize the examinations of children (from 0 to 15 years old) on adult scanners in developing countries. Methods A study was done in 2015 and 2018 on 312 pediatric patients to verify improved practices. The study of 2015 ended with proposed strategies. Dose and scan parameters were available for prospective dose analysis. These strategies were implemented in a study of 2018. Results Amount the CT examinations study in this paper, the common was head trauma (90 %). For every pediatric CT scan in 2015, a kV of 120 was used in the various hospitals. The mAs ranged from 57.75 to 283.33, slice thicknesses from 1.25 to 2.5 mm and pitch from 0.525 to 1.375 mm. In the study of 2018, implementing the strategy defined in the methodology and proposed in 2015: CTDIVol decreased by 21.27 % for children < 1 year, 31.97 % for children 1–4 years, 17 % for children 5–9 years. DLP also decreased by 25.14 %, 36.29 % and 19.85 % for children < 1 year, 1–4 years and 5–9 years respectively. Children were exposed to ionizing radiation on machines designed for adults, but now the doses received by children are reduced. Conclusions The reduction of doses during the pediatric CT examination is possible with the introduction of new optimization protocols or the acquisition of a new machine with a pediatric protocol.
An amendment to this paper has been published and can be accessed via the original article.
Background and Objective: Nowadays, Computer Tomography is one of the best radiological imaging technics which can give right diagnostic information, among the detection of multiphasic adenomas, the detection of cardiac, cerebral and vascular abnormalities. Although these good qualities, this technic is too radiant for the patient. In this paper, we based on the irradiation doses delivered from the current protocols to find a practical method of their optimization during the pediatric cranial scan. Materials and Methods: This work relies on a collection of data from patients in the hospitals, so that analyze them, give the conclusions and, propose an optimal practical method to decrease the irradiation doses. To collect data, we performed a prospective study of seventeen months (from December 2017 to May 2019) carried out simultaneously in three hospitals of the city: The Centre Medical la Cathédrale (H 1), the Yaoundé Central Hospital (H 2) and the Yaoundé Gyneaco-Obstetric and pediatric hospital (H 3). This study included a total of 192 cases of cerebral trauma, of which 11 cases excluded for incomplete information. The dosimetry quality control (CTDIvol) using the PMMA phantoms of 16 cm and 32 cm fulfilled. The scanographic parameters of the patient acquisition protocol were recorded and analyzed. Some of those parameters were modified and entered the CT with the help of a biomedical engineer to reduce the delivered dose. The relationship between CTDIvol and kV is statistically significant (p < 0.05) to identify significant differences in obtained results before and after the optimization of protocols. Results: Among patients, 172 are boys, and the remaining 9 are girls all were in the 0 to 15 age group.
Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.