Dose Optimization in Computer Tomography Pediatric Cranial Scans

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 ana-lyze 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. CTDIvol values varied from 34.2 mGy to 107.8 mGy and PDLs from 107.8 mGy.cm to 2214.5 mGy.cm in H1. In H2, CTDIvol varied from 5.8 mGy to 44 mGy and PDLs from 91.4 mGy.cm to 665.5 mGy.cm. CTDIvol varied between 9.34 mGy to 92.81 mGy and PDLs from 162.38 mGy.cm to 2713.67 mGy.cm in H3. All values are taken at 75th percentile, with or without contrast injection. Conclusion: The implementation of the optimization of protocols requires the display of the CT parameters to use and to respect during the traumatic brain tests. With displaying and respecting protocol, the CTDIvol decreased by almost 50 per cent.


Introduction
The development of treatment technics in healthcare improved during those last years. Thus, new devices and equipment allow today to the diagnosis, monitoring and treatment of complex diseases. Computed tomography (CT) is one of the medical imaging equipment whose performance improves every year.
The first CT was invented in 1970 [1]. Today, the number of pediatric CT has considerably increased [2], mainly due to the improvement and availability of scanners in the hospitals and the medical imaging laboratories. CT is a highperformance medical imaging technic. However, it is also highly irradiating compared to conventional radiography. Its practice is not without consequent in the life of patients because the risk of induced death by radio cancer for a Sievert has estimated at 14 per cent a birth versus 1 per cent at age 75, although the debate about ionizing radiation effects is still divergent [3] [4]. The risk assessment of induced radio cancer for CT scans performed before the age of 10 is between 0.001‰ (per thousand) and 10‰ (per thousand) depending on the type of examination and the age of the patient [1]. Secondary effects occur in the short terms following high doses of radiation. Those effects can be "deterministic", in the low dose range (<1 Gy), or "stochastic" that appear late depending on the dose received [5]. Technological progress in CT has considerably improved the performance of equipment. However, they have not helped to reduce the delivered dose, and they could even increase it by allowing faster acquisitions on large volumes of exploration. The delivered doses remain poorly appreciated because of a lack of indexes dosimetry references for relevant measures available on our equipment, and, also the absence of a team set up for the improvement of display and the compliance of protocols.
The optimization of irradiation doses during CT exam is a big challenge today.
Many types of research conducted to reduce those doses and keep the quality of captured images for the right diagnosis. In this paper, we focus mainly on irradia- as follows: Section 2 reviews published methods in the field. In Section 3, materials and methods are exploited to collect data throughout our work. Section 4 presents our results concerning the optimization of protocols during the CT exam. Those results are analyzed and discussed also-a conclusion provided in Section 5.

State of the Art
In 2014, Thomas Nelson said that a good team for improvement of display and the compliance of protocols must be composed by a radiologist, a medical physicist, and a technologist [6]. The radiologist is the clinical expert that justifies the prescription of CT scan in children and defines the imaging task. A medical physicist is a clinical expert who can suggest the CT parameters and technics so that the radiation doses of the child patients are As Low As Reasonably Achievable (ALARA). The technologist is the clinical expert in implementation, workflow and provides insight into the practical limitations and modifications of the proposed protocol [7]. The big challenge today is the achievement of diagnostic images while optimizing the child patient doses. That reduces the harmful effects of x-rays in those children. The radiation protection in pediatric is an old concern and dose reduction on CT has been a goal of paediatricians for many years [8]  [17] [19].
Based on the authors and to the best of our knowledge, none researches performed a study, especially on irradiation doses during the pediatric cranial CT, to reduce the irradiation doses from the current protocols. This drawback is the main objective of this paper. Indeed, we determine the delivered irradiation doses from the current protocols to find a practical method of their optimization during the pediatric cranial CT. and doses delivered (CTDI in mGy, and PDL in mGy.cm) as seen in Table 1.  mGy.cm. We can see the box plot displayed in Figure 1, the variation of the CTDIvol in Figure 2 and the variation of the DLP in each hospital. Figure 3 and  Table 4. Unfortunately, two months later in that hospital, the tube was changed by another biomedical en-      The area graph shown in Figure 5 allows us to see the decrease of CTDIv1 before optimization and CTDIv2 after the protocol optimization. Figure 6 presents the linear graph of averages showing the variation of CTDIv1 and CTDIv2 as a function of age groups after the protocol optimization. During scanner examinations, technicians could lower voltage values from 100 kV to 90 kV after injection of contrast media to get better images. According to the DRL in Europe, the CT head is 58 mGy [21]. In our country, it is estimated at 51 mGy. The decrease of the voltage remains in the majority of cases compatible with the use of the dose reduction software's (contrarily to the charge) [22].

Discussion
For the dosimetry indicators, one need two parameters such as CTDI vol that is an indicator of tissue dose that accounts for the average dose distribution (CTDI w ) in the exposed volume during 360˚ (pitch) tube rotation and DLP which gives information on the emitted dose in function of length explored during the acquisition [23].
The CTDI vol is controlled during the installation of the scanner and each tube change, the gap between the CTDI vol display and measure should not exceed ±20%. The radiological risk is taken into account by the effective dose (E) in mSv, which can be estimated from the DLP using conversion factors (eDLP) [24].
According to the generalized linear model, we can notice that the age has a significant influence on the DLP, and, compared to less than one year, the others age groups have a higher dose, unfortunately. However, compared with the H 1 hospital, the other hospitals record significantly lower doses. Figure 1, Figure 2 and Figure 3 show the variations of the CTDIvols and DLPs in H 1 , H 2 and H 3 hospitals before protocol optimization. In the moustache box (Figure 1), we see that CTDIvol values are higher than two other hospitals. In H 2 , high doses do not achieve low doses of H 1 and H 2 hospitals. In Figure 1 and Figure 2, the pooled standard deviation was used to calculate the intervals. We can also notice that the dose varies positively according to the load per cut and also according to the high voltage (the latter cannot usually be interpreted because of non-significance of the associated coefficient. We find that the DLP increases with the ageing patient regardless of the hospital centre ( Figure 4). That can be seen through me-  Figure 4 shows the change in DLPs by age groups and in each hospital. The variances assumed to be equal for the analysis. Factor information: the analysis of variance allows to say that at the threshold of 5 per cent, the DLP varies significantly by age groups, concerning trauma cases.
In Figure 5, the DLP is highest for age groups [5 -10[ and [10 -15] for H 1 than H 2 and H 3 hence the need to optimize protocols in H 1 hospital, in addition to seeing the low doses of H 3 hospital. Figure 5 shows how, after the protocol C. A. Takam et al. Open Journal of Radiology optimization one has low doses of irradiation (CTDIv2) in red colour before optimization, one has blue colour (CTDIv1). This area graph shows that protocol optimization is essential. Figure 6 shows how, before the optimization of the protocol, we have a blue linear graph which has high values CTDIv1. Figure 7 presents the CTDIvol of all hospitals. Although the diagnostic reference level is not regulatory limits [25], they are optimization tools that allow evaluation of its practices [26] [27].

Conclusion
The implementation of the optimization of protocols requires the display of the cranial parameters to be used and adhered to during brain examinations. That data and machine learning [28]. In our future work, we plan to use CT image captured with a low dose and built a Machine Learning model based on a convolutional neural network to perform intelligence interpretation. We will propose an optimal big data architecture with convolutional neural network for dose optimization in CT pediatric cranial scan

Funding
The authors confirm that there is no significant financial support for this work that could have influenced its outcome.

Ethical Approval
All displayed data in this paper were collected with the consent of patients during their exams.

Informed Consent
Written informed consent was obtained from the patient for publication of this review paper.

Conflicts of Interest
We wish to confirm that there are no known conflicts of interest associated with this publication.