Assessment of the Radiation Dose during 16 Slices CT Examinations

,; Mounir*, Mkimel; Redouane, El Baydaoui; ,; Reda, Mesradi Mohamed; ,; Saad, El Madani; ,; Abderaouf, El Hilali; ,

doi:10.35940/ijrte.d8388.118419

IJRTE

2019

DOI: 10.35940/ijrte.d8388.118419

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Assessment of the Radiation Dose during 16 Slices CT Examinations

Mkimel Mounir*,

El Baydaoui Redouane,

Mesradi Mohamed Reda

et al.

Abstract: In this work. Two studies were presented. The first one is an experimental study of the influence of different scan parameters (potential tube. current tube and pitch) on the radiation dose is presented. It has been demonstrated that the radiation dose increases if we increase the tube current or the potential current. Contrary to the pitch. the radiation dose is reduced when the pitch increases. The second study is a Monte Carlo validation of a CT named SOMATION emotion from Siemens using GATE. Results were c… Show more

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Cited by 3 publications

References 7 publications

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Feasibility of size-specific organ-dose estimates based on water equivalent diameter for common head CT examinations: a Monte Carlo study

Tahiri¹,

Benameur²,

Mkimel³

et al. 2023

J. Radiol. Prot.

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Computed tomography dose index (CTDI) is an unreliable dose estimate outside of the standard CTDI phantom diameters (16 and 32 cm). Size-specific dose estimate (SSDE) for head computed tomography (CT) examination was studied in the American Association of Physicists in Medicine Report 293 to provide SSDE coefficient factors based on water equivalent diameter as size metrics. However, it is limited to one protocol and for a fully irradiated organ. This study aimed to evaluate the dependency of normalized organ dose (ND) on water equivalent diameter as a size metric in three common protocols: routine head, paranasal sinus, and temporal bone. CTDIw measurements were performed for outlined protocols in the Siemens Emotion 16-slice-configuration scanner. Geant4 Application for Tomographic Emission Monte Carlo simulation platform, coupled with ten GSF patient models, was used to estimate organ doses. CT scanner system was modeled. Helical CT scans were simulated using constructor scan parameters and calculated scan lengths of each patient model. Organ doses provided by simulations were normalized to CTDIvol. The water equivalent diameters (D w) of patient models were obtained via relationships between D w and both effective diameter for a sample of patients’ data.NDs received by fully, partially, and non-directly irradiated organs were then reported as a function of D w. For fully irradiated organs, brain (R 2 > 0.92), eyes (R 2 > 0.88), and eye lens (R 2 > 0.89) correlate well with D w. For the rest of the results, a poor correlation was observed. For partially irradiated organs, the exception was scalp (R 2 = 0.93) in temporal bone CT. For non-directly irradiated organs, the exception was thyroid (R 2 > 0.90) and lungs (R 2 > 0.91) in routine head CT. ND correlates well in routine head CT than other protocols. For the most part, no relationship seems to exist between R 2 and scan percentage coverage. The results have revealed additional factors that may influence the ND and D w relationship, which explains the need for more studies in the future to investigate the effect of scan conditions and organ anatomy variation.

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Feasibility of size-specific organ-dose estimates based on water equivalent diameter for common head CT examinations: a Monte Carlo study

Tahiri¹,

Benameur²,

Mkimel³

et al. 2023

J. Radiol. Prot.

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Estimation of Computed Tomography Dose Index (CTDI) Using Monte Carlo Simulation

et al. 2021

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An Overview of Deep Learning Methods for Segmenting Thyroid Ultrasound Images

Kumar,

Panda,

Dayal

2023

IJAENT

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One of the various imaging modalities that is most frequently utilized in clinical practice is ultrasound (US). It is an emerging technology that has certain advantages along with disadvantages such as poor imaging quality and a lot of fluctuation. To aid in US diagnosis and/or to increase the objectivity and accuracy of such evaluation, effective automatic US image assessment techniques must be created from the perspective of image analysis. The most effective machine learning technology, notably in computer vision and general evaluation of images, has since been proven to belong to deep learning. Deep learning also has a huge potential for using US images for many automated activities. This paper quickly presents many well-known deep learning architectures before summarizing and delving into their applications in a number of distinct.

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Assessment of the Radiation Dose during 16 Slices CT Examinations

Cited by 3 publications

References 7 publications

Feasibility of size-specific organ-dose estimates based on water equivalent diameter for common head CT examinations: a Monte Carlo study

Feasibility of size-specific organ-dose estimates based on water equivalent diameter for common head CT examinations: a Monte Carlo study

Estimation of Computed Tomography Dose Index (CTDI) Using Monte Carlo Simulation

An Overview of Deep Learning Methods for Segmenting Thyroid Ultrasound Images

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