Development of an online automatic diagnostic reference levels management system for digital radiography: A pilot experience

Ng, Curtise; Sun, Zhonghua

doi:10.1016/j.cmpb.2010.07.008

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Hence, “as low as reasonably achievable” (ALARA) has become the fundamental principle of radiology practice [ 17 , 24 , 25 ]. International Commission on Radiological Protection (ICRP) has introduced the diagnostic reference levels (DRLs) initiative for radiological departments to identify examinations with radiation doses exceeding their corresponding DRLs and trigger the radiation dose-optimization process [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ]. As the radiation used in radiological examinations is the source of signal, a reduction of the radiation amount results in a decrease of signal strength and an increase of image noise.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

2022

Children

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“…X-ray digital radiography (DR) and computed tomography (CT) technology have been widely used as nondestructive testing and evaluation tools in a number of industrial applications [1][2][3][4] . However, the single view X-ray DR system is the most popular system for parts inspection, and the data obtained are two-dimensional.…”

Section: Introductionmentioning

confidence: 99%

Design of X-Ray Vision Automatic Testing System

Han

2014

AMR

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Spatial sampling criteria and fast recognition theory based on single view imaging system are proposed for automatic testing the assembly structures inside products in industry applications. There must be a maximum rotary step for an object within which the least structural size to be tested is ascertained. Rotating the object by the step and imaging it and so on until a 360°turn is completed, an image sequence is obtained that includes the full structural information for recognition. It is verified that objects could be recognized at a single or some limited orientations by analyzing the correlations among the image sequence. The theory is applied to the online automated X-ray vision system. Experiments show that the average identification takes less than 5s with 4.5% of wrong recognition expense.

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Attitudes of medical imaging technologists on use of gonad shielding in general radiography

et al. 2012

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Purpose: Gonad shielding is an important way to address the ‘as low as reasonably achievable’ (ALARA) requirement in radiography. However, its use relies on the attitudes of medical imaging technologists (MITs) to consistently adhere to professional conduct requirements relating to the application of gonad shielding. The purpose of this study was to investigate the attitudes of Western Australian (WA) MITs to the use of gonad shielding in general radiography and also to explore the variables influencing these attitudes. Methods: A questionnaire regarding MITs' attitudes towards gonad shielding was developed based on relevant literature and distributed to MITs working in general radiography employed by public and private clinical centres in WA metropolitan and rural regions. Descriptive (percentage of frequency, mean and standard deviation) and inferential statistics (chi‐square, t‐test and analysis of variance) were used to analyse the responses of the multiple choice (MC) and 5‐point scale questions from the returned questionnaires. Results: The questionnaire response rate was 44.6% (127/285). It was found that WA MITs perceived gonad shielding as important (mean: 3.8) and that adequate education had been provided (mean: 3.1). However, statistically significant attitude variations existed among MITs from different employment sectors (P < 0.01), equipment awareness (P < 0.05), genders (P < 0.005) and educational qualifications (P < 0.05). Conclusion: Overall, WA MITs have positive and appropriate attitudes to the use of gonad shielding in general radiography. However, statistically significant attitude variations existed among MITs. Possible directions for improvement include the provision of gonad shielding protocol in each x‐ray room in clinical departments and the enhancement of education and training of shielding application. This potentially will lead to improved consistency in the appropriate application of gonad protective shielding.

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Development of an online automatic diagnostic reference levels management system for digital radiography: A pilot experience

Cited by 6 publications

References 11 publications

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Design of X-Ray Vision Automatic Testing System

Attitudes of medical imaging technologists on use of gonad shielding in general radiography

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