Relationship between body habitus and image quality and radiation dose in chest X-ray examinations: A phantom study

Al-Murshedi, Sadeq; Hogg, Peter; England, Andrew

doi:10.1016/j.ejmp.2018.12.009

Cited by 16 publications

(8 citation statements)

References 27 publications

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“…This variation in exposure alterations has been attributed to the MRP's training on either film screen or digital radiography, past education as well as work practices. However, when a chest phantom was used by Al-Murshedi et al, [42] , AEC did not result in optimal image quality and resulted in reduced pathology detection in instances where the kVp was high. MRPs may therefore be required to manually set exposure factors which may be improved by maintaining records of exposure factors received by this patient pop-ulation [45] .…”

Section: General Radiographymentioning

confidence: 99%

“…In studies with phantoms, the distribution of adipose tissue at the anatomical area of interest was an important factor in determining the resultant image quality [42][43][44][45] . In their study with a pelvis phantom, Ismail et al, [44] reported a higher negative correlation (r = -0.83, p < 0.01) between the waist circumference and the image quality for abdominal images compared to that of the BMI and image quality (r = -0.73, p < 0.01).…”

Section: General Radiographymentioning

confidence: 99%

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Imaging individuals with obesity

Nabasenja

Barry

Nelson

et al. 2022

Journal of Medical Imaging and Radiation Sciences

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Section: General Radiographymentioning

confidence: 99%

Section: General Radiographymentioning

confidence: 99%

Imaging individuals with obesity

Nabasenja

Barry

Nelson

et al. 2022

Journal of Medical Imaging and Radiation Sciences

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“…X‐ray technique selection in portable imaging can be a challenging hurdle in the ongoing effort to maintain image quality across a wide range of patient sizes 1–5 . Technologists have mostly been educated to operate digital detectors by targeting a single detector exposure level to reduce noise and avoid saturation 6–9 .…”

Section: Introductionmentioning

confidence: 99%

“…X-ray technique selection in portable imaging can be a challenging hurdle in the ongoing effort to maintain image quality across a wide range of patient sizes. [1][2][3][4][5] Technologists have mostly been educated to operate digital detectors by targeting a single detector exposure level to reduce noise and avoid saturation. [6][7][8][9] Digital detector image quality is primarily determined by quantum mottle, while global image contrast is maintained over a wide range of detector exposures.…”

Section: Introductionmentioning

confidence: 99%

Method of determining technique from weight and height to achieve targeted detector exposures in portable chest and abdominal digital radiography

Hoerner

Grizzard

Moroz

2022

J Applied Clin Med Phys

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This study presents a methodology to develop an X‐ray technique chart for portable chest and abdomen imaging which utilizes patient data available in the modality worklist (MWL) to reliably achieve a predetermined exposure index (EI) at the detector for any patient size. The method assumes a correlation between the patients’ tissue equivalent thickness and the square root of the ratio of the patient's weight to height. To assess variability in detector exposures, the EI statistics for 75 chest examinations and 99 abdominal portable X‐ray images acquired with the new technique chart were compared to those from a single portable unit (chest: 3877 images; abdomen: 200 images) using a conventional technique chart with three patient sizes, and to a stationary radiography room utilizing automatic exposure control (AEC) (chest: 360 images; abdomen: 112 images). The results showed that when using the new technique chart on a group of portable units, the variability in EI was significantly reduced (p < 0.01) for both AP chest and AP abdomen images compared to the single portable using a standard technique chart with three patient sizes. The variability in EI for the images acquired with the new chart was comparable to the stationary X‐ray room with an AEC system (p > 0.05). This method could be used to streamline the entire imaging chain by automatically selecting an X‐ray technique based on patient demographic information contained in the MWL to provide higher quality examinations to clinicians by eliminating outliers. In addition, patient height and weight can be used to estimate the patients’ tissue equivalent thickness.

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“…However, physical characteristics such as the detective quantum efficiency [13,14] and image contrast [15,16] are still difficult to apply to clinical images, as the anatomical background has not been considered [1]. Moreover, analytical visual assessment methods, such as receiver operating characteristic curve analysis and visual grading characteristics analysis [17][18][19][20][21][22][23][24], are time-consuming and require advanced medical knowledge. Accordingly, they cannot be applied to large numbers of clinical images.…”

Section: Introductionmentioning

confidence: 99%

Calculating the target exposure index using a deep convolutional neural network and a rule base

et al. 2020

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The objective of this study is to determine the quality of chest X-ray images using a deep convolutional neural network (DCNN) and a rule base without performing any visual assessment. A method is proposed for determining the minimum diagnosable exposure index (EI) and the target exposure index (EIt). Methods: The proposed method involves transfer learning to assess the lung fields, mediastinum, and spine using GoogLeNet, which is a type of DCNN that has been trained using conventional images. Three detectors were created, and the image quality of local regions was rated. Subsequently, the results were used to determine the overall quality of chest X-ray images using a rule-based technique that was in turn based on expert assessment. The minimum EI required for diagnosis was calculated based on the distribution of the EI values, which were classified as either suitable or non-suitable and then used to ascertain the EIt. Results: The accuracy rate using the DCNN and the rule base was 81%. The minimum EI required for diagnosis was 230, and the EIt was 288. Conclusion: The results indicated that the proposed method using the DCNN and the rule base could discriminate different image qualities without any visual assessment; moreover, it could determine both the minimum EI required for diagnosis and the EIt.

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Relationship between body habitus and image quality and radiation dose in chest X-ray examinations: A phantom study

Abstract: Ti t l e R el a tio n s hi p b e t w e e n b o dy h a bi t u s a n d i m a g e q u ality a n d r a di a tio n d o s e in c h e s t x-r a y e x a mi n a tio n s : a p h a n t o m s t u dy

Cited by 16 publications

References 27 publications

Imaging individuals with obesity

Imaging individuals with obesity

Method of determining technique from weight and height to achieve targeted detector exposures in portable chest and abdominal digital radiography

Calculating the target exposure index using a deep convolutional neural network and a rule base

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