Quality and dose optimization in hand computed radiography

Pavan, Ana Luiza Menegatti; Alves, Allan Felipe Fattori; Duarte, S. B.; Giacomini, Guilherme; Sardenberg, Trajano; Miranda, José Ricardo de Arruda; Pina, Diana Rodrigues de

doi:10.1016/j.ejmp.2015.06.010

Cited by 7 publications

(12 citation statements)

References 14 publications

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“…Many studies have been performed to optimize X-ray exams using homogeneous phantoms for humans [ 10 , 12 , 14 , 23 ]. However, optimization is still neglected in veterinary medicine.…”

Section: Discussionmentioning

confidence: 99%

“…Constructing a homogeneous canine phantom is important to adhere to ALARA principles and minimize dose creep. These phantoms may be used to optimize image quality and dose using such metrics as the signal difference-to-noise ratio, figure of merit [ 12 , 14 ], modulation transfer function, and effective detective quantum efficiency [ 10 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Optimization procedures have been widely exploited for humans in the literature [ 1 , 10 – 14 ] Homogeneous phantoms are the simplest tool that is used for optimization purposes [ 15 , 16 ]. Homogeneous phantoms are extensively employed to assess objective metrics associated with image quality, such as the signal difference-to-noise ratio (SdNR) [ 12 , 14 ]. A higher SdNR provides superior image quality compared with a lower SdNR [ 12 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Homogeneous phantoms are extensively employed to assess objective metrics associated with image quality, such as the signal difference-to-noise ratio (SdNR) [ 12 , 14 ]. A higher SdNR provides superior image quality compared with a lower SdNR [ 12 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Homogeneous Canine Chest Phantom Construction: A Tool for Image Quality Optimization

et al. 2016

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Digital radiographic imaging is increasing in veterinary practice. The use of radiation demands responsibility to maintain high image quality. Low doses are necessary because workers are requested to restrain the animal. Optimizing digital systems is necessary to avoid unnecessary exposure, causing the phenomenon known as dose creep. Homogeneous phantoms are widely used to optimize image quality and dose. We developed an automatic computational methodology to classify and quantify tissues (i.e., lung tissue, adipose tissue, muscle tissue, and bone) in canine chest computed tomography exams. The thickness of each tissue was converted to simulator materials (i.e., Lucite, aluminum, and air). Dogs were separated into groups of 20 animals each according to weight. Mean weights were 6.5 ± 2.0 kg, 15.0 ± 5.0 kg, 32.0 ± 5.5 kg, and 50.0 ± 12.0 kg, for the small, medium, large, and giant groups, respectively. The one-way analysis of variance revealed significant differences in all simulator material thicknesses (p < 0.05) quantified between groups. As a result, four phantoms were constructed for dorsoventral and lateral views. In conclusion, the present methodology allows the development of phantoms of the canine chest and possibly other body regions and/or animals. The proposed phantom is a practical tool that may be employed in future work to optimize veterinary X-ray procedures.

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“…Many studies have been performed to optimize X-ray exams using homogeneous phantoms for humans [ 10 , 12 , 14 , 23 ]. However, optimization is still neglected in veterinary medicine.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Homogeneous Canine Chest Phantom Construction: A Tool for Image Quality Optimization

et al. 2016

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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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Optimizing image quality using automatic exposure control based on the signal-difference-to-noise ratio: a phantom study

Kawashima

Ichikawa

Hanaoka

et al. 2019

Australas Phys Eng Sci Med

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Quality and dose optimization in hand computed radiography

Cited by 7 publications

References 14 publications

Homogeneous Canine Chest Phantom Construction: A Tool for Image Quality Optimization

Homogeneous Canine Chest Phantom Construction: A Tool for Image Quality Optimization

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

Optimizing image quality using automatic exposure control based on the signal-difference-to-noise ratio: a phantom study

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