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A GPU Simulation Tool for Training and Optimisation in 2D Digital X-Ray Imaging.

Gallio E, Rampado O, Gianaria E, Bianchi SD, Ropolo R - PLoS ONE (2015)

Bottom Line: Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm.The application proved to be efficient and realistic.Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies.

View Article: PubMed Central - PubMed

Affiliation: S.C. Fisica Sanitaria, A.O.U. Città della Salute e della Scienza, Turin, Italy.

ABSTRACT
Conventional radiology is performed by means of digital detectors, with various types of technology and different performance in terms of efficiency and image quality. Following the arrival of a new digital detector in a radiology department, all the staff involved should adapt the procedure parameters to the properties of the detector, in order to achieve an optimal result in terms of correct diagnostic information and minimum radiation risks for the patient. The aim of this study was to develop and validate a software capable of simulating a digital X-ray imaging system, using graphics processing unit computing. All radiological image components were implemented in this application: an X-ray tube with primary beam, a virtual patient, noise, scatter radiation, a grid and a digital detector. Three different digital detectors (two digital radiography and a computed radiography systems) were implemented. In order to validate the software, we carried out a quantitative comparison of geometrical and anthropomorphic phantom simulated images with those acquired. In terms of average pixel values, the maximum differences were below 15%, while the noise values were in agreement with a maximum difference of 20%. The relative trends of contrast to noise ratio versus beam energy and intensity were well simulated. Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm. The application proved to be efficient and realistic. Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies.

No MeSH data available.


Comparison between a simulated (5a) and a real (5b) PA chest radiography of anthropomorphic CIRS phantom.The real image was obtained with a Philips Digital Diagnost with 117 kVp and 2 mAs. Both the images are raw images.
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pone.0141497.g005: Comparison between a simulated (5a) and a real (5b) PA chest radiography of anthropomorphic CIRS phantom.The real image was obtained with a Philips Digital Diagnost with 117 kVp and 2 mAs. Both the images are raw images.

Mentions: Fig 5 shows an example of a comparison between a simulated (5a) and a real (5b) radiography of the anthropomorphic phantom. The average pixel values and standard deviations obtained for ROI positioned in images of AP and LL chest, AP and LL lumbar spine and abdomen for both real and simulated radiographies are shown in Table 2. In terms of average pixel values, the maximum differences for digital direct detectors were below 10%, while a maximum difference of 15% was observed for computed radiography system. Standard deviation differences were in accordance with a maximum difference of 20%. This difference concerns the order of noise homogeneity required overall the area of the detectors in our routinary performed quality assurance tests.


A GPU Simulation Tool for Training and Optimisation in 2D Digital X-Ray Imaging.

Gallio E, Rampado O, Gianaria E, Bianchi SD, Ropolo R - PLoS ONE (2015)

Comparison between a simulated (5a) and a real (5b) PA chest radiography of anthropomorphic CIRS phantom.The real image was obtained with a Philips Digital Diagnost with 117 kVp and 2 mAs. Both the images are raw images.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636382&req=5

pone.0141497.g005: Comparison between a simulated (5a) and a real (5b) PA chest radiography of anthropomorphic CIRS phantom.The real image was obtained with a Philips Digital Diagnost with 117 kVp and 2 mAs. Both the images are raw images.
Mentions: Fig 5 shows an example of a comparison between a simulated (5a) and a real (5b) radiography of the anthropomorphic phantom. The average pixel values and standard deviations obtained for ROI positioned in images of AP and LL chest, AP and LL lumbar spine and abdomen for both real and simulated radiographies are shown in Table 2. In terms of average pixel values, the maximum differences for digital direct detectors were below 10%, while a maximum difference of 15% was observed for computed radiography system. Standard deviation differences were in accordance with a maximum difference of 20%. This difference concerns the order of noise homogeneity required overall the area of the detectors in our routinary performed quality assurance tests.

Bottom Line: Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm.The application proved to be efficient and realistic.Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies.

View Article: PubMed Central - PubMed

Affiliation: S.C. Fisica Sanitaria, A.O.U. Città della Salute e della Scienza, Turin, Italy.

ABSTRACT
Conventional radiology is performed by means of digital detectors, with various types of technology and different performance in terms of efficiency and image quality. Following the arrival of a new digital detector in a radiology department, all the staff involved should adapt the procedure parameters to the properties of the detector, in order to achieve an optimal result in terms of correct diagnostic information and minimum radiation risks for the patient. The aim of this study was to develop and validate a software capable of simulating a digital X-ray imaging system, using graphics processing unit computing. All radiological image components were implemented in this application: an X-ray tube with primary beam, a virtual patient, noise, scatter radiation, a grid and a digital detector. Three different digital detectors (two digital radiography and a computed radiography systems) were implemented. In order to validate the software, we carried out a quantitative comparison of geometrical and anthropomorphic phantom simulated images with those acquired. In terms of average pixel values, the maximum differences were below 15%, while the noise values were in agreement with a maximum difference of 20%. The relative trends of contrast to noise ratio versus beam energy and intensity were well simulated. Total calculation times were below 3 seconds for clinical images with pixel size of actual dimensions less than 0.2 mm. The application proved to be efficient and realistic. Short calculation times and the accuracy of the results obtained make this software a useful tool for training operators and dose optimisation studies.

No MeSH data available.