X-ray system simulation software tools for radiology and radiography education

Kengyelics, Stephen M.; Treadgold, Laura A.; Davies, Andrew G.

doi:10.1016/j.compbiomed.2017.12.005

Cited by 21 publications

(18 citation statements)

References 25 publications

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“…and the mass thickness has the form (19) The table is fed to the input of the algorithm for forming the calibration functions. The analytical function is constructed on the basis of table (20) Formula (20) allows one to evaluate ideal sinograms by real ones.…”

Section: Formation Of Calibration Functions Dependences Of the Mass Thicknesses Of Test Objects On Their Thicknesses In Free Path Lengthsmentioning

confidence: 99%

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Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Osipov

Yadrenkin

Чахлов

et al. 2021

Russ J Nondestruct Test

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Abstract— A calculation model of X-ray computed tomography with a density assessment function in the geometry of a parallel beam has been proposed. The model includes blocks for simulating and correcting sinograms and reconstructing section images. When generating sinograms, the parameters of the test object, source, and recorder of X-ray radiation have been taken into account. Modeling algorithms are implemented in the MathCad system and tested on virtual test objects.

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Section: Formation Of Calibration Functions Dependences Of the Mass Thicknesses Of Test Objects On Their Thicknesses In Free Path Lengthsmentioning

confidence: 99%

“…In addition to solving design problems, computational computed tomography models and software implementing them [20] are necessary for teaching students and postgraduates. It should be noted that,…”

Section: Introductionmentioning

confidence: 99%

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Osipov

Yadrenkin

Чахлов

et al. 2021

Russ J Nondestruct Test

View full text Add to dashboard Cite

show abstract

“…Much previously published research describing DRR algorithms has focused on the accuracy of the transport equation (physics) or the speed of calculation (computer science) rather than the geometric accuracy of the resultant image. 32,37,38 Nilsson et al (2004) did set out to geometrically validate an intraoral dental radiography simulator. 39 Measurements from resultant DRR’s were in close agreement with the expected theoretical length.…”

Section: Literature Reviewmentioning

confidence: 99%

Geometric validation of a computer simulator used in radiography education

Cosson

2020

BJR|Open

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Objectives: The radiographical process of projection of a complex human form onto a two-dimensional image plane gives rise to distortions and magnifications. It is important that any simulation used for educational purposes should correctly reproduce these. Images generated using a commercially available computer simulation widely used in radiography education (ProjectionVRTM) were tested for geometric accuracy of projection in all planes. Methods: An anthropomorphic skull phantom was imaged using standard projection radiography techniques and also scanned using axial CT acquisition. The data from the CT was then loaded into the simulator and the same projection radiography techniques simulated. Bony points were identified on both the real radiographs and the digitally reconstructed radiographs (DRRs). Measurements sensitive to rotation and magnification were chosen to check for rotation and distortion errors. Results: The real radiographs and the DRRs were compared by four experienced observers and measurements taken between the identified bony points on each of the images obtained. Analysis of the mean observations shows that the measurement from the DRR matches the real radiograph +1.5 mm/−1.5 mm. The Bland Altman bias was 0.55 (1.26 STD), with 95% limits of agreement 3.01 to −1.91. Conclusions: Agreement between the empirical measurements is within the reported error of cephalometric analysis in all three anatomical planes. The image appearances of both the real radiographs and DRRs compared favourably. Advances in knowledge: The commercial computer simulator under test (ProjectionVRTM) was able to faithfully recreate the image appearances of real radiography techniques, including magnification and distortion. Students using this simulation for training will obtain feedback likely to be useful when lessons are applied to real-world situations.

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“…However, training through a simulator on a screen based on software does not allow the student to directly manipulate the equipment but use an alternative input device such as a mouse or keyboard. The results, too, are shown only on a screen, undermining the sense of similarities to the actual clinical setting [11]. It has been pointed out continuously that as a result, training using simulators have very low benefit in acquiring experience or skills.…”

Section: Introductionmentioning

confidence: 99%

Design of a User Interface for Radiography Training Simulator

Choi¹

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The radiography training simulator currently in development minimizes errors through mock training on radiology and improves the accuracy of the tests to minimize radiation exposure of the patients and improve the quality of diagnosis services. The objective of this study is to suggest a configuration method for a user interface in developing a radiography training simulator. To configure the operating program of a radiography training simulator in the same manner as an actual radiography equipment, features allow viewing of the worklist and entry of patient information in accordance with the manual. Body part and projection are selected, after which detector, distance, angle and grid are chosen to proceed with the imaging. The image is then printed. The printed image can be transmitted to the PACS server. In addition, a scenario editing feature and help window on the test method are configured so that the user can configured an image database himself. The developed user interface for radiography training simulator simulates an actual X-ray test, improving the effects of mock training and repetitive learning. Since not all variables that may occur during the image printing cannot be configured, the location data of the model device and camera were used to lead to correct testing methods and positions, thus minimizing errors. In follow-up studies, technological development using object recognition and 3D data will be applied to realize various situations that are close to real-life situations.

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X-ray system simulation software tools for radiology and radiography education

Abstract: Current approaches to teaching x-ray physics in radiological science lack immediacy when linking theory with practice. This method of delivery allows students to engage with the subject in an experiential learning environment.

Cited by 21 publications

References 25 publications

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Calculation Model of X-Ray Computed Tomography with Density Assessment Function

Geometric validation of a computer simulator used in radiography education

Design of a User Interface for Radiography Training Simulator

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