A method to produce and validate a digitally reconstructed radiograph-based computer simulation for optimisation of chest radiographs acquired with a computed radiography imaging system

Moore, C S; Liney, Gary; Beavis, A W; Saunderson, J R

doi:10.1259/bjr/30125639

Cited by 35 publications

(35 citation statements)

References 39 publications

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“…A computer algorithm capable of simulating computed radiography (CR) chest images of average adults at various tube voltages, detector air kerma values and different scatter rejection techniques has been used by our group in a previous study to derive optimum exposure parameters for chest CR imaging [26]. The algorithm creates a digitally reconstructed radiograph (DRR), i.e.…”

Section: Scoring Of Simulated Chest Images (Derived From Previous Work)mentioning

confidence: 99%

Correlation of the clinical and physical image quality in chest radiography for average adults with a computed radiography imaging system

Moore¹,

Wood²,

Beavis³

et al. 2013

BJR

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Objective: The purpose of this study was to examine the correlation between the quality of visually graded patient (clinical) chest images and a quantitative assessment of chest phantom (physical) images acquired with a computed radiography (CR) imaging system. Methods:The results of a previously published study, in which four experienced image evaluators graded computer-simulated posteroanterior chest images using a visual grading analysis scoring (VGAS) scheme, were used for the clinical image quality measurement. Contrast-to-noise ratio (CNR) and effective dose efficiency (eDE) were used as physical image quality metrics measured in a uniform chest phantom. Although optimal values of these physical metrics for chest radiography were not derived in this work, their correlation with VGAS in images acquired without an antiscatter grid across the diagnostic range of X-ray tube voltages was determined using Pearson's correlation coefficient.Results: Clinical and physical image quality metrics increased with decreasing tube voltage. Statistically significant correlations between VGAS and CNR (R50.87, p,0.033) and eDE (R50.77, p,0.008) were observed. Conclusion:Medical physics experts may use the physical image quality metrics described here in quality assurance programmes and

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Section: Scoring Of Simulated Chest Images (Derived From Previous Work)mentioning

confidence: 99%

Correlation of the clinical and physical image quality in chest radiography for average adults with a computed radiography imaging system

Moore¹,

Wood²,

Beavis³

et al. 2013

BJR

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“…anatomical noise), produced at various tube potentials, receptor air kerma and scatter rejection methods, to optimise CR chest radiographic techniques. The computer model is based on a digitally reconstructed radiograph (DRR) simulation system-a computer simulation of a conventional two-dimensional (2D) radiograph created from CT data-and has been produced and thoroughly validated by our group [27]. The evaluation of simulated images was carried out by experienced image evaluators, and so this work presents the results of a virtual clinical trial.…”

mentioning

confidence: 99%

Use of a digitally reconstructed radiograph-based computer simulation for the optimisation of chest radiographic techniques for computed radiography imaging systems

Moore¹,

Avery²,

Balcam³

et al. 2012

BJR

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Objectives: The purpose of this study was to derive an optimum radiographic technique for computed radiography (CR) chest imaging using a digitally reconstructed radiograph computer simulator. The simulator is capable of producing CR chest radiographs of adults with various tube potentials, receptor doses and scatter rejection. Methods: Four experienced image evaluators graded images of average and obese adult patients at different potentials (average-sized, n550; obese, n520), receptor doses (n510) and scatter rejection techniques (average-sized, n520; obese, n520). The quality of the images was evaluated using visually graded analysis. The influence of rib contrast was also assessed. Results: For average-sized patients, image quality improved when tube potential was reduced compared with the reference (102 kVp). No scatter rejection was indicated. For obese patients, it has been shown that an antiscatter grid is indicated, and should be used in conjunction with as low a tube potential as possible (while allowing exposure times ,20 ms). It is also possible to reduce receptor air kerma by 50% without adversely influencing image quality. Rib contrast did not interfere at any tube potential. Conclusions: A virtual clinical trial has been performed with simulated chest CR images. Results indicate that low tube potentials (,102 kVp) are optimal for average and obese adults, the former acquired without scatter rejection, the latter with an antiscatter grid. Lower receptor (and therefore patient doses) than those used clinically are possible while maintaining adequate image quality.

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“…This conversion depends on the energy spectrum (and thus on the kVp) at which one wishes to emulate a CBCT projection. Our method is the same general procedure as that used by Moore et al 14 We begin by collecting cylindrical plugs composed of different materials. The plugs are inserts of known dimensions taken from a Gammex electron density calibration phantom.…”

Section: Iid Ct Number To Lac Conversion Functionmentioning

confidence: 99%

“…13 Moore et al used pencil beams rather than rays as projectors to increase DRR realism and avoid aliasing effects. 14 Birkfellner et al used a "wobbled splatting" technique for fast DRR generation. 15 Other techniques including shear-warp volume rendering, frequency domain volume rendering, and Monte Carlo integration based methods have also been investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Moore et al applied a scatter correction specifically aimed at improving DRRs using the beam stop array method. 14 Our approach is to use an in-house Monte Carlo algorithm simulating the CBCT system to obtain scatter estimates. 26 In contrast to the beam stop array method, this approach does not require any special equipment and more importantly avoids any extra scans, which deliver extra dose to a patient.…”

Section: Introductionmentioning

confidence: 99%

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A digitally reconstructed radiograph algorithm calculated from first principles

Staub

Murphy

2012

Medical Physics

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Purpose: To develop an algorithm for computing realistic digitally reconstructed radiographs (DRRs) that match real cone-beam CT (CBCT) projections with no artificial adjustments. Methods: The authors used measured attenuation data from cone-beam CT projection radiographs of different materials to obtain a function to convert CT number to linear attenuation coefficient (LAC). The effects of scatter, beam hardening, and veiling glare were first removed from the attenuation data. Using this conversion function the authors calculated the line integral of LAC through a CT along rays connecting the radiation source and detector pixels with a ray-tracing algorithm, producing raw DRRs. The effects of scatter, beam hardening, and veiling glare were then included in the DRRs through postprocessing. Results: The authors compared actual CBCT projections to DRRs produced with all corrections (scatter, beam hardening, and veiling glare) and to uncorrected DRRs. Algorithm accuracy was assessed through visual comparison of projections and DRRs, pixel intensity comparisons, intensity histogram comparisons, and correlation plots of DRR-to-projection pixel intensities. In general, the fully corrected algorithm provided a small but nontrivial improvement in accuracy over the uncorrected algorithm. The authors also investigated both measurement-and computation-based methods for determining the beam hardening correction, and found the computation-based method to be superior, as it accounted for nonuniform bowtie filter thickness. The authors benchmarked the algorithm for speed and found that it produced DRRs in about 0.35 s for full detector and CT resolution at a ray step-size of 0.5 mm. Conclusions:The authors have demonstrated a DRR algorithm calculated from first principles that accounts for scatter, beam hardening, and veiling glare in order to produce accurate DRRs. The algorithm is computationally efficient, making it a good candidate for iterative CT reconstruction techniques that require a data fidelity term based on the matching of DRRs and projections.

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A method to produce and validate a digitally reconstructed radiograph-based computer simulation for optimisation of chest radiographs acquired with a computed radiography imaging system

Cited by 35 publications

References 39 publications

Correlation of the clinical and physical image quality in chest radiography for average adults with a computed radiography imaging system

Correlation of the clinical and physical image quality in chest radiography for average adults with a computed radiography imaging system

Use of a digitally reconstructed radiograph-based computer simulation for the optimisation of chest radiographic techniques for computed radiography imaging systems

A digitally reconstructed radiograph algorithm calculated from first principles

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