Monte Carlo simulation of a mammographic test phantom A test phantom, including a wide range of mammographic tissue equivalent materials and test details, was imaged on a digital mammographic system. In order to quantify the effect of scatter on the contrast obtained for the test details, calculations of the scatter-to-primary ratio (S/P) have been made using a Monte Carlo simulation of the digital mammographic imaging chain, grid and test phantom. The results show that the S/P values corresponding to the imaging conditions used were in the range 0.0844.126. Calculated and measured pixel values in different regions (if the image were compared as a validation of the model and showed excellent agreement. The results indicate the potential of Monte Carlo methods in the image quality-patient dose process optimisation, especially in the assessment of imaging conditions not available on standard mammographic units

Alm,  C. G.

Dance,  D. R.

Hunt,  R. A.

Pachoud,  M.

Sandborg,  M.

Ullman,  G.

Verdun,  F. R.

Serveur académique lausannois

MONTE CARLO SIMULATION OF A MAMMOGRAPHICTEST PHANTOMR. A. Hunt1, D. R. Dance1,, M. Pachoud2, G. Alm Carlsson3, M. Sandborg3, G. Ullman3and F. R. Verdun21Department of Physics, The Royal Marsden Hospital NHS Foundation Trust, London SW3 6JJ, UK2University Institute for Applied Radiophysics, Grand-Pre´ 1 CH-1007 Lausanne, Switzerland3Department of Radiation Physics, IMV, Faculty of Health Sciences, Linko¨ping University,SE-58185 Linko¨ping, SwedenA test phantom, including a wide range of mammographic tissue equivalent materials and test details, was imaged on a digitalmammographic system. In order to quantify the effect of scatter on the contrast obtained for the test details, calculations ofthe scatter-to-primary ratio (S/P) have been made using a Monte Carlo simulation of the digital mammographic imagingchain, grid and test phantom. The results show that the S/P values corresponding to the imaging conditions used were in therange 0.084–0.126. Calculated and measured pixel values in different regions of the image were compared as a validation ofthe model and showed excellent agreement. The results indicate the potential of Monte Carlo methods in the image quality–patient dose process optimisation, especially in the assessment of imaging conditions not available on standard mammographicunits.INTRODUCTIONThe need for image quality assessments andoptimisation in mammography is well establishedand is often based on the use of test phantoms(1,2).One limitation of most of these test phantoms is thatthey are of uniform thickness and composition.Thus, they do not address the issue of the variationof image quality with tissue composition. In thiswork a test phantom has been designed to allow anobjective assessment of image quality in conven-tional and in digital mammography, taking intoaccount the dynamic range of the exposure(3). Thepurpose of this test phantom is to assess theoptimum between dose and image quality.Another approach for improving the balancebetween the radiological risk and the image qualityis the use of Monte Carlo simulations. They havebeen widely used in the field of diagnostic radiologyto estimate doses delivered to patients. In the earlydays, to render calculations tractable, organs wererepresented by simple volumes such as spheres orcylinders, and a very simple model of the breastwas used for mammography(4). Major improvementsof the computing power now allow the assessment ofpatient dose using voxel phantoms where the shapesand positions of the organs are realistic(5). In theframework of patient dose optimisation, MonteCarlo simulations have been extensively used toestimate scatter-to-primary ratio (S/P), in particularto improve the antiscatter grid performances(6).Finally, Monte Carlo simulations are now being usedto simulate the whole radiological chain (i.e. fromthe X-ray production to the raw data images)(7,8).These capabilities (precise dose assessment and rawimage production) widen the domain of optimisationsince no limitation concerning the availability of thetechnology exists.In an initial study of images obtained with the testphantom, measurements of the contrast of testdetails were compared with analytical calculationsbased on the attenuation of primary photons, butthe effects of scatter were not properly taken intoaccount because of lack of appropriate data. In orderto provide details of the effect of scattered radiationon the contrast, a voxelised model of the phantomhas been constructed and Monte Carlo calculationsmade of the S/P obtained both with and without anantiscatter grid. In addition, a comparison of meas-ured and calculated pixel values in digital imagesof the phantom has provided a validation of thecomputer program.MATERIALS AND METHODSThe test phantom used in this study has been fullydescribed elsewhere(3) and was manufactured byCIRS (Computerized Imaging Reference Systems,Norfolk Virginia 23513, USA). It is made of breasttissue equivalent material and has a similar shape toa compressed breast (Figure 1). A 35-mm base layerof 50:50 glandular/adipose tissue equivalent is sur-rounded by a 5-mm shell of 100% adipose equivalentmaterial to simulate the absorption of the skin (totalphantom thickness 45 mm). Three areas within thetest phantom simulate the absorption of 100%glandular, 50:50 glandular/adipose and 100%adipose tissue. Low contrast targets next to theCorresponding author: david.dance@rmh.nthames.nhs.ukRadiation Protection Dosimetry (2005), Vol. 114, Nos 1-3, pp. 432–435doi:10.1093/rpd/nch511ª The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org100% glandular and 50:50 glandular/adipose areasare produced by 5-mm-thick slabs of 75:25 glandular/adipose tissue equivalent material. A 5-mm slab of25:75 glandular/adipose tissue gives a third lowcontrast target next to the 100% adipose area.Digital images were acquired on a Senographe2000D unit (General Electric, USA) using a highvoltage of 28 kV, a tube current–exposure time prod-uct of 45 mAs and a Mo–Mo anode–filter combina-tion (exposure parameters representative of thoseused with a conventional mammography unit).The Monte Carlo program used to simulate thephantom is based on a code developed previously forthe simulation of mammography using simplephantoms(7). The program has been modified to in-corporate voxel phantoms and can calculate bothpixel values and the noise per pixel(8). For the latterpurpose, only quantum mottle is included and sys-tem unsharpness is ignored. The simulation includesthe CsI receptor used in the GE 2000D mammogra-phy unit and the mammographic antiscatter grid.The X-ray spectrum was matched to that used forthe experimental measurements. The voxel phantomwas constructed mathematically from the design spe-cification of the phantom, although the sharp steeledge and TLDs were omitted. The program makesseparate calculations of the energy deposited perpixel from primary and secondary photons and canthus give information on the variation of the S/Pratio and of the pixel value across the image.RESULTS AND DISCUSSIONFigure 2a and b present the image obtained with themammography unit and the calculated image usingthe Monte Carlo program, respectively. Thesefigures show that the test phantom developed allowsimage quality parameter assessment for a wide rangeFigure 1. Picture of the test phantom showing the different tissue equivalent regions and the removable adipose shell andits original schematic representation. (As shown in Figure 2a, the manufactured test phantom has been slightly modifiedfrom the original schematic representation).(a) (b)Figure 2. (a) Radiograph of the test phantom using a GE Senographe 2000D digital mammography unit at 28 kV using aMo–Mo anode–filter combination. (b) Image produced by Monte Carlo simulation with the schematic representationshown in Figure 1.SIMULATION OF A MAMMOGRAPHIC TEST PHANTOM433of tissue compositions in the same image. Moreover,the broad similarity of these two figures indicates theutility of the Monte Carlo approach. The differencesbetween the two images are due to the fact that theinitial design of the test phantom which has beenused in the Monte Carlo simulations (Figure 1) hasbeen slightly modified during the test phantom man-ufacturing. These differences have no major impacton the results, but will be accounted for in futurecalculations.The S/P values, calculated by Monte Carlo interms of energy imparted to the image detector andrepeated with and without the antiscatter grid, aresummarised in Table 1. The areas where S/P havebeen calculated have been numbered according toFigure 2b. As expected, the S/P without the grid inthe 100% glandular part (0.616 in area 1) is higherthan in the 100% adipose part (0.456 in area 11), inparticular, because of its higher primary X-rayabsorption properties compared with the fullyadipose tissue equivalent area.Figure 3 presents the relationship between themeasured pixel values, at various positions, on theimage obtained with the digital mammography unitand the pixel values calculated using the Monte Carlomethodology, both for a Mo–Mo spectrum at 28 kV.The calculations take account of both primary andscattered radiation. The data have been fitted to astraight line constrained to pass through the origin asboth calculated and experimental pixel values arezero when there is no exposure. The excellent correla-tion obtained (Pearson coefficient R2 ¼ 0.9971) is anencouraging demonstration of the proportionalityof experimental and calculated pixel values andvalidation of the computer model.CONCLUSIONSThe results of this work have allowed us to obtainvalues of the S/P for the Lausanne mammographicphantom both with and without an antiscatter grid.In spite of relatively large differences in breast-equivalent tissue compositions within the test phan-tom, the S/P variations at the entrance of thedetector when the grid is used remain small. Theresults presented in this study and a related paperin this journal(8) clearly demonstrate that MonteCarlo methods can be used to accurately simulateradiological imaging situations.ACKNOWLEDGEMENTSThe authors gratefully acknowledge the financialsupport of the Swiss Federal Office for Educationand Science (grant number 99.0739) and the EUFifth Framework Research Programme (grantnumber FIGMCT 2000 00036).REFERENCES1. van Voudenberg, S., Thijssen, M. and Young, K.European guidelines for quality assurance in mammo-graphy screening. Third edition [Luxembourg: CEC(European Commission)] (1999). Report EURno. 14821 (1999).2. Hendrick, R. E. and Berns, E. A. Optimizingmammographic technique. In: Proceedings of the RSNAcategorical course in breast imaging. Chicago, IL,pp. 79–89 (1999).3. Pachoud, M., Lepori, D., Valley, J. F. andVerdun, F. R. Development of a test object to assessimage quality in conventional and digital mammogra-phy, taking into account dynamic range. Phys. Med.Biol. 49, 5267–5281 (2004).4. Dance, D. R. and Day, G. J. The computation ofscatter in mammography by Monte Carlo methods.Phys. Med. Biol. 29, 237–247 (1984).Table 1. S/P values calculated with the Monte Carlomethod.Number ofthe areaS/P (noantiscatter grid)S/P (withantiscatter grid)1 0.616 0.10082 0.626 0.10273 0.759 0.12584 0.582 0.10535 0.570 0.09826 0.528 0.09257 0.561 0.09488 0.476 0.09039 0.472 0.086410 0.450 0.088311 0.465 0.0843Monte carlo estimatesRaw experimental dataFigure3. Relationship between measured pixel values on theradiograph of the test phantom and the energy impartedper pixel (arbitrary units) computed by the Monte Carlomodel at various locations within the test phantom.R. A. HUNT ET AL.4345. Zankl, M., Fill, U., Petoussi-Henss, N. and Regulla, D.Organ dose conversion coefficients for external photonirradiation of male and female voxel models. Phys. Med.Biol. 47, 2367–2385 (2002).6. Sandborg, M., Dance, D. R., Alm Carlsson, G. andPersliden, J. Monte Carlo study of grid performance indiagnostic radiology: task dependent optimisation forscreen-film imaging. Brit. J. Radiol. 67, 76–85 (1994).7. Dance, D. R., Thilander, A. K., Sandborg, M.,Skinner, C. L., Castellano, I. A. and Carlsson, G. A.Influence of anode/filter material and tube potential oncontrast, signal-to-noise ratio and average absorbed dosein mammography: a Monte Carlo study. Brit. J. Radiol.73, 1056–1067 (2000).8. Hunt, R. A., Dance, D. R., Bakic, P. R.,Maidment, A. D. A., Sandborg, M., Ullman, G.and Alm Carlsson, G. Calculation of theproperties of digital mammograms using a computersimulation. Rad. Prot. Dosim. 114(1-3), 395–398,(2005).SIMULATION OF A MAMMOGRAPHIC TEST PHANTOM435

Monte Carlo simulation of a mammographic test phantom

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Monte Carlo simulation of a mammographic test phantom

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