Computer Methods and Programs in Biomedicine (2005) 78, 179—190
Development of a simulator for radiographic imageoptimization
Mark Winslowa, X. George Xua,b,∗, Birsen Yazici c
a Program of Nuclear Eng. and Eng. Physics, Rensselaer Polytechnic Institute, Troy, NY 12180, USAb Department of Biomedical Engineering, Rensselaer Polytechnic Institute, NES Building, Tibbits Avenue,Troy, NY 12180, USAc Department of Electrical, Computer, & Systems Engineering, Rensselaer Polytechnic Institute, Troy,NY 12180, USA
Received 12 October 2004; received in revised form 20 January 2005; accepted 4 February 2005
In the United States, approximately 250 million ra-diological examinations are performed each year,making diagnostic medical examinations the largestsource of man-made radiation exposure [1]. A ma-jor goal of radiography is to maximize the amount ofdiagnostic information while minimizing the radia-
examinations require the selection of a beam qual-ity (i.e., X-ray voltage and filtration) and X-ray tubeoutput (mA s), which affect both the patient doseand the corresponding image quality. Optimizationis difficult because of the following four reasons:(1) the X-ray imaging chain contains a large numberof variables associated with X-ray source, patientanatomy, scatter removal grid, and X-ray detector
tion exposure to the patient. All radiographic X-ray
the sole indicator of patient risk but is not obtain-able without a whole-body phantom of delineated
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organs; (4) perception plays a key role in evaluatingimage quality, which needs to be related to radiolo-gist performance. At the core of the problem is thelack of a suitable simulator capable of interlinkingimage quality and patient dosimetry. Although dif-ferent aspects of this problem have been previouslystudied [2,3], at this time, there is no simulatorthat will allow for such a complicated optimizationprocess to be performed. To this end, we have de-veloped the virtual photographic and radiographicimaging simulator (ViPRIS) that has the potential tomeet our needs.
At the core of this simulator are the image setsthat have been made available to the public by theUnited States Nation Library of Medicine’s VisibleHuman Project (VHP). The VHP was conducted tocreate digital anatomical data for adult male andfemale by obtaining transverse CT, MR and cryosec-tional images of representative male and femalecadavers. For our project, ViPRIS utilizes two setsof anatomically identical datasets: color cryosec-tional photographs and CT scan images [4]. ViPRIScontains an image projection phantom constructedfrom the CT data set to simulate the primary X-rays. Scattered X-rays are simulated using the vis-
1-mm thick slices to be removed from head to toefor cross-sectional color photographs. At the endof the VHP, a total of 1878 transverse color pho-tographs were obtained and segmented to iden-tify up to 1400 organs and tissues of interest forthe male cadaver to form a digital atlas of whatwas called the ‘‘Visible Man.’’ At Rensselaer, seg-mentation and classification were performed to fi-nally adopt 72 radiosensitive organs and tissues forradiation dosimetry studies [5]. A sample slice ofthe VHP man cryosectional photos, the correspond-ing segmented VIP-Man image slice, and the cor-responding CT slice are provided in Fig. 1. Valuesof tissue densities and compositions for radiationdosimetry calculations were based on those rec-ommended in ICRP 23 [6]. The original voxel sizeof the VIP-Man model is 0.33mm× 0.33mm× 1mmand the whole body is represented by 3,384,606,720voxels. The VIP-Man model has been implementedinto Monte Carlo simulation codes to study radiationdoses of an adult male exposed to various externaland internal sources [7—10]. Complete listing of pa-pers related to the VIP-Man model can be found atRRMDG.rpi.edu.
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ible photographic Man tomographic phantom (VIP-Man) previously constructed from segmented colorcryosectional photographs of the VHP (as discussedin more detail below). The two phantoms are ofidentical anatomy, allowing study of patient doseand production of radiographic images simultane-ously. ViPRIS is very flexible and allows for theadjustment of different parameters. Nodules canbe embedded in ViPRIS to evaluate the thresholdof detectability at different locations. The user ispermitted to specify different grid filtering param-eters and the detector efficiency. A simple user-friendly software interface permits an observerto investigate the relationship among nodule de-tection threshold, nodule characteristics, patientdose, detector efficiency, and X-ray beam settings.
2. Background
2.1. Prior work: the VIP-Man model tocompute radiation doses
The VIP-Man model was originally developed fromthe VHP image data to calculate radiation dosesto an adult male [5]. Image data from the VHP in-clude four sets of images of two cadavers [4]. Themale cadaver weighed 90 kg and was 186 cm tall.CT, MRI, and X-ray images were first obtained ofthe fresh body. The body was then frozen to allow
.2. Modification of the VIP-Man model forhis study
n order to reduce the computational time, a sim-ler version of the VIP-Man model with a voxel sizef 1mm× 1mm× 1mm was utilized for this study.n X-ray examinations, a patient often raises therms to expose the chest area. For this reason, therms of the original VIP-Man model were removedo more realistically represent a patient undergoingn X-ray examination. Approximately, 200 slices ofhe VHP transverse images of the upper arms wereanually edited using a commonly available com-ercial imaging software package. In the upper armegion, the arms were in contact with the mainody. Anatomical landmarks were used to decidehere to delineate the boundary. After a section ofrm was removed, a layer of skin with proper thick-ess was added to the main body. The lower armsnd hands were removed from the body by usingcustom C++ code that utilized an object size fil-er to automatically identify and remove objectsot connected to the main body. We used the codeo process an additional 250 slices. All modifiedlices were manualy viewed and verified slice bylice prior to our Monte Carlo calculations. Three-imensional surface renderings of the armless VIP-an model can be seen in Fig. 2.A total of 72 separate organs and tissues were
agged with information on chemical composition,
Development of a simulator for radiographic image optimization 181
Fig. 1 Gray scale image of the (a) original cryosectional image of the male Visible Human Project and (b) correspondingslice of the segmented VIP-Man Phantom, and (c) image of the corresponding CT scan slice of the male Visible HumanProject.
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Fig. 2 Surface rendering of the VIP-Man tomographicmodel with the skin, heart, and bone displayed.
density, and location for Monte Carlo simulationsin two codes: MCNP and Electron Gamma ShowerNational Research Council (EGSnrc) [11,12]. TheEGSnrc code is used in this study to calculate organdoses as well as the scatter images, but a cross-check with the MCNP code was made at an earlystage of the study to verify the geometry defini-tions of the VIP-Man. Because VIP-Man is defined byan extremely large number of voxels, a look-up ta-ble algorithm was developed to reduce the burdenon computer random access memory by compress-ing the image data in EGSnrc.
2.3. Related work and lessons learned
Lazos et al. at University of Patras in Greece andSandborg et al. at Linkoping University in Swe-den have studied image optimization using com-puter models of dosimetry and imaging, respec-tively [2,3].
The group from University of Patras has devel-oped software that produces images based on asimulated X-ray system. The system includes sev-eral modules including the following: Monte Carlomodeling of the particle beam with variable spec-trums (kVp); grid transmission rates; image forma-tion; voxel dose calculations. The Patras group’ssimulator is a significant advancement in comput-erized simulation of radiography. However, the ob-jective of our software, ViPRIS, is to optimize theimaging chain by identifying ways to maintain sat-isfactory image quality while minimizing the effec-tive dose to the patient. Different tissues within thebody have different weighting factors with regardto radiation risk [13]. Without a phantom contain-ing segmented and defined organs such calculationshave to rely on conversion coefficients developedfor an average person, and not the specific personbtcpVdatqdevpatwtwCiwpsavs
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eing studied. According to Wise et al. [14], varia-ion of patient size, sex, field size, and field positionan lead to significant variation of effective doseer kerma area product by up to a factor of two. TheIP-Man phantom provides an ideal opportunity toevelop a well-defined database of organ doses forspecific individual. When a small amount of radia-ion (low fluence) is used to produce an image, theuantum (statistical) noise in the image is the pre-ominating factor that degrades image quality. Thisffect can be seen in videos obtained using nightision technology when there is little light, or lowhoton fluence. Fig. 6a contains a low fluence im-ge. When an image is degraded severely by quan-um noise the radiologist’s ability to detect lesionsithin the radiograph is limited. Our effort includeshe addition of a quantum noise model. Originallye attempted to produce images solely using Montearlo techniques. We discovered that the comput-ng time required to produce an adequate imageas not practical. Therefore, we adopted the ap-roach proven by the Patras group of creating twoub-simulation systems. Adopting the split systempproach developed by the Patras group proved in-aluable and allowed for the development of theimulator.The group from Linkoping University has devel-
ped a software package designed to calculate theontrast of veins and arteries within a tomographichantom [2]. The Linkoping group used a phan-om developed earlier at Yale University for nuclear
Development of a simulator for radiographic image optimization 183
medicine applications [15]. The phantom was builtfrom CT images for head—torso region of the bodywith only a small number of tissue types. The reso-lution of the images used to construct the phantomwere not as fine as those from the VHP. The math-ematical simulations conducted by the LinkOpinggroup proved that human tomographic phantomscould be used for studying image quality charac-teristics. We wish to advance the technique by us-ing the more detailed VIP-Man phantom with moretissue types and by simulating actual radiographicimages for use in observer studies.
3. Design considerations
The objective of ViPRIS is to produce simulatedradiographs of reasonable quality, using variablesettings, which contain simulated lesions of vari-ous sizes, and locations. The simulated radiographscould then be used in future image quality analysisstudies. In order to ensure the clinical applicabilityof the simulated images, we have collaborated withexperienced radiologists and medical physicists atSv
wtfiifmddlspBwptnenmatewdpsf
X-ray images has been since divided into severalphases as detailed below. Images formed in such away were satisfactory to the radiologists and med-ical physicists.
4. System description
4.1. Stages of image formation andoptimization process
Optimization of the radiography process occurs us-ing three modules. ViPRIS is used to produce re-alistic chest X-ray images and represents the firstmodule. The secondmodule is the VIP-Man phantomand the dosimetry calculations within EGSnrc. Thethird module is simulated observer studies usingHotelling statistics and an analysis program withinthe MATLAB software program. The observer stud-ies module will be discussed as part of our futurework.
4.2. Formation of the X-ray image
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tate University of New York, Upstate Medical Uni-ersity.When the simulator was first conceived in 2001,e produced images solely using the VIP-Man phan-om. The images were blocky and did not containull anatomical details. This was because the lungsn the VIP-Man were assigned a uniform density dur-ng the segmentation and organ delineation processor calculating average organ doses. While this seg-entation was adequate for radiation protectionosimetry and Monte Carlo modeling purposes, itid not contain subtle anatomical details about theungs. The lungs on the initial images had a singleolid gray scale value. As a crosscheck, we com-ared the ViPRIS images to the VHP chest X-rays.eing engineers, we had thought that the imagesere of good quality, until the consulting medicalhysicists and radiologists indicated otherwise. Inhe radiologists’ experience, normal anatomy sig-ificantly confounds the detection of lesions. If, forxample, a lesion is present within a patient’s lungear veins and other structures, it becomes muchore difficult to detect. By simulating the lungs ascontinuous tissue type as we did initially, the de-ection process would have been unrealistically tooasy for observers. The observer studies thereforeould have indicated a much lower threshold foretection than would have been obtained in a realatient. It was at this point that we adopted theplit approach in using two phantoms for two dif-erent purposes. The formation of simulated chest
he geometry used for formation of the image cane seen in Fig. 3. ViPRIS simulates X-ray projectionn three phases. First, for a given X-ray tube setting,iPRIS calculates the primary X-ray intensity (i.e.,ransmitted X-rays) by tracing each X-ray throughhe image projection phantom. ViPRIS then scalesnd calculates the scattered X-rays based on storedata files from pre-generated Monte Carlo simula-ions. The scattered data set is smoothed to removexcess noise cause by statistical variations inherentn Monte Carlo simulations. Each data set is modi-ed based on the specified detective quantum effi-iency. Quantum noise is added to the primary andcattered images. Finally, the two images are com-ined to form the simulated radiograph. The detailsf each step of the process are given below.
.2.1. Primary X-ray imagehe primary X-ray image is formed by photons thatransmit through the patient anatomy without in-eractions. The number of transmitted photons iseadily obtainable from attenuation coefficients ofhe associated tissue volume. Visible Human CT im-ges are useful because they contain the electronensity of each tissue slice and can be convertednto attenuation coefficient data using Equation (1).
Ux = 1000�x − �w
mw(1)
= I0 e−t˙� (2)
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Fig. 3 Radiographic imaging chain containing the ViPRIS computational phantom. The simulated image receptor willhave transmitted and scattered photons.
where HUx is the Hounsfield Unit value of voxel x,�x the linear attenuation coefficient of the mate-rial of voxel x, and �w is the attenuation coefficientof water. I in Equation (2) is the number of pho-tons that transmit through the body along the X-raybeam, where I0 is the number of photons incidenton the patient along the path from the source tothe detection pixel, t the voxel dimension in theanterior/posterior direction, and � is calculatedby ViPRIS from the Visible Human CT data set forevery pathway/pixel. The attenuation coefficientsare summed along the path length, as illustratedin Fig. 4. X-ray tube output (mA s) determines thenumber of X-ray photons to be emitted from thetube, and is simulated by I0 as specified by the user.
The Visible Human CT data set does not con-tain lesions in the chest. To simulate lesions, ViPRISreplaces attenuation coefficients of certain voxelswithin the selected chest region of the CT withthose of lesions. The software allows a user to se-lect a variety of lesion geometries and composi-tions in the form of a lesion HU. For example, afatty lesion will have a lower HU than a calcifiedlesion.
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4.2.2. X-ray scattering imageWhen an X-ray photon enters the patient body,there is a probability that an X-ray may undergoscattering from the path towards the film or detec-tor grid and will add a random background noise tothe primary image. A critical part of increasing thesignal-to-noise ratio within a radiographic image isremoving the scattered signal from the final imagereceptor. Anti-scatter grids only attenuate, and donot totally block, the scattered X-rays. Because thescatter and primary portions of an image are com-bined on a film or detector, the pattern at whicha scattered photon contributes to a radiograph isstill not fully characterized. Monte Carlo techniquesare ideally suited for modeling this random process.The Monte Carlo computer code EGSnrc is used inViPRIS to simulate the detailed photon interactionsin the phantom. The scattered portion is recordedfor later use. This process is extremely time con-suming because the probability of Compton scat-tering is relatively small for X-rays and a large num-ber of particle histories are necessary to reduce thestatistical uncertainty inherent in the Monte Carlomethod. To further reduce the statistical uncer-tainty (motel or noise) each data set are soothedooateetssti
r denoised using a 24× 24 boxcar filter. Selectionf the 24× 24 boxcar smoothing filter was basedn experiment during which several smoothing fil-ers were examined. Smaller filters did not providenough of a denoising effect and larger filters gen-rated too much blurring. The error introduced byhe 24× 24 smoothing filter is minimal because thecatter portion of an image is a general fog withoutpecific anatomical detail that is superimposed overhe entire image. The filter has a width of approx-mately 2 cm. The lungs were the only anatomical
ig. 4 Intensity of the transmitted photons is deter-ined by the total attenuation coefficients of the voxelsn X-ray transverses.
Development of a simulator for radiographic image optimization 185
landmarks that were identifiable with any of thefilters examined. To ensure that we have used theproper number of particles required for a reason-able image, we ran an experiment on a sub sectionof the phantom’s lung/chest region. We conductedseveral simulations of varying fluences ranging from1 to 1000 million on a section of the chest. Becauseof the focusing effect of reducing field size, this wasequivalent to running approximately 7 billion par-ticles on the entire phantom. The 1000 million runwas selected as our gold standard. A comparisonwas made between each of the lower fluence runsto see how closely they resembled the high fluenceimage. From this experiment, we determined that100 million particle histories for the higher energysimulations (>40 keV) and 1 billion particle histo-ries for the lower energy simulations provided anaccurate representation of the scatter portion ofthe image. Because the X-ray scattering process isextremely inefficient, the Monte Carlo simulationsare time consuming. Using our computational hard-ware system, the image data files required approx-imately 6 weeks to produce.
4.2.3. Formation of the final imageOlntm
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To convert the photon fluence of the primaryimage into energy fluence the number of survivingparticles in each pixel (I from Equation (2)) is multi-plied by the user specified energy. For the scatteredportion, the same procedure is followed; however,the average scatter particle energy is used.
To combine the scatter and primary portionsof the radiograph the specifications of the user-defined grid are utilized. ViPRIS simulates the pres-ence of a grid by allowing the user to specify thetransmission percentages of the primary and scat-tered image. Any grid can be simulated using Buckyfactors and scatter to primary ratio data specifiedby grid manufacturers. The default grid is a stan-dard 10:1 with a transmission rate of 65% primaryand 5% scatter. For example, when the user speci-fies a 0% transmission of the primary photons, theprimary image is completely removed and the scat-tered image is displayed. Conversely, when a 0%transmission of the scattered photons is selected,the scattered image is removed and the primary im-age is displayed.
The response curve of film is an elongated Sshape with a log linear useful region, while mod-ern digital detectors have a nearly linear responsecViisledg
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nce the primary and scatter images are calcu-ated, ViPRIS accounts for various imaging compo-ents detailed below and forms a final image. Prioro any operations the scatter data are scaled toatch the fluence specified by the user.Quantum noise within an imaging system is a
ormal distribution or a Poisson distribution. Withn adequate sample size a Gaussian distribution isepresentative. ViPRIS uses a Box—Muller approxi-ation of a Gaussian distribution to add simulateduantum noise to the primary and scatter imageata [16]. The square root of the number of photonsncident on a pixel is used to simulate the standardeviation of noise as governed by Gaussian statis-ics. Equation (3) shows how a Box—Muller distri-ution is generated. Variables x and y are randomumbers uniformly distributed over the interval [0,]. n Is the resultant Gaussian random distributionith a zero mean and standard deviation of one.he resulting n is multiplied by the assumed stan-ard deviation and added to each pixel count.
f(x) = √− ln(x)
g(y) = √2 cos(2y)
n = f(x)g(y)
(3)
ecause the quantum absorption efficiency (detec-ion efficiency) of the detector is not unity, the userpecified efficiency is used to modify the number ofarticles detected in all pixels. This allows for theimulation of any detector as well as X-ray system.
urve across the entire dynamic range [17]. SinceiPRIS is designed to simulate the more modern dig-tal detector system, the log of the energy fluences assumed to be log linear. The data set is thencanned for the lowest and highest numbers. Theowest log energy fluence is then subtracted fromach data point. A new maximum is calculated andivided by 255. The data are then converted into aray scale value between 0 and 255.
.3. Calculation of organ doses for riskssessment
rior to the development of the full ViPRIS sys-em, we tested using the VIP-Man phantom to eval-ate the doses received by patients during severalommon radiographic evaluation procedures [18].e discovered the number of particle histories re-uired for adequate statistics needed for effectiveose calculations were approximately 100 timesess than those required for production of the scat-er image.When the photon fluence used to create a radio-
raphic image is increased, the number of photonshat reach the X-ray film or digital X-ray sensor willncrease, leading to an improved signal-to-noiseatio. Unfortunately, increasing the fluence willimultaneously increase the radiation dose to theatient. ViPRIS is designed to allow the user topecify how many particles are projected on each
186 M. Winslow et al.
image pixel (I0). When a small number of photonsare specified, the projected image is grainy (noisy)and it is difficult to observe fine details. During theoptimization process, a ViPRIS user can observehow the quality of an image changes with differentphoton fluencies. Our objective is to find the pointwere adequate images are produced with theminimum dose.
Organs receive X-ray doses from secondary elec-trons resulting from photoelectron absorption andCompton scattering. During the formation of the
scatter profile the EGSnrc code tracks the energydeposition in each of the major organs of the VIP-Man model. The mean absorbed dose in an organor tissue (DT) is calculated as the total energy de-posited in organ T divided by the organ mass. Theequivalent dose (HT) in organ T is calculated bymul-tiplying the mean absorbed dose by the radiation-weighting factor, wR, which is unity for photons andelectrons. Since the same equivalent dose value cancause different risk in different organs or tissues, atissue-weighting factor (wT), has to be applied to
Fig. 5 Flow chart of optimization process, spilt into four secalculation (left), development of the scatter portion of the idevelopment of the primary portion of the image from the rayimages and determination of lowest dose setting (lower cente
ctions: Monte Carlo data generation and effective dosemage from the Monte Carlo data profile (upper center),tracing algorithm (right), and analysis of the generatedr).
Development of a simulator for radiographic image optimization 187
yield the total risk, in terms of effective dose (E)using Equation (4) [13].
E =∑
TwTHT (4)
Twelve critical organs/tissues and their weightingfactors are explicitly recommended by the ICRP.The bone surface, which is in the scale of ∼10�m,is too small to be defined in VIP-Man. Consequently,the dose to bone surface was substituted with thedose to bone, as in all such calculations. The testis(only one remained in the Visible Man) within theVIP-Man model was used to represent gonads. A thinlayer of fat tissue around chest level is used as malebreast. Ten more organs and tissues are includedin the remainder group of organs which togethershares a total tissue weighting factor of 0.05. Ina later ICRP Publication [19], upper large intestinewas combined to the critical organ, colon. Thus, theVIP-Man has only eight organs in the ‘‘Remainder’’group according the ICRP definitions (no female or-gans in VIP-Man, e.g., uterus).
Monoenergetic photons were considered inViPRIS for dose calculations. The user specifies theenergy to be used for each simulation. For each pre-giis
for effective dose per particle at the specified en-ergy. Since the user specifies a fluence to be simu-lated an effective dose is calculated by multiplyingthe dose conversion factor by the fluence.
4.4. Hardware and compiler information
Scatter images were generated using EGSnrc ona computer using a 1.6GHz Pentium 4, 512MB ofRAM, running Linux. ESG4nrc uses a MORTRAN com-piler. A total of 100 million particle histories weresimulated for energies between 40 and 150 keV,while 1 billion particle histories were simulated for30 and 35 keV. The 1 billion particle runs requiredapproximately 2 weeks while the 100 million parti-cle runs required approximately 1.5 days. Primaryimage formation and merging of the scatter andprimary images took place in a C++ code running onanother computer utilizing Windows XP. Energy orparticle counts per pixel are represented by largetwo-dimensional float arrays, 440× 494. A flowchart of the basic operations can be seen in Fig. 5.
Because we wished to simulate many differentradiographs, the scatter image was saved andseia
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enerated scattered image there is a correspond-ng dosimetry data set. When the scatter data files loaded the associated dosimetry data set is alsopecified. The data set contains conversion factors
ig. 6 Simulated primary chest X-ray images for 50-keV X-rahoton fluence: (a) at relative fluence of 1, the lesion is notisible.
caled depending on the fluence desired withinach simulation. Development of each simulatedmage using the C++ portion of the code takespproximately 3min. The scatter image does not
ys with a lesion (highlighted by the circles) for differentvisible and (b) at relative fluence of 100, the lesion is
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Fig. 7 Scattered 50-keV X-rays simulated using VIP-Man tomographic phantom and Monte Carlo method. One-hundredmillion particles were simulated.
contain a lesion. However, the scatter image is ageneral fog and the impact of not having a smalllesion (1 cm× 1 cm) within the VIP-Man phantom isminimal. By only placing lesions within the primaryimaging code we have been able to simulate amultitude of different fluence and lesion combi-nations in a single afternoon. Using a pure MonteCarlo approach would have required several daysto calculate each data point.
5. Status of work
Fig. 6 shows preliminary images we have obtainedusing the image projection phantom for the primarytransmission at 50 keV, at two different levels of ra-diation exposure. The images in Fig. 6 also show acircular mass lesion that has been inserted into theright lung. It can be seen that image quality im-
proves as the fluence increases from Fig. 6a and bdue to the 10-fold reduction in the level of quan-tum mottle (noise) (caused by a 100-fold increasein photon fluence); the added lesion is essentiallyinvisible in Fig. 6a, but is readily visible in Fig. 6b.In generating these types of images, we have totalcontrol over both the lesion characteristics (size,shape, and composition) and the lesion location.
Fig. 7 contains a preliminary image of the scat-tered 50-keV X-rays. It can be observed from theimage that the scattered X-ray energy depositionhas a non-uniform pattern throughout a radiograph.
Effective dose and organ doses have beencalculated in a previous paper for photons incidentfor several views and charted with regard toenergy imparted [17]. The feasibility of ViPRIS incalculating effective doses of a patient has beendemonstrated, and we will extend our calculationsto include all X-ray energies and fluences. Oncesimulations are completed, this work will result
Development of a simulator for radiographic image optimization 189
in a thorough database for organ doses from chestX-ray examinations.
6. Lessons learned
The simulator has been in development for overa year. During that time, we attempted to gen-erate images using a combination of techniquesincluding pure Monte Carlo techniques. When thesystem was modeled using pure Monte Carlo tech-niques the simulation took several days, and theresulting image was unrealistic and only containedbones. Details of the lungs, stomach, and heartcould not be observed. We then tried to simu-late the radiography using a pure ray-tracing al-gorithm. The simulated radiographs we obtainedwere not realistic enough because they did notcontain adequate anatomical variation. Lung tis-sue contains many variations. The VIP-Man phantomsimulated the lungs as a uniform tissue. Small le-sions can hide inside the subtle variations caused byanatomy.
By splitting the simulation into Monte Carlo anda primary image analysis portions, we reduced therCchdphbpti
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filter. We have developed a program in MATLAB toimplement and test the use of these simulated ob-servers and will be publishing our findings in thenear future.
7.2. Conclusion
We have constructed ViPRIS to study optimizationof the X-ray imaging chain. The tomographic phan-toms constructed from Visible Human anatomicalcolor images and the computed aided tomographydata set combined with the ESGnrc Monte Carlocode and our custom ray tracing algorithm are idealfor simulating a true radiograph. Quantum noiseand blurring effects are the key ‘‘degradation’’components, which are needed to form a final chestX-ray image that is realistic for use in radiologicalresearch. Development of ViPRIS also permits accu-rate organ doses and effective doses to be obtainedfrom validated Monte Carlo simulations. In the fu-ture, we will automate the X-ray image productionand perform observer studies using simulated ob-servers. Such studies will include a range of nodulemorphology (spherical, lobulated, and speculated)and investigate detection performance in differentttsi
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untime considerably. Previous work ran a Montearlo simulation each time a single parameter washanged. Therefore, each simulation took manyours and multiple simulations were required to re-uce quantum noise. By running a large number ofarticles and saving the data file, statistical noiseas been significantly reduced. Once a data file haseen generated running a simulation within the C++rogram only takes a few minutes. These innova-ions allow the user to generate many simulatedmages in a few hours.
. Future plans
.1. Image quality analysis
n the future, we will demonstrate that the ViPRISimulator can be used for chest X-ray system opti-ization given a specific diagnostic task, such as tu-or diagnosis or staging. We will couple the ViPRISith a simulated model observer to quantify theuality of the images produced with respect to tu-or detectability. Measures of image quality withespect to tumor detection have been studied ex-ensively by other groups [20,21]. Our plans are touild on their work. We are in the process of eval-ating the use of several observer models includingotelling observer, channelized Hotelling observer,nd a non-prewhitening matched filter with an eye
ypes of anatomic backgrounds. ViPRIS is expectedo be a powerful tool to study the intricate relation-hip between dose and image quality in radiologicalmaging.
cknowledgments
e wish to thank Dr. Walter Huda, Dr. Kent Ogden,nd Dr. Ernest Scalzetti of the Department ofadiology, State University of New York, Upstateedical University, for their guidance and input onhe clinical realism of the simulator.
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