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Jan 2012

Volume 39, Issue 1, pp. 1-564

Spotlight Figure

Med. Phys. 39, 237 (2012); http://dx.doi.org/10.1118/1.3668059 (9 pages)

Quentin Diot, Brian Kavanagh, Robert Timmerman, and Moyed Miften
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POINT/COUNTERPOINT: Medical Physics residency programs in nonacademic facilities should affiliate themselves with a university-based program

Jatinder Saini, M.S., Jason R. Sherman, M.S., and Colin G. Orton, Ph.D., Moderator

Med. Phys. 39, 1 (2012); http://dx.doi.org/10.1118/1.3658739 (3 pages)

Online Publication Date: 9 December 2011

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Abstract Unavailable
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01.40.Di Course design and evaluation
87.85.-d Biomedical engineering

RADIATION IMAGING PHYSICS: Fast time-of-flight camera based surface registration for radiotherapy patient positioning

Simon Placht, Joseph Stancanello, Christian Schaller, Michael Balda, and Elli Angelopoulou

Med. Phys. 39, 4 (2012); http://dx.doi.org/10.1118/1.3664006 (14 pages)

Online Publication Date: 9 December 2011

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Purpose: This work introduces a rigid registration framework for patient positioning in radiotherapy, based on real-time surface acquisition by a time-of-flight (ToF) camera. Dynamic properties of the system are also investigated for future gating/tracking strategies.
Methods: A novel preregistration algorithm, based on translation and rotation-invariant features representing surface structures, was developed. Using these features, corresponding three-dimensional points were computed in order to determine initial registration parameters. These parameters became a robust input to an accelerated version of the iterative closest point (ICP) algorithm for the fine-tuning of the registration result. Distance calibration and Kalman filtering were used to compensate for ToF-camera dependent noise. Additionally, the advantage of using the feature based preregistration over an “ICP only” strategy was evaluated, as well as the robustness of the rigid-transformation-based method to deformation.
Results: The proposed surface registration method was validated using phantom data. A mean target registration error (TRE) for translations and rotations of 1.62 ± 1.08 mm and 0.07° ± 0.05°, respectively, was achieved. There was a temporal delay of about 65 ms in the registration output, which can be seen as negligible considering the dynamics of biological systems. Feature based preregistration allowed for accurate and robust registrations even at very large initial displacements. Deformations affected the accuracy of the results, necessitating particular care in cases of deformed surfaces.
Conclusions: The proposed solution is able to solve surface registration problems with an accuracy suitable for radiotherapy cases where external surfaces offer primary or complementary information to patient positioning. The system shows promising dynamic properties for its use in gating/tracking applications. The overall system is competitive with commonly-used surface registration technologies. Its main benefit is the usage of a cost-effective off-the-shelf technology for surface acquisition. Further strategies to improve the registration accuracy are under development.
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87.57.nj Registration
06.20.fb Standards and calibration
02.60.-x Numerical approximation and analysis
87.53.Jw Therapeutic applications, including brachytherapy
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EDITORIAL: Medical Physics open access papers

William Hendee, Editor

Med. Phys. 39, i (2012); http://dx.doi.org/10.1118/1.3670366 (2 pages)

Online Publication Date: 15 December 2011

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Abstract Unavailable
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01.30.Ee Monographs and collections
87.00.00 Biological and medical physics

RADIATION IMAGING PHYSICS: A forward bias method for lag correction of an a-Si flat panel detector

Jared Starman, Carlo Tognina, Larry Partain, and Rebecca Fahrig

Med. Phys. 39, 18 (2012); http://dx.doi.org/10.1118/1.3664004 (10 pages)

Online Publication Date: 9 December 2011

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Purpose: Digital a-Si flat panel (FP) x-ray detectors can exhibit detector lag, or residual signal, of several percent that can cause ghosting in projection images or severe shading artifacts, known as the radar artifact, in cone-beam computed tomography (CBCT) reconstructions. A major contributor to detector lag is believed to be defect states, or traps, in the a-Si layer of the FP. Software methods to characterize and correct for the detector lag exist, but they may make assumptions such as system linearity and time invariance, which may not be true. The purpose of this work is to investigate a new hardware based method to reduce lag in an a-Si FP and to evaluate its effectiveness at removing shading artifacts in CBCT reconstructions. The feasibility of a novel, partially hardware based solution is also examined.
Methods: The proposed hardware solution for lag reduction requires only a minor change to the FP. For pulsed irradiation, the proposed method inserts a new operation step between the readout and data collection stages. During this new stage the photodiode is operated in a forward bias mode, which fills the defect states with charge. A Varian 4030CB panel was modified to allow for operation in the forward bias mode. The contrast of residual lag ghosts was measured for lag frames 2 and 100 after irradiation ceased for standard and forward bias modes. Detector step response, lag, SNR, modulation transfer function (MTF), and detective quantum efficiency (DQE) measurements were made with standard and forward bias firmware. CBCT data of pelvic and head phantoms were also collected.
Results: Overall, the 2nd and 100th detector lag frame residual signals were reduced 70%–88% using the new method. SNR, MTF, and DQE measurements show a small decrease in collected signal and a small increase in noise. The forward bias hardware successfully reduced the radar artifact in the CBCT reconstruction of the pelvic and head phantoms by 48%–81%.
Conclusions: Overall, the forward bias method has been found to greatly reduce detector lag ghosts in projection data and the radar artifact in CBCT reconstructions. The method is limited to improvements of the a-Si photodiode response only. A future hybrid mode may overcome any limitations of this method.
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87.57.Q- Computed tomography
87.85.Ox Biomedical instrumentation and transducers, including micro-electro-mechanical systems (MEMS)
85.30.Kk Junction diodes
85.60.Dw Photodiodes; phototransistors; photoresistors
85.60.Gz Photodetectors (including infrared and CCD detectors)
87.59.-e X-ray imaging

RADIATION IMAGING PHYSICS: Computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

Berkman Sahiner, Heang-Ping Chan, Lubomir M. Hadjiiski, Mark A. Helvie, Jun Wei, Chuan Zhou, and Yao Lu

Med. Phys. 39, 28 (2012); http://dx.doi.org/10.1118/1.3662072 (12 pages)

Online Publication Date: 9 December 2011

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Purpose: To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system.
Methods: IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the microcalcifications in a cluster candidate and the cluster shape were used to reduce the number of FPs.
Results: The prescreening stage detected a cluster seed object in 94% of the biopsied microcalcification clusters at a threshold of 100 cluster seed objects per DBT volume. After clustering, the detection sensitivity was 90% at 15 marks per DBT volume. After FP reduction, at 85% sensitivity, the average number of FPs estimated using the data set containing microcalcification clusters was 3.8 per DBT volume, and that estimated using the data set free of microcalcification clusters was 3.4. The detection performance for malignant microcalcification clusters was superior to that for benign clusters.
Conclusions: Our study indicates the feasibility of the 3D approach to the detection of clustered microcalcifications in DBT and that the newly designed enhancement-modulated 3D calcification response function is promising for prescreening. Further work is needed to assess the generalizability of our approach and to improve its performance.
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87.85.Pq Biomedical imaging
87.19.xj Cancer
87.57.nf Reconstruction
87.57.nm Segmentation
87.57.rh Mammography
87.63.lm Image enhancement

RADIATION THERAPY PHYSICS: Monte Carlo linear accelerator simulation of megavoltage photon beams: Independent determination of initial beam parameters

Sigrun Saur Almberg, Jomar Frengen, Arve Kylling, and Tore Lindmo

Med. Phys. 39, 40 (2012); http://dx.doi.org/10.1118/1.3668315 (8 pages)

Online Publication Date: 9 December 2011

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Purpose: To individually benchmark the incident electron parameters in a Monte Carlo model of an Elekta linear accelerator operating at 6 and 15 MV. The main objective is to establish a simplified but still precise benchmarking procedure that allows accurate dose calculations of advanced treatment techniques.
Methods: The EGSnrc Monte Carlo user codes BEAMnrc and DOSXYZnrc are used for photon beam simulations and dose calculations, respectively. A 5 × 5 cm2 field is used to determine both the incident electron energy and the electron radial intensity. First, the electron energy is adjusted to match the calculated depth dose to the measured one. Second, the electron radial intensity is adjusted to make the calculated dose profile in the penumbrae region match the penumbrae measured by GafChromic EBT film. Finally, the mean angular spread of the incident electron beam is determined by matching calculated and measured cross-field profiles of large fields. The beam parameters are verified for various field sizes and shapes.
Results: The penumbrae measurements revealed a non-circular electron radial intensity distribution for the 6 MV beam, while a circular electron radial intensity distribution could best describe the 15 MV beam. These electron radial intensity distributions, given as the standard deviation of a Gaussian distribution, were found to be 0.25 mm (in-plane) and 1.0 mm (cross-plane) for the 6 MV beam and 0.5 mm (both in-plane and cross-plane) for the 15 MV beam. Introducing a small mean angular spread of the incident electron beam has a considerable impact on the lateral dose profiles of large fields. The mean angular spread was found to be 0.7° and 0.5° for the 6 and 15 MV beams, respectively.
Conclusions: The incident electron beam parameters in a Monte Carlo model of a linear accelerator could be precisely and independently determined by the benchmarking procedure proposed. As the dose distribution in the penumbra region is insensitive to moderate changes in electron energy and angular spread, accurate penumbra measurements is feasible for benchmarking the electron radial intensity distribution. This parameter is particularly important for accurate dosimetry of mlc-shaped fields and small fields.
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87.56.bd Accelerators
87.55.kh Applications
87.55.dk Dose-volume analysis

RADIATION THERAPY PHYSICS: Dosimetric investigation of breath-hold intensity-modulated radiotherapy for pancreatic cancer

Mitsuhiro Nakamura, Shun Kishimoto, Kohei Iwamura, Takehiro Shiinoki, Akira Nakamura, Yukinori Matsuo, Keiko Shibuya, and Masahiro Hiraoka

Med. Phys. 39, 48 (2012); http://dx.doi.org/10.1118/1.3668314 (7 pages)

Online Publication Date: 9 December 2011

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Purpose: To experimentally investigate the effects of variations in respiratory motion during breath-holding (BH) at end-exhalation (EE) on intensity-modulated radiotherapy (BH–IMRT) dose distribution using a motor-driven base, films, and an ionization chamber.
Methods: Measurements were performed on a linear accelerator, which has a 120-leaf independently moving multileaf collimator with 5-mm leaf width at the isocenter for the 20-cm central field. Polystyrene phantoms with dimensions of 40 × 40 × 10 cm were set on a motor-driven base. All gantry angles of seven IMRT plans (a total of 35 fields) were changed to zero, and doses were then delivered to a film placed at a depth of 4 cm and an ionization chamber at a depth of 5 cm in the phantom with a dose rate of 600 MU/min under the following conditions: pulsation from the abdominal aorta and baseline drift with speeds of 0.2 mm/s (BD0.2mm/s) and 0.4 mm/s (BD0.4mm/s). As a reference for comparison, doses were also delivered to the chamber and film under stationary conditions.
Results: In chamber measurements, means ± standard deviations of the dose deviations between stationary and moving conditions were −0.52% ± 1.03% (range: −3.41–1.05%), −0.07% ± 1.21% (range: −1.88–4.31%), and 0.03% ± 1.70% (range: −2.70–6.41%) for pulsation, BD0.2mm/s, and BD0.4mm/s, respectively. The γ passing rate ranged from 99.5% to 100.0%, even with the criterion of 2%/1 mm for pulsation pattern. In the case of BD0.4mm/s, the γ passing rate for four of 35 fields (11.4%) did not reach 90% with a criterion of 3%/3 mm. The differences in γ passing rate between BD0.2mm/s and BD0.4mm/s were statistically significant for each criterion. Taking γ passing rates of > 90% as acceptable with a criterion of 3%/3 mm, large differences were observed in the γ passing rate between the baseline drift of ≤5 mm and that of >5 mm (minimum γ passing rate: 92.0% vs 82.7%; p < 0.01).
Conclusions: This study suggested that the baseline drift of >5 mm should be avoided in the BH–IMRT.
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87.55.dk Dose-volume analysis
87.19.xj Cancer
87.53.Jw Therapeutic applications, including brachytherapy
87.19.Wx Pneumodyamics, respiration

RADIATION IMAGING PHYSICS: A voxel-based finite element model for the prediction of bladder deformation

Xiangfei Chai, Marcel van Herk, Maarten C. C. M. Hulshof, and Arjan Bel

Med. Phys. 39, 55 (2012); http://dx.doi.org/10.1118/1.3668060 (11 pages)

Online Publication Date: 9 December 2011

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Purpose: A finite element (FE) bladder model was previously developed to predict bladder deformation caused by bladder filling change. However, two factors prevent a wide application of FE models: (1) the labor required to construct a FE model with high quality mesh and (2) long computation time needed to construct the FE model and solve the FE equations. In this work, we address these issues by constructing a low-resolution voxel-based FE bladder model directly from the binary segmentation images and compare the accuracy and computational efficiency of the voxel-based model used to simulate bladder deformation with those of a classical FE model with a tetrahedral mesh.
Methods: For ten healthy volunteers, a series of MRI scans of the pelvic region was recorded at regular intervals of 10 min over 1 h. For this series of scans, the bladder volume gradually increased while rectal volume remained constant. All pelvic structures were defined from a reference image for each volunteer, including bladder wall, small bowel, prostate (male), uterus (female), rectum, pelvic bone, spine, and the rest of the body. Four separate FE models were constructed from these structures: one with a tetrahedral mesh (used in previous study), one with a uniform hexahedral mesh, one with a nonuniform hexahedral mesh, and one with a low-resolution nonuniform hexahedral mesh. Appropriate material properties were assigned to all structures and uniform pressure was applied to the inner bladder wall to simulate bladder deformation from urine inflow. Performance of the hexahedral meshes was evaluated against the performance of the standard tetrahedral mesh by comparing the accuracy of bladder shape prediction and computational efficiency.
Results: FE model with a hexahedral mesh can be quickly and automatically constructed. No substantial differences were observed between the simulation results of the tetrahedral mesh and hexahedral meshes (<1% difference in mean dice similarity coefficient to manual contours and <0.02 cm difference in mean standard deviation of residual errors). The average equation solving time (without manual intervention) for the first two types of hexahedral meshes increased to 2.3 h and 2.6 h compared to the 1.1 h needed for the tetrahedral mesh, however, the low-resolution nonuniform hexahedral mesh dramatically decreased the equation solving time to 3 min without reducing accuracy.
Conclusions: Voxel-based mesh generation allows fast, automatic, and robust creation of finite element bladder models directly from binary segmentation images without user intervention. Even the low-resolution voxel-based hexahedral mesh yields comparable accuracy in bladder shape prediction and more than 20 times faster in computational speed compared to the tetrahedral mesh. This approach makes it more feasible and accessible to apply FE method to model bladder deformation in adaptive radiotherapy.
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87.85.jc Electrical, thermal, and mechanical properties of biological matter
87.19.rd Elastic properties
87.53.Jw Therapeutic applications, including brachytherapy

RADIATION IMAGING PHYSICS: Prior image constrained compressed sensing: Implementation and performance evaluation

Pascal Thériault Lauzier, Jie Tang, and Guang-Hong Chen

Med. Phys. 39, 66 (2012); http://dx.doi.org/10.1118/1.3666946 (15 pages)

Online Publication Date: 9 December 2011

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Purpose: Prior image constrained compressed sensing (PICCS) is an image reconstruction framework which incorporates an often available prior image into the compressed sensing objective function. The images are reconstructed using an optimization procedure. In this paper, several alternative unconstrained minimization methods are used to implement PICCS. The purpose is to study and compare the performance of each implementation, as well as to evaluate the performance of the PICCS objective function with respect to image quality.
Methods: Six different minimization methods are investigated with respect to convergence speed and reconstruction accuracy. These minimization methods include the steepest descent (SD) method and the conjugate gradient (CG) method. These algorithms require a line search to be performed. Thus, for each minimization algorithm, two line searching algorithms are evaluated: a backtracking (BT) line search and a fast Newton-Raphson (NR) line search. The relative root mean square error is used to evaluate the reconstruction accuracy. The algorithm that offers the best convergence speed is used to study the performance of PICCS with respect to the prior image parameter α and the data consistency parameter λ. PICCS is studied in terms of reconstruction accuracy, low-contrast spatial resolution, and noise characteristics. A numerical phantom was simulated and an animal model was scanned using a multirow detector computed tomography (CT) scanner to yield the projection datasets used in this study.
Results: For λ within a broad range, the CG method with Fletcher-Reeves formula and NR line search offers the fastest convergence for an equal level of reconstruction accuracy. Using this minimization method, the reconstruction accuracy of PICCS was studied with respect to variations in α and λ. When the number of view angles is varied between 107, 80, 64, 40, 20, and 16, the relative root mean square error reaches a minimum value for α ≈ 0.5. For values of α near the optimal value, the spatial resolution of the reconstructed image remains relatively constant and the noise texture is very similar to that of the prior image, which was reconstructed using the filtered backprojection (FBP) algorithm.
Conclusions: Regarding the performance of the minimization methods, the nonlinear CG method with NR line search yields the best convergence speed. Regarding the performance of the PICCS image reconstruction, three main conclusions can be reached. (1) The performance of PICCS is optimal when the weighting parameter of the prior image parameter is selected to be near α = 0.5. (2) The spatial resolution measured for static objects in images reconstructed using PICCS from undersampled datasets is not degraded with respect to the fully-sampled reconstruction for α near its optimal value. (3) The noise texture of PICCS reconstructions is similar to that of the prior image, which was reconstructed using the conventional FBP method.
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87.57.nf Reconstruction
02.60.-x Numerical approximation and analysis

RADIATION MEASUREMENT PHYSICS: Evaluation of the accuracy of 3DVH software estimates of dose to virtual ion chamber and film in composite IMRT QA

Arthur J. Olch

Med. Phys. 39, 81 (2012); http://dx.doi.org/10.1118/1.3666771 (6 pages)

Online Publication Date: 9 December 2011

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Purpose: A novel patient-specific intensity modulated radiation therapy (IMRT) QA system, 3DVH software and mapcheck 2, purports to be able to use diode array-measured beam doses and the patient’s DICOM RT plan, structure set, and dose files to predict the delivered 3D dose distribution in the patient for comparison to the treatment planning system (TPS) calculated doses. In this study, the composite dose to an ion chamber and film in phantom predicted by the 3DVH and mapcheck 2 system is compared to the actual measured chamber and film doses. If validated in this context, then 3DVH can be used to perform an equivalent dose analysis as that obtained with film dosimetry and ion chamber-based composite IMRT QA. This is important for those losing their ability to perform film dosimetry for true composite IMRT QA and provides a measure of confidence in the accuracy of 3DVH 3D dose calculations which may replace phantom-based IMRT QA.
Methods: The dosimetric results from 15 consecutive patient-specific IMRT QA tests performed by composite field irradiation of ion chamber and EDR2 film in a solid water phantom were compared to the predicted doses for those virtual detectors based on the calculated 3D dose by the 3DVH software using mapcheck 2 measured doses of each beam within each plan. For each of the 15 cases, immediately after performing the ion chamber plus film measurements, the mapcheck 2 was used to measure the dose for each beam of the plan. The dose to the volume of the virtual ion chamber and the dose distribution in the plane of the virtual film calculated by the 3DVH software was extracted. The ratio of the measured to 3DVH or eclipse-predicted ion chamber doses was calculated. The same plane in the phantom measured using film and calculated with eclipse was exported from 3DVH and the 2D gamma metric was used to compare the relationship between the film doses and the eclipse or 3DVH predicted planar doses. Also, the 3D gamma value was calculated in the 3DVH software which compares the eclipse dose to the 3DVH predicted dose distribution. For the 2D and 3D gamma metrics, 2% dose and 2 mm distance to agreement (DTA) were used. In addition, a simple dose difference was performed using either a 2% or 3% dose difference tolerance.
Results: The mean ratio ± standard deviation of the measured vs 3DVH or vs eclipse-predicted dose to the ion chamber was 1.013 ± 0.015 and 1.003 ± 0.012, respectively. For 3DVH vs eclipse, the mean percentage of pixels failing the 3D gamma metric was 1.2% ± 1.4% while the failure rate for the 2D gamma metric was 1.1% ± 0.9%. When either 3DVH or eclipse was compared to EDR2 film, the gamma failure rate was 2.3% ± 2.0% and 1.6% ± 1.7%, respectively. Mean dose difference failures were 9%–27% ± 5%–15% for 2 or 3% dose difference tolerances, depending on the combination of systems tested. No statistically significant differences were found for any of the planar dosimetric comparisons.
Conclusions: 3DVH + mapcheck 2 predicts the same absolute dose, the percent of pixels failing the gamma metric, and the percent of pixels failing 2% or 3% dose difference tolerance tests as one would have obtained had one made measurements in solid water phantom using an ion chamber and coronal film instead of a diode array. This is also a necessary although not sufficient condition for validation of the accuracy of 3DVH predictions of the 3D dose using beam-by-beam measurements.
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87.55.Qr Quality assurance in radiotherapy
87.53.Bn Dosimetry/exposure assessment
87.53.Jw Therapeutic applications, including brachytherapy
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Medical Physics is an official science journal of the AAPM and of the COMP/CCPM/IOMP
William R. Hendee, Editor
Departments of Radiology, Radiation Oncology, Biophysics, Community and Public Health, Medical College of Wisconsin
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College Park, Maryland 20740
301-209-3352

© 2012, American Association of Physicists in Medicine. Individual readers of this journal, and nonprofit libraries acting for them, are freely permited to make fair use of the material in it, such as to copy an article for use in teaching or research. (For other kinds of copying see "Copying Fees.") Permission is granted to quote from this journal in scientific works with the customary acknowledgment of the source. To reprint a figure, table, or other excerpt requires, in addition, AAPM may require that permission also be obtained from one of the authors. Address inquiries and notices to Penny Slattery, Journal Manager, Medical Physics Journal, AAPM, One Physics Ellipse, College Park, MD 20740-3846; email: journal@aapm.org.

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