bluuebunny/crossref_metadata_2025 huggingface dataset:
TH‐A‐213AB‐11: Deliverable Optimization Incorporating Monitor Unit Constraints for Intensity Modulated Proton Therapy Treatment Planning
Authors: Li X , Cao W , Wang X , Li Y , Zhu X , Sahoo N , Gillin M , Lim G , Zhang X
Published: June 2012

Purpose: Delivery constraints on monitor units (MUs) are not currently considered during optimization of intensity modulated proton therapy (IMPT) planning in our treatment planning system (TPS). This may result in differences between the delivered and ideally optimized dose distributions. The purpose of this study is to develop a deliverable optimization algorithm incorporating MU constraints (DOMU) in IMPT treatment planning and evaluate its clinical applicability. Methods: A novel objective formula was incorporated with the limited‐memory Broyden‐Fletcher‐Goldfarb‐Shannon (L‐BFGS) algorithm to directly optimize the deliverable IMPT plan with minimum MU constraint (0.005 MUs per spot). Treatment plans for a homogeneous phantom were generated for different spot spacings, using optimization without MU constraints and post‐processing (OMPP) and DOMU. A treatment plan for a head‐neck cancer patient prescribed to 63 Gy was also designed with DOMU using different spot spacings. For all plans, the dose‐volume‐histograms (DVHs) were analyzed to evaluate the benefits of the DOMU. Results: For the regular shaped homogeneous phantom, only the plan generated by OMPP using 5mm spacing showed pronounced dose distortion compared to ideally optimized dose. Plan using 7mm spacing still produced accepted target coverage. For the patient study, plans generated by OMPP using both 5mm and 7mm spacing displayed significant dose distortion and unacceptable target coverage. The dose covering 99% of the target volume was 60.2 Gy and 61.7 Gy for 5mm and 7mm spot spacing plans generated by DOMU, an increase of 18.8 Gy and 18.4 Gy compared with plans generated by OMPP. The parotid sparing of DOMU generated plans in 5 mm spot spacing was significantly better than that in 7 mm spot spacing indicating the importance of using smaller spacing to improve the IMPT quality. Conclusion: The DOMU algorithm, which incorporates the minimum MU constraints into the optimization, can reduce the dose distortion caused by post‐processing process and achieve the optimum delivered dose.
TH‐A‐BRB‐03: Tumor Control Probability Models for Hypofractionated Therapy: Known Unknowns, Unknown Unknowns, and Comparison with Data
Authors: Deasy J , Jeong J
Published: June 2012

In order to better understand factors that affect tumor dose response to hypofractionated and standard fractionation (∼ 2 Gy/day), we recently constructed a state‐based model to predict radiotherapy tumor outcomes. Put simply the model assumes that each small volume of the tumor at the beginning of therapy (a ‘tumorlet’) has a given, unchanging, supply level of oxygen and glucose. Cells are divided into those that have both adequate oxygen and adequate glucose (and are therefore at least intermittently proliferating), cells that only have adequate glucose (radiobiologically hypoxic but surviving), and those that are dying due to a lack of oxygen and glucose. The model is just complicated enough to take into account measured cell loss factors, growth fractions, and radiosensitivity as a function of cell cycle. The model naturally reproduces the effects of reoxygenation, accelerated clonogen proliferation, and regression as a function of the local tumorlet cell‐loss factor and growth fraction. Although the model appears to reproduce behavior consistent with response to standard fractionation, it does not adequately explain response to single‐ or few‐fraction delivery, where hypoxic cell survival is predicted to be orders‐ of‐magnitude too high. It is unlikely that a too‐high value of alpha/beta by itself explains the discrepancy as the ratio is expected to increase under hypoxic conditions by a factor equal to the oxygen enhancement ratio. These results are in agreement with previously published calculations by Brown et al. (IJROBP (2010) 78: 323‐327) using a different model of tumor response. We will discuss uncertainties in the model, including effects that have been left out, such as variations in vascular delivery.

Learning Objectives:

1. Understanding factors expected to impact radiotherapy response, including radiosensitivity, repair, reoxygenation, the cell loss factor, the growth fraction, and hypoxia.

2. Understand basic mathematical formulations that can capture radiosensitivity and repair.
TH‐A‐BRB‐04: Vascular Response to Microbeam Radiation Therapy in Vivo Using a Murine Window Chamber Tumor Model
Authors: Dewhirst M , Fontanella A , Palmer G , Boss M , Zhang J , Hadsell M , Chang S
Published: June 2012

Microbeam Radiation Therapy (MRT) has shown a marked tumor‐specific effect. The tissue‐sparing property of this unique treatment is possibly facilitated by efficient normal‐vessel repair mechanisms, contrary to the catastrophic disruption of poorly regulated tumor‐associated vasculature. However, significant controversy exists with regard to the role of endothelial cell death in radiation response. It is possible that the direct killing of tumor vasculature may increase radiation response within the tumor due to either oxygen/nutrient deprivation or through the “bystander effect”. The aim of this study was to characterize the vascular response to MRT in vivo using a murine window chamber model over an extended time course. MRT treatment at various doses (300 micron single beam, 0Gy, 50Gy, 80Gy) was applied to the murine window chamber tumor model. Changes in tumor‐associated vasculature after the MRT treatment utilizing intravital microscopy were profound. A robust angiogenic response was clearly present along the microbeam track in what appeared to be clusters of vessel proliferation following MRT of 50 and 80Gy. This distinctive pattern of angiogenesis was imaged through Day 7, as the irradiated track and regional microenvironment were infiltrated with a dense vascular network. In more recent experiments, we have observed infiltration of tumor cells along pre‐existing vascular networks adjacent to the microbeam, to distant unirradiated sites. These types of responses were not seen in controls or in chambers following wide beam irradiation to the entire window at comparable doses. This behavior strongly suggests that the treatment is promoting epithelial mesenchymal transition, angiogenesis and local invasion. Although these observations are for microbeam treatments, the biology being observed could equally be applied to the issue of marginal miss, which is of concern for high dose conformal radiotherapy. The window chamber model provides a novel approach to direct visualization of the longitudinal vascular changes following treatment with MRT. These findings suggest that radiation induced angiogenesis at sites adjacent to the MRT beam may play an important role in modifying the tumor microenvironment. Such a response may have significant implications with regard to the understood mechanism of radiation‐induced cell death.

Learning Objectives:

1. Be able to describe concept of microbeam irradiation

2. Understand underlying mechanisms and implications of driving angiogenic response of tumor after irradiation

This work was supported by NIH/NCI grant CA40355 and DoD grant BC083195.
TH‐A‐BRA‐01: Contouring Variations and Their Impact on Dose‐Volume Histograms in Non‐Small‐Cell Lung Cancer Radiotherapy: Analysis of a Multi‐ Institutional Pre‐Clinical Trial Planning Study
Authors: Cui Y , Chen W , Kong F , Appenzoller L , Beatty R , Maxim P , Ritter T , Sohn J , Higgins J , Yu Y , Galvin J , Xiao Y
Published: June 2012

Purpose: To quantify variations in target and normal structure contouring and evaluate dosimetric impact of these variations on conformal radiotherapy plans in non‐small‐cell lung cancer(NSCLC) cases.Methods: Two NSCLC cases were distributed and highly conformal radiotherapy plans generated by multiple institutions as a pre‐clinical trial planning study for RTOG protocol 1106 were used in this analysis. The same PET‐CT scans were provided to each institution for contouring and planning (prescription dose 74Gy). Eleven plans for Case1 and seven plans for Case2 were collected. Based on the contours from multiple sites, a consensus structure set was initially generated using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm, and then reviewed by physicians from participating institutions for agreement. The volume variation among institution contours and the deviation of them from consensus contour were analyzed. The dose‐volume histograms(DVH) for individual institution plans were re‐calculated using consensus contours and compared with their submitted DVH. Tumor Control Probability (TCP) was also calculated using both DVHs.Results: Planning target volumes(PTV) from different institutions ranged from 349cc to 522cc in Case1, and from 339cc to 686cc in Case2. The mean surface distance and Dice's coefficient between institutions' PTV and consensus PTV were 2.6±0.7mm(1.9– 4.4mm)(mean±SD(range)) and 92.4±3.3%(83.6‐95.4%) respectively for Case1, and 4.7±2.2mm(2.3–8.1mm) and 86.4±7.6%(74.7–94.5%) for Case2. For normal structures, brachial plexus presented large variation in contouring(Dice's coefficient below 50%), cord and esophagus presented moderate variation(Dice's coefficient around 78%), and lungs and heart presented least variation. The PTV D95% changed from 69.9±4.0Gy/74.2±0.5Gy(Case1/Case2) to 66.3±7.7Gy/59.3±18.9Gy when consensus PTV was used for re‐calculation. Maximum cord dose changed from 47.8±3.2Gy/35.3±9.7Gy to 51.1±4.3Gy/36.6±10.8Gy. TCP decreased from 84.3±6.5%/87.8±1.9% to 72.6±24.7%/52.8±44.4% with consensus contours.Conclusions: The amount of contouring variations in two NSCLC cases was presented and analysis shows the impact on DVH parameters can be significant. Quality assurance of contouring is essential for successful multi‐institutional clinical trial. This project is funded, in part, under a grant with the Pennsylvania Department of Health. The Department specifically declaims responsibility for any analyses, interpretations or conclusions.
TH‐A‐BRA‐02: Dose Uncertainties in IMPT for Oropharyngeal Cancer in the Presence of Anatomical, Setup and Range Errors
Authors: Kraan A , van de Water S , Teguh D , Al‐Mamgani A , Madden T , Kooy H , Heijmen B , Hoogemans M
Published: June 2012

Purpose: Setup, range and anatomical uncertainties influence the dose delivered with proton pencil‐beams, but a quantification of these errors for intensity‐modulated proton therapy (IMPT) for oropharyngeal cancer is lacking. The purpose of this work is to quantify these effects. We also investigate whether treatment plans are more robust against errors when more beam directions are applied. Methods: We used an in‐house developed inverse treatment planning system for proton pencil‐beam scanning, which performs multi‐criteria optimization. First, treatment plans for 3, 5, and 7 beam directions were created for 5 oropharyngeal cancer patients. A simultaneous‐integrated boost technique was used, prescribing 66Gy and 54Gy to high and low dose regions, respectively, delivered in 30 fractions. Second, 300 treatment simulations were performed, recalculating the dose while including uncertainties. Anatomical uncertainties were taken into account by using two CT scans per patient. For setup errors an online setup‐protocol was simulated. DVH parameters were used for dose evaluation. Results: The treatment plans were of high quality, with good target coverage and excellent OAR sparing. However, setup, range and anatomical uncertainties can lead to large differences between the planned and delivered dose. For the 3‐beam plans, the expected V95% of the high‐ dose CTV reduced from 100% (planned) to on average 92%(range:82–98%), and the expected V107% increased from 1.3% (planned) to on average 4.2%(range:0.1%‐6.7%). The expected V95% of the low‐dose CTV reduced from 100% (planned) to on average 94% (range:86‐99%). The dose to the OARs generally increased. The plans with more beam directions were not more robust against errors (p>0.05). Conclusions: Setup, range and anatomical uncertainties in IMPT for oropharyngeal cancer patients can lead to considerable underdosage of the target volume, which should be accounted for by replanning and/or robust optimization techniques. Robustness is not achieved by increasing the number of beam directions included in the treatment plan.
TH‐A‐BRA‐03: Daily IGRT QA Phantom for MR‐Guided Linacs
Authors: Moseley D , Stanescu T
Published: June 2012

Purpose: To design a novel phantom compatible with multiple imaging modalities (kV, MV and MRI) for the daily quality assurance (QA) of an MR‐guided linac. Methods: At our institution, we are in the process of installing a decoupled MR‐linac system, consisting of a 1.5T Siemens Espree MR scanner on rails that can be moved and image in the immediate proximity of a Varian TrueBeam linac. The two components are connected via a robotic patient couch. For this configuration, both MR and x‐ray based (kV, MV) imaging modalities will be available and will have to be mutually correlated to confirm the robustness of the RT ensemble. The authors modified the design of on an existing daily QA phantom that is compatible with kV radiographs, MV EPIDs and kV CBCT. New features were added to make the phantom also visible on MRI. The overall design was optimized by means of numerical simulations to remove any significant MR image‐ related artifacts due to magnetic susceptibility‐induced effects. An image software analysis tool was also developed to process the data and capture all kV/MV/MRI unique characteristics. In addition, the software provides the capability of record and track over time the performance of the tests via control charts. Results: The phantom was developed to test kV/MV/MR system coincidence, 3D CBCT and 3D MR independent and cross‐modality mutual registration, kV/MV and MR projection images, kV/MV coincidence, MR/linac laser and light field coincidence, remote table adjustments. Conclusions: A novel phantom along with an image processing software package was developed for the routine daily QA of a linac‐MR on rails radiotherapy system. D. Moseley has a license agreement with Modus Medical, Inc.
TH‐A‐BRA‐04: Evaluation of Three IGRT Approaches for Prostate Cancer Treatment
Authors: Qin A , Liang J , Yan D
Published: June 2012

Purpose: To evaluate two online IGRT and one hybrid adaptive modification for prostate cancer treatment with onboard CBCT imager. Methods: Two online IGRT and one hybrid adaptive modification were simulated and evaluated retrospectively using daily CBCT images obtained from 5 prostate cancer patients treated with total dose 64Gy in 20 fractions. For each daily treatment, two CBCTs obtained at the pre‐ and post‐treatment delivery were used in the study. Online IGRT techniques include (1) Online‐ correction: a pre‐treatment IMRT plan with 3mm CTV‐to‐PTV margin was delivered following the CBCT imaging and online target position correction; and (2) Online‐planning: an online inverse plan designed on the pre‐ treatment CBCT image with 3mm target margin was accomplished and delivered. The Hybrid‐adaptation consists of the online prostate position correction and delivery of a pre‐treatment IMRT plan with no target margin for the first week of the treatment, and an offline adaptive inverse planning with using the planning CT and the first week of post‐treatment CBCT images for the remaining treatment. Treatment dose for all 3 approaches were constructed using all the post‐treatment CBCT images. Evaluations were all performed using the treatment dose distribution in organs of interest for all patients. Results: The minimal delivered dose (D99) in CTV (prostate + seminal vesicle) is in the range of [51.6, 65.6]Gy for Online‐ correction; [63.2, 66.1]Gy for Online‐planning; and [62.8, 66.1]Gy for the Hybrid‐adaptation. The rectal volume with 62.6Gy, V62.6, for Online‐ correction, Online‐planning, Hybrid‐adaptation are 0.7+/−1.0%, 0.4+/−0.2%, 0.5+/−1.0%; the rectal V59.4 are 2.1+/−1.8%, 1.5+/‐1.1%, 1.6+/−1.9%; and V56.5 are 4.0+/−3.2%, 3.0+/−2.2%, 3.5+/−3.2%. The bladder volume dose, V64.1, are 2.3+/−2.2%, 0.5+/−0.6%, 0.4+/−0.3%, and V59.4 are 5.2+/−3.6%, 2.0+/−1.1%, 1.6+/−0.8% respectively for the 3 approaches. Conclusions: Both Online‐planning and Hybrid‐adaptation achieved comparable target coverage and normal tissue sparing, and were superior to Online‐correction. However, Hybrid‐adaptation is more efficiency in clinical practice. This research was partially supported by Elekta.
TH‐A‐BRA‐07: Optimization of Gamma Parameters for EPID‐Based Pretreatment Detection of Delivery Errors in IMRT Fields
Authors: Gordon J , Siebers J
Published: June 2012

Purpose: Gamma analysis is widely used for intensity modulated radiation therapy (IMRT) quality assurance. However, when calculated on a discrete grid with conventional dose/distance criteria of f_dose = 3%/f_dist = 3mm, gamma exhibits poor correlation with dose errors and poor specificity/sensitivity for detecting fluence deviations. This work shows how gamma criteria may be optimized for error detection, and uses repeat images of 11 clinical IMRT fields to quantify delivery errors that can be reliably detected via optimal analysis of electronic portal (EPID) images. Methods: Multiple EPID images were acquired of 11 clinical IMRT fields. For each field, images were aligned and averaged, generating a reference image. Gamma was computed for individual images relative to reference using two methods: discrete grid (0.37mm̂2 pixel size) and continuous (interpolation‐ free). Rectangular errors were inserted into measured images, and receiver operating characteristic (ROC) studies determined parameters that yield maximum image classification accuracy. Classification was deemed reliable if =95% of images were correctly classified. Results: Key parameters are f_grad=0.37xf_dose/f_dist and the gamma distribution tail cutoffs used to classify images as errored or error‐free. f_grad controls the ‘looseness' of the distance‐to‐agreement (DTA) search. Classification is optimal when f_grad is loose enough to ignore normal image‐to‐image variations, but tight enough to detect delivery errors. Conventional 3%/3mm criteria give sub‐ optimal error detection. Even with optimal parameters, calculation on a discrete grid introduces sufficient sampling errors as to seriously degrade error detection. If continuous gamma calculation is employed with optimal parameters, one can reliably detect small errors, e.g., dose deviations of 5‐ 10% over areas of 3×3 pixels (∼1mm̂2) at isocenter. Conclusions: Optimal detection of IMRT delivery errors using EPID images is achieved with continuous gamma, and optimal criteria (f_dose, f_dist and classification cutoffs). Caution must be exercised when gamma is calculated on a discrete grid using conventional 3%/3mm criteria. Research funding from Varian Medical Systems.
TH‐A‐BRA‐09: Statistical Assessment of Plan Robustness Under Uncertainties: IMRT Vs. Proton Therapy
Authors: Park P , Cheung J , Chen Y , Zhu X , Sahoo N , Court L , Mohan R , Dong L
Published: June 2012

Purpose: Fair comparisons between x‐ray IMRT and Proton therapy plans should include assessment of plan robustness. However, this is difficult because the dose distribution can be altered in the presence of uncertainties in different ways for each modality. In order to fairly evaluate plan's robustness for both arms, we propose the use of statistical parameters to assess the probabilistic distribution of dose and DVHs under setup and range uncertainties. Methods: A lung cancer patient currently undergoing randomized trial was selected for this study. Physician approved x‐ray IMRT and passively scattered proton plans were created under identical planning objectives. For each of these plans, a total of 500 dose calculations were performed that simulated 50 virtual treatments with each treatment having 10 fractions. We found random error quickly converged within 10 fractions. A random setup error was introduced for each of the multiple fractions while a static systematic setup and range errors were introduced over simulated treatments. In calculating the doses, we assumed a static dose distribution for the IMRT plan. For Proton plans, a new dose calculation was performed for every simulation using a fast dose calculation method. Based on these simulations, the expectation value of DVH (E[DVH]) and standard deviation (SD[DVH]) were calculated. Both SD[DVH] and the difference between E[DVH] and the original DVH characterize the plan's robustness. Results: The E[DVH] showed that the V100% target coverage was 0.5% and 4% less than the original coverage for IMRT and Proton plans, respectively. The SD[DVH] near the prescription dose bin were 1% and 3% for IMRT and Proton plans respectively. Conclusions: Our results showed that the E[DVH] and SD[DVH] are viable options for evaluating plan robustness for both treatment modalities. Clinical prescription and dose objectives should be evaluated under uncertainty to assure treatment goals can be realized against uncertainties considered. Supported in part by NCI P01CA021239‐29A1.
TH‐A‐213CD‐05: NEQ(k) The Signal and Noise Transfer Properties in Differential Phase Contrast CT
Authors: Tang X , Yang Y , Tang S
Published: June 2012

Purpose: As an emerging x‐ray tomographic imaging method for preclinical and eventually clinical applications, the grating‐based x‐ray differential phase contrast (DPC) CT is drawing increasing interest for its research and development. It has been hoped that the potential ▵S(k) in the DPC‐CT can be significantly larger than its counterpart in the conventional attenuation‐ based CT. We investigate the DPC‐CT's NEQ(k) under the ideal observer framework in this study. Methods: In general, the signal and noise transfer properties of an imaging system is determined by its modulation transfer function MTF(k) and noise power spectrum NPS(k), respectively, which jointly characterize the spectrum of noise equivalent quanta NEQ(k). The eventual performance of an imaging method is determined by its detectability index or squares signal‐to‐noise ratio defined as an integration of the product of inputting signal, i.e., the contrast between the object to be imaged and the background, fed into the system to implement the imaging method and the system's NEQ(k). Through theoretical analysis, modeling, simulation, we derive and evaluate the NEQ(k) of the DPC‐CT and compare it with that of the conventional CT. Results: Preliminary data show that, there exist only a modest difference in the signal transfer property MTF(k) between the DPC‐CT and conventional CT. However, owing to an adoption of the Hilbert filtering for image reconstruction, the noise transfer property NPS(k) of the DPC‐CT differs drastically from that of the conventional CT, which results in a dramatic difference in NEQ(k) between the DPC‐CT and conventional CT. Conclusions: Based on an in‐depth investigation, the potential imaging performance of the grating‐based DPC‐CT in comparison to the conventional attenuation‐based CT can be fully understood and appreciated, providing insightful guidelines on the design and performance optimization of DPC‐CT as a new imaging technology. This work is partially supported by the US National Institute of Health through grants P50‐AG025688 and 2P50AG025688.
TH‐A‐213CD‐07: Feasibility of Direct L‐Shell Fluorescence Imaging of Gold Nanoparticles Using a Benchtop X‐Ray Source
Authors: Manohar N , Reynoso F , Cho S
Published: June 2012

Purpose: To test the feasibility of benchtop direct fluorescence imaging of small objects loaded with low concentration of gold nanoparticles (GNPs) using gold L‐shell fluorescence x‐rays for pre‐clinical in‐vivo imaging. Methods: 1‐cm diameter cylindrical plastic tubes containing saline solutions and GNPs at 2%, 0%, and 0.5% (by weight), were placed in a vertically stacked geometry and irradiated using a pencil‐beam of 40 kVp x‐rays. A Si‐PIN detector, placed at 90° with respect to the beam axis such that it had a view along the longitudinal axes of the tubes, captured the spectrum of scattered and gold L‐shell fluorescence photons from the cylinders. Keeping the detector and source positions fixed, the distance from the source to the point of interrogation was kept constant by translation of the stack of tubes vertically and horizontally in steps of 2.5 mm until all tubes were imaged. The resulting gold fluorescence signals were extracted and used to generate a rudimentary 2‐D direct fluorescence image of the tubes. Results: Gold La and Lß fluorescence signals (9.7 and 11.4 keV) were detected with very high signal‐to‐background ratios (up to a factor of 3). The direct 2‐D fluorescence image reconstructed from the experimental geometry accurately showed the positions of each tube and relative GNP concentrations. The relative signal strength from each tube had a linear correlation with GNP concentration. The current results were extrapolated to a GNP detection limit on the order of parts‐per‐million. Conclusions: 2‐D fluorescence imaging of GNP location and concentration within small objects can be accomplished by detection of gold L‐shell fluorescence x‐rays. The current benchtop experimental setup can be easily adapted for in‐/ex‐vivo imaging. After further optimization, this setup can be used to image intra‐tumoral distribution of GNPs in vivo during pre‐clinical animal studies. Supported by NIH/NCI grant 1R01CA155446. Supported by NIH/NCI grant 1R01CA155446
TH‐A‐213CD‐10: A Preliminary Evaluation of An Industrial X‐Ray Backscatter System for Medical Applications
Authors: Doxsee K , Bova F , Dugan E , Shedlock D
Published: June 2012

Purpose: To evaluate the advantages of Compton backscatter radiography system as a medical imaging tool by comparing images obtained to traditional transmission radiography methods. Methods: Five phantoms were scanned with a scatter x‐ray imaging (SXI) system and state of the art transmission imaging systems. Four phantoms contained biological tissue equivalent samples used to exemplify objects found in medical imaging scenarios. For a complex biological setting a euthanized 60 lb pig was scanned. Parameters such as the x‐ray tube voltage, x‐ray tube current, pixel integration time, stand‐off distance, detector type and detector collimation were varied. Images acquired with the scatter and transmission modalities were compared by observing the differences between the mean pixel value and standard deviation of regions within the images. Visual comparisons between images were also used. Results: The contrast differences between SXI and transmission images varied based on phantom composition and placement of objects within them. As a result, neither system showed better overall object contrast across all phantom scans. SXI images of bone at 1.6 cm depth and 7.8 cm depth revealed a 30.3% change in contrast while transmission images showed a 0.1% difference in contrast. Standard deviation of the mean pixel values (noise) in the SXI images ranged from 13.7% – 21.1% whereas the standard deviation for the transmission images ranged from 3.1% to 6.6%. Detector collimation prevented the detection of surface scatter and better represented objects deeper within a phantom. Conclusions: Clinical applicability of an industrial SXI system for medical applications was demonstrated. Exposures with the SXI system required access from only one side of a patient, giving it a smaller form factor and eliminating the spatial constraints imposed by traditional transmission x‐ray imaging systems. Additionally, depth information was observed in SXI images, potentially reducing the number views necessary to properly diagnose patients.
TH‐A‐213CD‐11: Backscatter Imaging: Obtaining Images at Multiple Depths From a Single View
Authors: Juneja B , Bova F , Dugan E , Shedlock D
Published: June 2012

Purpose: To demonstrate that by changing cut‐off planes of a Scatter X‐ray Imaging (SXI) system and that through successive image subtraction, the depth and density information about an object can be derived from a single view. Methods: Phantoms of various thickness and complexity were simulated in a Monte Carlo based transport code MCNPx. An analytic mathematical model of the system was created in MATLAB to verify the initial MCNP results. When necessary, MCNP ptrac files were extracted using a code written in MATLAB for details of all photon interactions. To verify the proposed method, an air cavity and a piece of bone were simulated in a water phantom. Multiple simulations were performed by varying the collimation length and depth of the objects inside water. Initially, only the central axis signal from the phantom was considered, and later a series of backscatter radiographs were obtained using MCNP to validate the method. Results: It was shown that introducing bone and air into a phantom reduced the detector signal. Collimating to the surface plane eliminated a plethora of surface scatter. The near surface slices contributed the most scatter signal, which attenuated exponentially deeper in the phantom. Image subtraction of multiple collimated cut‐off planes showed an expected 60% and 100% drop in signal when bone and air, respectively, were present. However, in successive images, the water signal was misestimated due to attenuation by the object above. This was reduced, in both cases to less than 5%, by applying attenuation corrections for these images. Conclusions: Monte Carlo simulations verify the possibility of obtaining depth and density information of objects in a phantom from a single view using SXI systems if necessary attenuation corrections were applied. There is a need for a sophisticated iterative algorithm that assess these attenuation corrections for each slice.
TH‐A‐217BCD‐03: Spectral Distortion Correction for Photon‐Counting X‐Ray Detectors
Authors: Ding H , Molloi S
Published: June 2012

Purpose: To investigate the feasibility of using an image‐based method to correct for spectral distortions from photon‐counting detectors for their application in breast computed tomography (CT). Methods: The polyenergetic incident spectrum was simulated with the tungsten anode spectral model. Experiments were performed on a Cadmium‐Zinc‐Telluride (CZT) photon‐counting detector with five energy thresholds. BR12 phantoms of various thicknesses were used for calibration. A non‐linear function was selected to fit the count correlation between the simulated and the measured spectra in the calibration process. To evaluate the proposed spectral distortion correction method, both the corrected counts and the effective attenuation coefficients were compared to the simulated values for polymethyl methacrylate (PMMA) phantoms of 8.7 mm, 48.8 mm and 100.0 mm. The feasibility of applying the proposed method to quantitative material decomposition was tested using a dual‐energy imaging technique with a three‐material phantom that consisted of water, lipid and protein. Results: The implementation of the proposed method reduced the relative RMS error of the output counts in the five energy bins with respect to the simulated incident counts from 23.0%, 33.0% and 54.0% to 1.2%, 1.8% and 7.7% for 8.7 mm, 48.8 mm and 100.0 mm PMMA phantoms, respectively. The accuracy of the effective attenuation coefficient of PMMA estimate was also improved with the proposed spectral distortion correction. Finally, the relative RMS error of water, lipid and protein decompositions in dual‐ energy imaging was significantly reduced from 53.4% to 6.8% after correction was applied. Conclusions: The study demonstrated that the proposed method can effectively reduce the spectral distortions caused by various artifacts, including pulse pileup and charge sharing effects. It may be used as a generalized procedure for the spectrum distortion correction of different photon‐counting detectors in clinical breast CT systems.
TH‐A‐217BCD‐05: Material Decomposition in Spectral Computed Tomography (CT) Using a Likelihood‐Based Method
Authors: Berenji B , Klopfer M , Molloi S
Published: June 2012

Purpose: To investigate the feasibility of accurate quantification of concentrations of mixtures of K‐edge contrast agents in spectral Computed Tomography (CT) imaging based on a novel maximum likelihood based method. Methods: Calcium, gold, and iodine solutions of various concentrations were prepared in vials and placed in phantoms for imaging at 100 kVp with a Cadmium‐Zinc‐Telluride (CZT) photon counting detector. The linear attenuation maps were reconstructed in 5 energy bins, whose boundaries were 22, 34, 60, 80, and 100 keV. A novel maximum likelihood‐based algorithm for material decomposition was developed. Summing over the energy bins, on a pixel‐by‐pixel basis, this procedure takes into account the reconstructed linear attenuation coefficient, its associated noise, and the tabulated mass attenuation coefficient of each material of interest, to determine a cost function. The unknown parameters to be fitted by optimization by the MINUIT optimizer (from CERN) of the cost function are the concentrations of the materials of interest at each pixel. The result is a density map image for upper limits on the concentration of each material. Results: Multiple material decomposition of the spectral CT phantoms according to this method yields reliable estimates of concentrations. The upper limits on concentrations are linearly correlated with known concentrations. Conclusions: This study indicates that the problem of decomposition of multiple materials of interest may be facilitated by optimization in the image domain, based on linear attenuation values, and using log‐likelihood and upper limit techniques. This technique can be used for analysis of the spectral CT data.
TH‐A‐217BCD‐09: Volumetric Breast Density Determination Based On X‐Ray Phase‐Shifts
Authors: Wu X , Yan A , Liu H
Published: June 2012

Purpose: High breast density is one of the biggest risk factors for breast cancer. Identifying patient having persistent high breast density is important for breast cancer screening and prevention. In this work we propose for the first time an x‐ray phase‐shift based method for measuring the volumetric breast density. Methods: When x‐ray traverses the breast, x‐ray not only gets its intensity attenuated, but also gets its phase shifted. Studying the x‐ray phase‐shifts generated by the breast tissues, we derived a formula for determining the volumetric breast density from the breast phase map. We show that the volumetric breast density can be reconstructed by retrieving the breast phase map from just a single phase‐sensitive projection of the breast. To validate this phase‐shifts method for measuring the volumetric breast density, we performed computer simulations with an anthropomorphic digital breast phantom. In the simulation we simply set the ray path‐lengths inside a breast to its compressed thickness as a practical approximation. Results: Using this phase‐shifts based method, we reconstructed the phantom's volumetric breast density that is differs from the true breast density of the phantom by only 0.22%, achieving a good accuracy for the breast density determination. With the existence of projection noise, the reconstructed volumetric breast density differs from the true breast density of the phantom by only 1.42% or less as long as the breast signal‐noise ratios being larger than 34. Conclusions: We proposed an x‐ray phase‐shift based method for measuring the volumetric breast density. The simulation results validated this novel method. This research was supported in part by the DoD Breast Cancer Research Program grant W81XWH‐08‐1‐0613 and the NIH grant R01CA142587.
TH‐B‐211‐01: Cone Beam CT: Dose Measurement, Calculation, and Inclusion in the Treatment Plan
Authors: Alaei P , Ding G
Published: June 2012

Measurement and calculation of the dose from imaging beams have gained interest following development of cone beam CT (CBCT) systems. Much work has been done in the use of various dosimeters to measure, as well as methods and algorithms to calculate the imaging dose both in phantom and in patient. Depending on beam quality, measuring imaging dose produces its own challenges. There have also been proposals on the methodology and quantities suitable to describe the dose from CBCT as well as those suitable to quantify the dose to patient. This lecture will review the various measurement and calculation methods as well as quantities used to describe the dose from CBCT systems. It will also review methods being developed to include imaging dose in the patient treatment plans.

Learning Objectives:

1. Understand the dosimeteric tools and methods used to measure dose from CBCT imaging

2. Understand the methods used to calculate dose from CBCT imaging

3. Understand the methodology used to describe the dose from CBCT imaging

4. Understand the methods used to generate beam data from imaging systems for commissioning imaging beams in the treatment planning systems

5. Update on the progress made on inclusion of the CBCT imaging dose in patient treatment plans using existing commercial planning systems as well as development of new algorithms.
TH‐B‐211‐02: Patient Organ Doses From Imaging in IGRT: KV Radiograph Vs. KV‐ CBCT Vs. MV Portal Image Vs. MV‐CBCT
Authors: Ding G , Alaei P
Published: June 2012

Image‐guided radiation therapy (IGRT) plays an increasingly essential role in the accurate delivery of highly conformal dose to the target as it makes it possible to see the target and organs at risk before treatment while the patient is in the treatment position. X‐ray imaging devices, especially kilovoltage x‐ray imaging devices integrated in a radiation treatment delivery unit, are increasingly available in the clinics. The conventional electronic portal imaging devices (EPID) using megavoltage photon beams are still available and being used as well. X‐ray imaging procedures deliver additional radiation dose to patients and this dose may entail risk to patients. Knowledge of radiation dose resulting from x‐ray imaging and the techniques to reduce the imaging dose are important for clinicians in making informed decisions for treatment management regarding the benefit and risk to patients. This lecture will provide an overview of radiation doses to patients resulting from different imaging procedures and an update on the progress of AAPM TG‐180 report.

Learning Objectives:

1. Understand the different available image guidance modalities and devices used in IGRT;

2. Understand the magnitude of the organ dose resulting from difference devices and acquisition procedures;

3. Understand the variation of patient imaging dose distributions among different imaging procedures;

4. Understand how patient dose distributions from an image procedure are calculated

5. Understand the increased magnitude of the dose to bone as compared to soft tissue for kilovoltage x‐rays;

6. Learn the techniques to reduce the imaging dose to patients and sensitive organs;

7. Update on the progress of AAPM TG‐180 report: “Modeling and Accounting for the Imaging Guidance Radiation Doses to Radiotherapy Patients in Treatment Planning”
TH‐C‐213AB‐03: Bayesian Network Framework for Biophysical Radiation Pneumonitis Modeling
Authors: Lee S , Bradley J , Ybarra N , Jeyaseelan K , Seuntjens J , Naqa I El
Published: June 2012

Purpose: To build a biophysical risk model of radiation pneumonitis (RP) using a Bayesian Network (BN) framework for combining biomarker information with dosimetric parameters. Methods: 22 non‐small‐cell lung cancer patients who received radiotherapy (RT) between 2006 to 2009 were selected. Blood samples were collected from each patient before and during RT. From each sample the concentration of the following four candidate biomarkers were measured: alpha‐2‐macroglobulin (a2M), angiotensin converting enzyme (ACE), transforming growth factor ß(TGF‐ß), and interleukin‐6 (IL‐6). The biomarker information was reduced to a subset of variables whose linear combination showed the highest correlation with RP via logistic regression analysis. The selected biomarker variables were combined with two dosimetric known RP parameters (mean lung dose, tumor position in superior‐inferior direction) as features to learn a BN classifier for predicting RP onset. Predictive power of the learned BN classifier was compared with a logistic regression and Naïve Bayes classifiers in a simulated validation dataset. Results: The following 5 biomarker variables were selected for BN learning: 1) pre‐RT concentration level of a2M, 2) ratio of pre‐ to intra‐RT levels of a2M, 3) intra‐RT IL‐6 level, 4) ratio of pre‐ to intra‐RT levels of TGF‐β, and 5) pre‐RT ACE level. The learned BN structure identified the probabilistic relations amongst the 7 features and RP where the role of a2M as a mediator of biological interaction was noticed. Performance of the BN classifier was 0.873, 0.959, and 0.676 for overall classification accuracy, positive and negative predictive power, respectively. It outperformed a logistic regression and a Naïve Bayes counterpart in terms of overall accuracy and positive predictive value. Conclusions: The presented BN approach has a potential to enhance the prediction power of RP by explicitly modeling interactions between physical and biological variables, which can also be used to validate or guide further biomarker research of RP.
TH‐C‐213AB‐04: Kinetics of Repair Deduced From Multi‐Fractioned Irradiation Regimen in Mouse Lung Application of the GLQ Model
Authors: Huang Z , Mayr N , Lo S , Sattler J , Liu T , Zhang D , Jia G , Lu L , Zhang H , Yuh W
Published: June 2012

Purpose: Repair kinetics of radiation damage in mouse lung remains controversial. Using the generalized LQ (gLQ) model that was developed to address the dilemma of the LQ model in high dose range we investigate repair kinetics of mouse lung to radiation. Methods: In an animal study (Vegesna et al. Radiother Oncol 1989; 15:115), mouse lungs were irradiated with repeated dose from 1.6 Gy to 14.3 Gy. The endpoint was death due to radiation pneumonitis. A repair half‐time T1/2=1.5 hour derived from the data of reciprocal of LD50 (lethal dose for 50% mortality) vs. dose‐per‐fraction was much longer than the repair half‐time of 0.4 hour published in the literature. In this study, the same dataset was reanalyzed by using both the LQ and the gLQ models. The least χ2 method was adopted to fit the data and to evaluate the merit for the two models. Results: The combined repair half‐times of 0.4 and 4 hour were used in the gLQ and LQ models, the gLQ model fit the data better than the LQ model: the reduced χ2 is 0.46 vs. 3.8, and α/ß is 2.4 vs. 2.6, respectively. However, when only the fast mode of repair time (0.4 hour) was used, the LQ model couldn't produce the downward curvature based on the low dose data while the gLQ predicted well the high dose data. The gLQ model fit the data much better than the LQ model: the reduced khgr;2 is 0.16 vs. 2.3, and α/ß is 2.4 vs. 3.0, respectively, yielding the consistent α/ß ratio. Conclusions: The gLQ model provides a consistent interpretation of the mouse lung data across fraction sizes up to 14 Gy. Therefore, the gLQ model is able to extend our clinical experience accumulated from conventional low‐dose fractionation to high dose irradiation schedules, including SRS/SBRT and HDR brachytherapy.
TH‐C‐213AB‐06: Risk of Radiation‐Induced Cardiotoxicity and Secondary Cancers in Hodgkin's Lymphoma Patients
Authors: Toltz A , Shin N , Laude C , Roberge D , Freeman C , Seuntjens J , Parker W
Published: June 2012

Purpose: To evaluate the potential reduction of risk of late radiation‐ induced effects for young patients with Hodgkin's lymphoma treated with intensity modulated proton therapy (IMPT) as compared to 3D conformal involved‐field photon radiotherapy. The late effects considered were cardiac mortality and secondary cancer in the lungs and breasts (for female patients). Methods: Patient data was acquired for eleven patients who were under thirty years of age and were treated with radiotherapy for Hodgkin's lymphoma in Quebec in 2010. The original CT simulation images were used to re‐plan the patients with IMPT using commercially‐available treatment planning software. The contours of the critical organs of interest were reviewed for each patient by a physician and modified when necessary for consistency. The dose‐volume matrices of the original photon plans and the new proton plans were analyzed using radiobiological models to assess the risk of late effects. The relative seriality model was utilized to predict excess risk of fatal cardiotoxicity. The organ equivalent dose concept was utilized in conjunction with a modified linear quadratic model to predict the excess absolute risk for induction of lung cancer and breast cancer. Parameters for each model were derived from retrospective studies in the literature. Results: Mean excess risk of fatal cardiotoxicity was 0.9% for photon plans and 0.6% for proton plans. Mean excess absolute risk of developing lung cancer thirty years post‐treatment was 11.9/10,000 patients per year (PY) for photons and 6.9/10,000 PY for protons. Mean excess absolute risk of developing breast cancer thirty years post‐treatment was 6.0/10,000 PY for photons and 2.1/10,000 PY for protons. Conclusions: IMPT has the potential to reduce the risk of fatal, radiation‐induced late cardiotoxicity as well as the risk of radiation‐induced secondary cancers in the lungs and breasts of young patients receiving radiotherapy for Hodgkin's lymphoma. This work has been funded in part by a grant from the Fonds de recherche SantÃ© Quebec (FRSQ).
TH‐C‐213AB‐08: The Cell‐Kill‐Based Equivalent Uniform Dose (cEUD) Predictor: A Modification to Account for Increasing Radioresistance with Increasing Tumor Volume
Authors: Zuniga A , Thorstad W , Bradley J , Deasy J
Published: June 2012

Purpose: The cell‐kill‐based uniform dose (cEUD) formula accounts for varying tumor volume and dose variability in the tumor, has been shown to be highly correlated with local control (LC) in head and neck (H&N) and non‐small‐cell lung (NSCLC) cancer datasets. However, previous fits resulted in high fitted surviving fractions at 2 Gy (SF2 ∼0.8), which is not radiobiologically credible. The purpose of this work is to apply a modification to the cEUD equation to more realistically model the tumor volume effect, while obtaining radiobiologically meaningful estimates of SF2. Methods: We propose a modification of Niemierko's formula, such that SF2 increases linearly with tumor volume, normalized to an arbitrary reference tumor size: SF2(1+ k(VT/Vref)). The resulting proportionality constant was fitted using outcome data. We modeled two different datasets collected at Washington University in Saint Louis: (A) 56 NSCLC patients who received 3D conformal radiotherapy with a median prescription dose of 70 Gy (60‐84 Gy) and a median follow‐up of 32 months; (B) 80 H&N squamous cell carcinoma patients who received definitive IMRT, with a median prescription dose of 70 Gy (66–72 Gy) and a median follow‐up of 19 months. We tested correlation with LC using the area under the receiver operating characteristic curve (AUC). Results: Using a proportionality constant of k=0.05, and Vref=10 cc, we obtained high correlations with outcome for both datasets (AUC_lung= 0.729; AUC H&N= 0.758), while keeping SF2 at a meaningful value (<=0.5). However, AUC values did not significantly increase compared to the simpler model. Conclusions: Introducing this modification into SF2 to account for increasing radioresistance with increasing tumor volume, led to comparable correlations of cEUD with LC, while allowing for a more reasonable range of SF2 values. This modified model is expected to make more realistic predictions concerning the effects of cold‐spots or hot‐spots than the unmodified model. Partially supported by NIH R01 grant CA85181