bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐T‐536: LINAC‐Based Single Isocenter Frameless SRT for Brain Metastases
Authors: Liu B , Zhang L , Rigor N , Kim J 
Published: June 2015

Purpose:Single‐isocenter Stereotactic Radiotherapy of multiple brain metastases with Varian 21 IX LINAC, using Aktina Pinpoint system for patient setup.Methods:In 2014, five single‐isocenter RapidArc SRT plans were delivered to five patients with 2 to 8 brain metastases using Varian 21 IX. Aktina Pinpoint system was used for setup and 2mm PTV margin were used. CBCT was acquired before and after the beam delivery. The prescription is 2100 cGy in 3 fractions. Eclipse planning system was used for treatment planning. Depending on the number of metastases and their locations, 1 to 5 coplanar or non coplanar arcs were used. Typically, 2 or 3 arcs are used. IMRT QAs were performed by comparing an A1SL ion chamber point dose measurement in solid water phantom to point dose of the plan; also, based on EPID measurement, 3D spatial dose was calculated using DosimetryCheck software package from MathResolutions Inc. The EPID system has an active area of 40cm by 30cm with 1024 by 768 photodiodes, which corresponds to a resolution of 0.4mm by 0.4mm pixel dimension.Results:for all the plans, at least 95% PTV coverage was achieved for full prescription dose, with plan normalization > 75%. RTOG conformity indices are less than 1.1 and Paddick gradient indices are less than 4.5. The distance from prescription IDL to 50% IDL increases as the number of metastases increases, and it ranges from 0.6mm to 0.8mm. Treatment time varies from 10mins to 30mins, depending on the number of arcs and if the arcs are coplanar. IMRT QA shows that the ion chamber measurement agree with the eclipse calculation within 3%, and 95% of the points passed Gamma, using 3% dose difference and 3mm DTAConclusion:High quality single isocenter RapidArc SRT plan can be optimized and accurately delivered using Eclipse and Varian 21IX.
SU‐E‐T‐540: MCNPX Simulation of Proton Dose Distributions in a Water Phantom
Authors: Lee C , Lee Y , Chen S , Chiang B , Tung C , Chao T 
Published: June 2015

Purpose:In this study, fluence and energy deposition of proton and proton by‐products and dose distributions were simulated. Lateral dose distributions were also been discussed to understand the difference between Monte Carlo simulations and pencil beam algorithm.Methods:MCNPX codes were used to build a water phantom by using “repeated structures” technique and the doses and fluences in each cell was recorded by mesh tally. This study includes, proton equilibrium and proton disequilibrium case. For the proton equilibrium case, the doses difference between proton and proton by‐products were studied. A 160 MeV proton pencil beam was perpendicularly incident into a 40 × 40 × 50 cm3 water phantom and the scoring volume was 20 × 20 × 0.2 cm3. Energy deposition and fluence were calculated from MCNPX with (1) proton only; and (2) proton and secondary particles. For the proton disequilibrium case, the dose distribution variation using different multiple Coulomb scattering were studied. A 70 MeV proton pencil beam was perpendicularly incident into a 40 × 40 × 10 cm3 water phantom and two scoring voxel sizes of 0.1 × 0.1 × 0.05 cm3 and 0.01 × 0.01 × 0.05 cm3 were used for the depth dose distribution, and 0.01 × 0.01 × 0.05 cm3 for the lateral profile distribution simulations.Results:In the water phantom, proton fluence and dose in depths beyond the Bragg peak were slightly perturbed by the choice of the simulated particle types. The dose from secondary particles was about three orders smaller, but its simulation consumed significant computing time. The depth dose distributions and lateral dose distributions of 70 MeV proton pencil beam obtained from MCNPX, GEANT4, and the pencil beam algorithm showed the significant deviations, probably caused by multiple Coulomb scattering.Conclusion:Multiple Coulomb scattering is critical when there is in proton disequilibrium.
SU‐E‐T‐541: Measurement of CT Density Model Variations and the Impact On the Accuracy of Monte Carlo (MC) Dose Calculation in Stereotactic Body Radiation Therapy for Lung Cancer
Authors: Xiang H , Li B , Russo G , Behrman R , Lu H , Fernando H , Kachnic L 
Published: June 2015

Purpose:To measure the CT density model variations between different CT scanners used for treatment planning and impact on the accuracy of MC dose calculation in lung SBRT.Methods:A Gammex electron density phantom (RMI 465) was scanned on two 64‐slice CT scanners (GE LightSpeed VCT64) and a 16‐slice CT (Philips Brilliance Big Bore CT). All three scanners had been used to acquire CT for CyberKnife lung SBRT treatment planning. To minimize the influences of beam hardening and scatter for improving reproducibility, three scans were acquired with the phantom rotated 120° between scans. The mean CT HU of each density insert, averaged over the three scans, was used to build the CT density models. For 14 patient plans, repeat MC dose calculations were performed by using the scanner‐specific CT density models and compared to a baseline CT density model in the base plans. All dose re‐calculations were done using the same plan beam configurations and MUs. Comparisons of dosimetric parameters included PTV volume covered by prescription dose, mean PTV dose, V5 and V20 for lungs, and the maximum dose to the closest critical organ.Results:Up to 50.7 HU variations in CT density models were observed over the baseline CT density model. For 14 patient plans examined, maximum differences in MC dose re‐calculations were less than 2% in 71.4% of the cases, less than 5% in 85.7% of the cases, and 5–10% for 14.3% of the cases. As all the base plans well exceeded the clinical objectives of target coverage and OAR sparing, none of the observed differences led to clinically significant concerns.Conclusion:Marked variations of CT density models were observed for three different CT scanners. Though the differences can cause up to 5–10% differences in MC dose calculations, it was found that they caused no clinically significant concerns.
SU‐E‐T‐640: Proton Modulated Arc Therapy Using Scanned Pencil Beams
Authors: Carabe‐Fernandez A , Kirk M , Sanchez‐Parcerisa D , Fager M , Burgdorf B , Stowe M , Solberg T 
Published: June 2015

Purpose:Rotational proton radiotherapy would be an interesting treatment modality if it proves to produce dose distributions that conform to the target as well or better than currently available treatment modalities, while reducing the dose to the surrounding organs at risk. A treatment planning study is presented, showing how this objective can be achieved using a single or small number of scanned mono‐energetic pencil beams delivered while the proton gantry rotates at the same time that they may deliver faster and more biologically effective treatments than current proton modalities.Methods Proton treatment using single‐ (SFO) and multi‐field optimized (MFO) PBS plans are compared with PMAT plans in phantoms of different shape and uniformity as well as on a brain case. The method followed in ECLIPSE to produce PMAT plans is presented and the feasibility is discussed. Results Table 1 shows that the conformity and uniformity of the PMAT plans is of similar order than those of the SFO and MFO plans. However, in the brain case, the DVHs shown in Figure 1 indicate a significant reduction of the dose to the OARs when PMAT is used. Similarly, the use of PMAT increases the LET in the treatment area, which could translate into a more biologically effective treatment.Conclusions PMAT could provide dose distributions as conformal as current PBS plans but could also offer faster beam delivery and more biologically effective proton plans.
SU‐F‐BRD‐13: A Phenomenological Relative Biological Effectiveness (RBE) Model for Proton Therapy Based On All Published in Vitro Cell Survival Data
Authors: McNamara A , Schuemann J , Paganetti H 
Published: June 2015

Purpose:Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies. A recent review study (Paganetti 2014), collected over 72 experimental reports on proton RBE, providing a comprehensive dataset for predicting proton RBE. Using this dataset we develop a model to predict RBE based on dose, linear energy transfer (LET) and the tissue specific parameter (α/β)x.Methods:The relationship of the RBE on dose, dose average LET (LETd) and (α/β)x was explored using 290 experimental data points. The RBE was calculated for each experimental point for doses ranging from 1 to 10 Gy. An RBE model, based on the linear‐quadratic model, was derived using a nonlinear regression fit to the experimental data.Results:The proposed RBE model predicts that the RBE increases with LET and decreases with (α/β)x. The model additionally predicts a decrease in RBE with increasing dose for low (α/β)x values (< 3 Gy). However the model predicts an increase in RBE with increasing dose for large (α/β)x (> 4 Gy). Previous phenomenological models were based on a small subset of experimental data and deviate significantly from our findings.Conclusion:The proposed RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. The model agrees with previous theoretical predictions on the relationship between RBE, LETd and (α/β)x and also makes predictions on the relationship between RBE and dose. The proposed model shows a relationship between α and LETd but no significant relationship between β and LETd. Previously published phenomenological models based on a limited data set might have to be revised.
MO‐F‐CAMPUS‐J‐05: Toward MRI‐Only Radiotherapy: Novel Tissue Segmentation and Pseudo‐CT Generation Techniques Based On T1 MRI Sequences
Authors: Aouadi S , McGarry M , Hammoud R , Torfeh T , Perkins G , Al‐Hammadi N 
Published: June 2015

Purpose:To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft‐tissue) on T1 ‐weighted brain MRI and to create a pseudo‐CT for MRI‐only radiation therapy verification.Methods:Contrast‐enhanced T1‐weighted fast‐spin‐echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI‐Simulator, are used.MRIs are firstly pre‐processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft‐tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bone and soft‐tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan‐line filling algorithm. The air mask is extracted by morphological opening followed by a post‐processing based on knowledge about air regions geometry. The remaining rough bone pre‐segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo‐CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft‐tissue MR intensities in [‐400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU].Results:Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft‐tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft‐tissue (0.91) but low for air regions (0.48). Pseudo‐CT produces DRRs with acceptable clinical visual agreement to CT‐based DRR.Conclusion:The proposed approach makes it possible to use T1‐weighted MRI to generate accurate pseudo‐CT from 4‐class segmentation.
MO‐F‐CAMPUS‐I‐04: Characterization of Fan Beam Coded Aperture Coherent Scatter Spectral Imaging Methods for Differentiation of Normal and Neoplastic Breast Structures
Authors: Morris R , Albanese K , Lakshmanan M , Greenberg J , Kapadia A 
Published: June 2015

Purpose:This study intends to characterize the spectral and spatial resolution limits of various fan beam geometries for differentiation of normal and neoplastic breast structures via coded aperture coherent scatter spectral imaging techniques. In previous studies, pencil beam raster scanning methods using coherent scatter computed tomography and selected volume tomography have yielded excellent results for tumor discrimination. However, these methods don't readily conform to clinical constraints; primarily prolonged scan times and excessive dose to the patient. Here, we refine a fan beam coded aperture coherent scatter imaging system to characterize the tradeoffs between dose, scan time and image quality for breast tumor discrimination.Methods:An X‐ray tube (125kVp, 400mAs) illuminated the sample with collimated fan beams of varying widths (3mm to 25mm). Scatter data was collected via two linear‐array energy‐sensitive detectors oriented parallel and perpendicular to the beam plane. An iterative reconstruction algorithm yields images of the sample's spatial distribution and respective spectral data for each location. To model in‐vivo tumor analysis, surgically resected breast tumor samples were used in conjunction with lard, which has a form factor comparable to adipose (fat).Results:Quantitative analysis with current setup geometry indicated optimal performance for beams up to 10mm wide, with wider beams producing poorer spatial resolution. Scan time for a fixed volume was reduced by a factor of 6 when scanned with a 10mm fan beam compared to a 1.5mm pencil beam.Conclusion:The study demonstrates the utility of fan beam coherent scatter spectral imaging for differentiation of normal and neoplastic breast tissues has successfully reduced dose and scan times whilst sufficiently preserving spectral and spatial resolution. Future work to alter the coded aperture and detector geometries could potentially allow the use of even wider fans, thereby making coded aperture coherent scatter imaging a clinically viable method for breast cancer detection.United States Department of Homeland Security; Duke University Medical Center ‐ Department of Radiology; Carl E Ravin Advanced Imaging Laboratories; Duke University Medical Physics Graduate Program
MO‐F‐CAMPUS‐T‐02: Optimizing Orientations of Hundreds of Intensity‐Modulated Beams to Treat Multiple Brain Targets
Authors: Ma L , Dong P , Keeling V , Hossain S , Ahmad S , Larson D , Sahgal A 
Published: June 2015

Purpose:To investigate a new modulated beam orientation optimization (MBOO) approach maximizing treatment planning quality for the state‐of‐the‐art flattening filter free (FFF) beam that has enabled rapid treatments of multiple brain targets.Methods:MBOO selects and optimizes a large number of intensity‐modulated beams (400 or more) from all accessible beam angles surrounding a patient's skull. The optimization algorithm was implemented on a standalone system that interfaced with the 3D Dicom images and structure sets. A standard published data set that consisted of 1 to 12 metastatic brain tumor combinations was selected for MBOO planning. The planning results from various coplanar and non‐coplanar configurations via MBOO were then compared with the results obtained from a clinical volume modulated arc therapy (VMAT) delivery system (Truebeam RapidArc, Varian Oncology).Results:When planning a few number of targets (n<4), MBOO produced results equivalent to non‐coplanar multi‐arc VMAT planning in terms of target volume coverage and normal tissue sparing. For example, the 12‐Gy and 4‐Gy normal brain volumes for the 3‐target plans differed by less than 1 mL ( 3.0 mLvs 3.8 mL; and 35.2 mL vs 36.3 mL, respectively) for MBOO versus VMAT. However, when planning a larger number of targets (n≥4), MBOO significantly reduced the dose to the normal brain as compared to VMAT, though the target volume coverage was equivalent. For example, the 12‐Gy and 4‐Gy normal brain volumes for the 12‐target plans were 10.8 mL vs. 18.0 mL and 217.9 mL vs. 390.0 mL, respectively for the non‐coplanar MBOO versus the non‐coplanar VMAT treatment plans, yielding a reduction in volume of more than 60% for the case.Conclusion:MBOO is a unique approach for maximizing normal tissue sparing when treating a large number (n≥4) of brain tumors with FFF linear accelerators.Dr Ma and Dr Sahgal are currently on the board of international society of stereotactic radiosurgery. Dr Sahgal has received support for educational presentations from Elekta company
MO‐F‐CAMPUS‐T‐05: Correct Or Not to Correct for Rotational Patient Set‐Up Errors in Stereotactic Radiosurgery
Authors: Briscoe M , Ploquin N , Voroney JP 
Published: June 2015

Purpose:To quantify the effect of patient rotation in stereotactic radiation therapy and establish a threshold where rotational patient set‐up errors have a significant impact on target coverage.Methods:To simulate rotational patient set‐up errors, a Matlab code was created to rotate the patient dose distribution around the treatment isocentre, located centrally in the lesion, while keeping the structure contours in the original locations on the CT and MRI. Rotations of 1°, 3°, and 5° for each of the pitch, roll, and yaw, as well as simultaneous rotations of 1°, 3°, and 5° around all three axes were applied to two types of brain lesions: brain metastasis and acoustic neuroma. In order to analyze multiple tumour shapes, these plans included small spherical (metastasis), elliptical (acoustic neuroma), and large irregular (metastasis) tumour structures. Dose‐volume histograms and planning target volumes were compared between the planned patient positions and those with simulated rotational set‐up errors. The RTOG conformity index for patient rotation was also investigated.Results:Examining the tumour volumes that received 80% of the prescription dose in the planned and rotated patient positions showed decreases in prescription dose coverage of up to 2.3%. Conformity indices for treatments with simulated rotational errors showed decreases of up to 3% compared to the original plan. For irregular lesions, degradation of 1% of the target coverage can be seen for rotations as low as 3°.Conclusions:This data shows that for elliptical or spherical targets, rotational patient set‐up errors less than 3° around any or all axes do not have a significant impact on the dose delivered to the target volume or the conformity index of the plan. However the same rotational errors would have an impact on plans for irregular tumours.
MO‐F‐CAMPUS‐J‐02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning
Authors: Chang X , Yang D 
Published: June 2015

Purpose:To investigate the method to automatically recognize the treatment site in the X‐Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup.Methods:The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down‐sampled (from 1024×768 to 128×96) by using bi‐cubic interpolation algorithm. An appearance‐based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi‐class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross‐validation experiments were performed to evaluate the performance.Results:MATLAB image processing Toolbox and scikit‐learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images.Conclusion:The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance.The work was partially supported by a research grant from Varian Medical System.
MO‐F‐CAMPUS‐T‐03: Verification of Range, SOBP Width, and Output for Passive‐Scattering Proton Beams Using a Liquid Scintillator Detector
Authors: Henry T , Robertson D , Therriault‐Proulx F , Beddar S 
Published: June 2015

Purpose:Liquid scintillators have been shown to provide fast and high‐resolution measurements of radiation beams. However, their linear energy transfer‐dependent response (quenching) limits their use in proton beams. The purpose of this study was to develop a simple and fast method to verify the range, spread‐out Bragg peak (SOBP) width, and output of a passive‐scattering proton beam with a liquid scintillator detector, without the need for quenching correction.Methods:The light signal from a 20×20×20 cm3 liquid scintillator tank was collected with a CCD camera. Reproducible landmarks on the SOBP depth‐light curve were identified which possessed a linear relationship with the beam range and SOBP width. The depth‐light profiles for three beam energies (140, 160 and 180 MeV) with six SOBP widths at each energy were measured with the detector. Beam range and SOBP width calibration factors were obtained by comparing the depth‐light curve landmarks with the nominal range and SOBP width for each beam setting. The daily output stability of the liquid scintillator detector was also studied by making eight repeated output measurements in a cobalt‐60 beam over the course of two weeks.Results:The mean difference between the measured and nominal beam ranges was 0.6 mm (σ=0.2 mm), with a maximum difference of 0.9 mm. The mean difference between the measured and nominal SOBP widths was 0.1 mm (σ=1.8 mm), with a maximum difference of 4.0 mm. Finally an output variation of 0.14% was observed for 8 measurements performed over 2 weeks.Conclusion:A method has been developed to determine the range and SOBP width of a passive‐scattering proton beam in a liquid scintillator without the need for quenching correction. In addition to providing rapid and accurate beam range and SOBP measurements, the detector is capable of measuring the output consistency with a high degree of precision.This project was supported in part by award number CA182450 from the National Cancer Institute.