bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐F‐J‐33: On‐Line Daily KV Image Protocol of Lung Patient Setup with Minimal CBCT
Authors: Zhou J , Prado K , Gopal A , D'souza W , Lasio G , Yi B 
Published: July 2016

Purpose:This study's intent is to test if the first 3 fractions of an IGRT protocol consisting of a kV orthogonal pair and subsequent CBCT are representative of all remaining treatment fractions.Methods:At our institution the standard IGRT protocol includes CBCT after kV imaging for the first 3 fractions followed by daily kV and weekly CBCT. We randomly selected 20 lung patients who have at least 8 CBCTs in the entire treatment course (total 187 sets of orthogonal kV images plus CBCT). Our evaluation criterion is the fraction of kV‐vs‐CBCT position mismatches in the first 3 fractions. An additional CBCT is acquired if only 1 fraction mismatches among the first three. We define as 'protocol eligible’ (eGroup) the group of patients for whom the setups with kV images match within the pre‐selected thresholds of 3, 4 and 5 mm those of the consecutive CBCTs for at least 60% of the fractions. A protocol ineligible (iGroup) is defined as the group of patients other than eGroup.Results:The fractions of patients in the eGroup were 70%, 85% and 95%, when the setup thresholds selected were 3, 4 and 5mm respectively. The sensitivities of determining iGroup from the first 3 fractions of kV and CBCT were 0.83, 1.0, and 1.0, and the specificities were 0.93, 0.94 and 0.89 with thresholds 3, 4 and 5 mm respectively.Conclusion:The method suggested here well categorized cases for our kV‐based on‐line IGRT protocol. More patients are eGroup eligible if larger setup threshold is used. CBCT frequency in the on‐line daily kV plus weekly CBCT protocol may need to increase for protocol ineligible patients. Also, our IGRT CBCT frequency could be decreased when position differences between kVs and CBCTs are less than 1.5mm, the average positional standard deviation of out kV‐CBCT sets.
SU‐F‐J‐37: Commissioning a Six Degree of Freedom Couch
Authors: Roberts D , Lam K , Bredfeldt J , Covington E 
Published: July 2016

Purpose:The proper operation of a six degree of freedom couch (6DOFC) to position a patient for treatment requires that a number systems on the treatment unit work together correctly. Commissioning a 6DOFC requires not only the commissioning of each separate system but also commissioning the interactions between the systems.Methods:To commission a 6DOFC, we tested the resolvers that readout the linear and angular positions; the orthogonality of the axes of the laser positioning system, the imaging system, the gantry and the couch as well as their relationship to each other. An automated Winston‐Lutz test was used verify the coincidence of the origins of the laser system, imaging system and treatment unit. The IsoCal phantom was used to verify the consistency of axes orientations between systems. The operation of the couch and imaging system was verified under simulated load conditions and an end to end test was performed to verify correct operation of the system.Results:The tests on the couch resolvers, orthogonality and coincidence of the axes of each system were successfully completed and showed excellent agreement. The automated Winston‐Lutz test showed that the maximum displacement of the center of the radiation field with respect to the isocenter was <0.6 mm (radius) at isocenter. The analysis of the IsoCal phantom showed that the maximum variation of the radiation field with respect to known locations in the phantom was ∼0.9 mm for all the BBs in the phantom. Load tests showed maximum errors of 1 mm.Conclusion:We tested all system associated with the 6DOFC and found the uncertainty due to imaging was <.9mm and the uncertainty due to loading ∼1 mm, resulting in a total error of ∼1.4 mm.
SU‐F‐J‐39: Dose Reduction Strategy Using Attenuation‐Based Tube Current Modulation Method in CBCT for IGRT
Authors: Son K , Lee H , Kim C , Kim J , Cho S 
Published: July 2016

Purpose:To reduce radiation dose to the patients, tube current modulation (TCM) method has been actively used in diagnostic CT systems. However, TCM method has not yet been applied to a kV‐CBCT system on a LINAC machine. The purpose of this study is to investigate whether the use of TCM method is desirable in kV‐CBCT system for IGRT. We have developed an attenuation‐based tube current modulation (a‐TCM) method using the prior knowledge of treatment CT image of a patient.Methods:Patients go through a diagnostic CT scan for RT planning; therefore, using this prior information of CT images, one can estimate the total attenuation of an x‐ray through the patient body in a CBCT setting for radiation therapy. We performed a numerical study incorporating major factors into account such as polychromatic x‐ray, scatter, noise, and bow‐tie filter to demonstrate that a‐TCM method can produce equivalent quality of images at reduced imaging radiation doses. Using the CT projector program, 680 projection images of the pediatric XCAT phantom were obtained both in conventional scanning condition, i.e., without modulating the tube current, and in the proposed a‐TCM scanning condition. FDK reconstruction algorithm was used for image reconstruction, and the organ dose due to imaging radiation has been calculated in both cases and compared using GATE/Geant4 simulation toolkit.Results:Reconstructed CT images in the a‐TCM method showed similar SSIM values and noise properties to the reference images acquired by the conventional CBCT. In addition, reduction of organ doses ranged from 12% to 27%.Conclusion:We have successfully demonstrated the feasibility and dosimetric merit of the a‐TCM method for kV‐CBCT, and envision that it can be a useful option of CBCT scanning that provides patient dose reduction without degrading image quality.
SU‐F‐J‐40: Evaluation of Sensitivity of the Automatic Matching Between Cone‐Beam CT Image and Simulation CT Image in TrueBeam 2.0 Imaging System 6DoF Considering Different Uncertainty Sources
Authors: Bonaque J , Bautista‐Ballesteros J , Ibanez‐Rosello B , Lliso F , Carmona V , Gimeno J , Perez‐Calatayud J 
Published: July 2016

Purpose:To estimate the sensitivity of TrueBeam 2.0 Imaging System 6DoF automatic matching tool through the acquisition of cone‐beam CT images in different phantoms applying submillimeter translations and rotations of tenths of a degree and registered with image simulation CT.Methods:To evaluate overall system‐wide image, we consider two uncertainties source; First, the uncertainty of the manual phantom displacement (ε_m). This uncertainty is calculated by a digital caliper (0.01 mm) for vertical (Vrt), lateral (Lat) and longitudinal (Lng). A digital inclinometer (0.01°) for the pitch and roll and the own phantom scale to evaluate the coordinate rotation (Rtn). The second uncertainty is the displacement detected by the algorithm system of matching (σ_d) that we obtain from the standard deviations of the different measurements. We use three different phantoms. The BrainLab Radiosurgery system for supporting masks with an anthropomorphic dummy adapted to allow displacements of 0.1 mm in Vrt, Lat and Lng dimensions and rotations of 0.1° in Pitch dimension. For the analysis of the Rtn and Roll dimensions we use two homemade phantoms (RinoRot and RinoRoll, La Fe Hospital, Valencia, Spain) that allow rotations of 0.3°.Results:In the case of manual displacement of 0.10 ± 0.03 mm in the translations, the system detect 0.10 ± 0.07 mm, 0.12 ± 0.07 mm and 0.13 ± 0.07 mm (mean ± SD) in Lat, Vrt and Lng respectively. In the case of rotational dimension, manual displacement of 0.3 ± 0.1° was detected with 0.19 ± 0.06°, 0.29 ± 0.03° and 0.27 ± 0.06° in Pitch, Roll and Rtn.Conclusion:We conclude that the sensitivity of the automatic matching system is within 0.10 mm in translations and 0.3° in rotations. These values are under the own sensitivity of the software.
SU‐F‐J‐43: Positional Variation of Implanted Fiducial Markers Over the Course of Image Guided Radiotherapy for Pancreatic Cancer
Authors: Shen S , Jacob R , Popple R , Wu X , Cardan R , Brezovich I 
Published: July 2016

Purpose:Pancreas is a soft‐tissue organ, implanted fiducials can change positions due to migration or tissue deformation. This study quantified positional variation of fiducials in IGRT for pancreatic cancer.Methods:20 patients had at least 3 gold fiducials implanted in pancreas under EUS guidance. Patients had 4D‐CT simulation for gated treatment. Daily gated OBI kV images (Turebeam) were used for positional alignment with fiducials for total of 25 or 28 fractions. Relative distances among 3 fiducials (d1 – 2, d1 – 3, d2 – 3) were measured from 4D‐CT end‐of‐expiration phase bin; and from gated kV images in first, mid, and last fraction (n=180).Results:The median duration between implant and simulation was 11 (range 0–41) days. The median duration between simulation and first fraction was 17 (range 8–24) days. The median relative distance was 12 (range 4–78) mm for d1 – 2, 24 (range 6–80) mm for d1 – 3, and 19 (range 5–63) mm for d2 – 3. The median deviation was 1 mm for d1 – 2, d1 – 3, d2 – 3 between simulation and first fraction, first and mid fraction, mid and last fraction (n=180). Two patients (10%) had deviation >= 5 mm (5, 11 mm) between simulation and first fraction. One patient (5%) had deviation >= 5 mm (11 mm) between first and mid fraction. No patient (0%) had deviation >= 5 mm between mid and last fraction. In all 3 cases with deviation >=5 mm, only one fiducial was significantly deviated. No clear evidence that deviation size was associated with time interval between implant and first fraction.Conclusion:Implanted gold fiducials were quite stable over time in their relative positions in pancreas. Our data suggested at least 3 fiducials are needed. In cases that one fiducial was significantly deviated in daily kV images, this fiducial should be excluded in image guidance.
SU‐F‐J‐60: Impact of DIR Method On Treatment Dose Wrapping
Authors: Qin A , Liang J , Zhu J , Ding X , Castillo E , Guerrero T , Yan D 
Published: July 2016

Purpose:To investigate clinical relevant discrepancy between doses wrapped by pure image and biomechanical model based deformable registration (DIR).Methods:12 patients, each with a CT pair, were included (5 H&N, 5 Prostate and 2 Lung). A research DIR tool (ADMRIE) was utilized for image based DIR (IMG‐DIR). To assure organ matching, contour constrain was applied for prostate patients. Tetrahedron meshes were generated for organs (parotid, bladder, rectum and lung). Deformable vector fields (DVF) from IMG‐DIR were interpolated to the surface node of meshes as boundary condition. Biomechanical models using finite element modeling (FEM) were generated by assigning organ specific material properties. The models were then input into a FEM tool (ABAQUS) to calculate internal deformation (FEM‐DIR). The output volume node displacements were then interpolated to image grids to get refined DVF. The IMRT treatment doses were wrapped by both DVFs to pre‐treatment CTs. DVF vector distance (DVF‐VD) was calculated on each organ. Dose parameters were calculated for wrapped doses and normalized to pretreatment plan. Gamma passing rate (GPR) was calculated with IMG‐DIR dose as reference. Correlation was evaluated between parotid shrinkage and DVF‐VD /dose‐discrepancy.Results:H&N:parotid volume with DVF‐VD (>1.5mm) was 6.5±4.7%. The normalized mean dose difference (NMDD) of IMG‐DIR and FEM‐DIR was −0.8±1.5%, with range (−4.7% to 1.5%). 2mm/2% GPR was 99.0±1.4%. Moderate correlation was found between parotid shrinkage and DVF‐VD (R=0.61)/NMDD (R=0.68). Prostate:bladder had a NMDD of −9.9±9.7%, with FEM‐DIR wrapped dose systematically higher. Only minor deviation was observed for rectum NMDD (0.5±1.1%). 3mm/3% GPR of bladder and rectum were 81.9±12.0% and 93.1±4.3%, respectively. One of lung patients had 3.9%NMDD and 3mm/3%GPR of 95.2% inside lung.Conclusion:Impact of DIR methods on treatment dose wrapping is patient and organ specific. Generally, bigger organ with larger volume variation leads to greater dose wrapping uncertainty.Acknowledgement:Elekta research grant support.This work was supported by research funding from Elekta
SU‐F‐J‐67: Dosimetric Changes During Radiotherapy in Lung Cancer Patients with Atelectasis
Authors: Guy C , Weiss E , Jan N , Reshko L , Christensen G , Hugo G 
Published: July 2016

Purpose:Large geometric changes which occur during thoracic radiotherapy alter normal anatomy and target position and may induce clinically important dose changes. This study investigates variation of organ‐at‐risk (OAR) dose caused by atelectasis resolution during radiotherapy.Methods:3D IMRT treatment plans were obtained for 14 non‐small‐cell lung cancer patients. Dose of the clinical plan was recalculated on a baseline scan in which lung was collapsed and on a midtreatment scan in which lung re‐aeration had occurred. The changes in OAR doses were compared between the two time points. RTOG‐0617 and inhouse dose‐volume constraints were chosen for investigation and included spinal cord, esophagus, heart, and healthy lung.Results:17 dose metrics were evaluated. The mean (SD) of change in mean lung dose, from baseline to mid‐treatment (average taken across all patients), was 0.2 Gy (2.2 Gy) and ranged from −3.2 Gy to 6.0 Gy. 50% of patients experienced relative changes in mean lung dose of greater than 5% of baseline value. The mean (SD) of changes in heart V40, V45, and V60 were 3.2% (3.4%), 3.0% (2.9%), and 1.4% (2.1%), respectively, and were significant for the study cohort (Wilcoxon signed‐rank test, p=0.0107 for V40, p=0.0052 for V45, and p= 0.0353 for V60. Ranges in changes of Heart V40, V45, and V60 were −1.9% to 8.6%, −1.7% to 7.5%, and −2.1% to 4.5%, respectively. The mean (SD) of changes in Esophagus PRV Dmean and V60 were 0.3 Gy (3.3 Gy) and 0.8% (7.7%), respectively, and ranged from −4.8 Gy to 6.8 Gy for Dmean and −15.2% to 14.6% for V60.Conclusion:Patients with atelectasis present at the start of radiotherapy experience significant increases in heart dose. Substantial increases in mean lung dose also occur in a subset of patients.This work supported by the National Cancer Institute of National Institutes of Health under Award Number R01CA166119. Disclosures: Phillips Medical systems (Hugo, Weiss), National Institutes of Health (Hugo, Weiss, Christensen), and Roger Koch (Christensen) support, UpToDate (Weiss) royalties, and Varian Medical Systems (Hugo, Weiss) license. No potential conflicts of interest.
SU‐F‐J‐72: A Clinical Usable Integrated Contouring Quality Evaluation Software for Radiotherapy
Authors: Jiang S , Dolly S , Cai B , Mutic S , Li H 
Published: July 2016

Purpose:To introduce the Auto Contour Evaluation (ACE) software, which is the clinical usable, user friendly, efficient and all‐in‐one toolbox for automatically identify common contouring errors in radiotherapy treatment planning using supervised machine learning techniques.Methods:ACE is developed with C# using Microsoft .Net framework and Windows Presentation Foundation (WPF) for elegant GUI design and smooth GUI transition animations through the integration of graphics engines and high dots per inch (DPI) settings on modern high resolution monitors. The industrial standard software design pattern, Model‐View‐ViewModel (MVVM) pattern, is chosen to be the major architecture of ACE for neat coding structure, deep modularization, easy maintainability and seamless communication with other clinical software. ACE consists of 1) a patient data importing module integrated with clinical patient database server, 2) a 2D DICOM image and RT structure simultaneously displaying module, 3) a 3D RT structure visualization module using Visualization Toolkit or VTK library and 4) a contour evaluation module using supervised pattern recognition algorithms to detect contouring errors and display detection results. ACE relies on supervised learning algorithms to handle all image processing and data processing jobs. Implementations of related algorithms are powered by Accord.Net scientific computing library for better efficiency and effectiveness.Results:ACE can take patient's CT images and RT structures from commercial treatment planning software via direct user input or from patients’ database. All functionalities including 2D and 3D image visualization and RT contours error detection have been demonstrated with real clinical patient cases.Conclusion:ACE implements supervised learning algorithms and combines image processing and graphical visualization modules for RT contours verification. ACE has great potential for automated radiotherapy contouring quality verification. Structured with MVVM pattern, it is highly maintainable and extensible, and support smooth connections with other clinical software tools.
SU‐F‐J‐74: High Z Geometric Integrity and Beam Hardening Artifact Assessment Using a Retrospective Metal Artifact Reduction (MAR) Reconstruction Algorithm
Authors: Woods K , DiCostanzo D , Gupta N 
Published: July 2016

Purpose:To test the efficacy of a retrospective metal artifact reduction (MAR) reconstruction algorithm for a commercial computed tomography (CT) scanner for radiation therapy purposes.Methods:High Z geometric integrity and artifact reduction analysis was performed with three phantoms using General Electric's (GE) Discovery CT. The three phantoms included: a Computerized Imaging Reference Systems (CIRS) electron density phantom (Model 062) with a 6.5 mm diameter titanium rod insert, a custom spine phantom using Synthes Spine hardware submerged in water, and a dental phantom with various high Z fillings submerged in water. Each phantom was reconstructed using MAR and compared against the original scan. Furthermore, each scenario was tested using standard and extended Hounsfield Unit (HU) ranges. High Z geometric integrity was performed using the CIRS phantom, while the artifact reduction was performed using all three phantoms.Results:Geometric integrity of the 6.5 mm diameter rod was slightly overestimated for non‐MAR scans for both standard and extended HU. With MAR reconstruction, the rod was underestimated for both standard and extended HU. For artifact reduction, the mean and standard deviation was compared in a volume of interest (VOI) in the surrounding material (water and water equivalent material, ∼0HU). Overall, the mean value of the VOI was closer to 0 HU for the MAR reconstruction compared to the non‐MAR scan for most phantoms. Additionally, the standard deviations for all phantoms were greatly reduced using MAR reconstruction.Conclusion:GE's MAR reconstruction algorithm improves image quality with the presence of high Z material with minimal degradation of its geometric integrity. High Z delineation can be carried out with proper contouring techniques. The effects of beam hardening artifacts are greatly reduced with MAR reconstruction. Tissue corrections due to these artifacts can be eliminated for simple high Z geometries and greatly reduced for more complex geometries.
SU‐F‐J‐77: Variations in the Displacement Vector Fields Calculated by Different Deformable Image Registration Algorithms Used in Helical, Axial and Cone‐Beam CT Images of a Mobile
Authors: Ali I , Jaskowiak J , Alsbou N , Ahmad S 
Published: July 2016

Purpose:To investigate quantitatively the displacement‐vector‐fields (DVF) obtained from different deformable image registration algorithms (DIR) in helical (HCT), axial (ACT) and cone‐beam CT (CBCT) to register CT images of a mobile phantom and its correlation with motion amplitudes and frequencies.Methods:HCT, ACT and CBCT are used to image a mobile phantom which includes three targets with different sizes that are manufactured from water‐equivalent material and embedded in low density foam. The phantom is moved with controlled motion patterns where a range of motion amplitudes (0–40mm) and frequencies (0.125–0.5Hz) are used. The CT images obtained from scanning of the mobile phantom are registered with the stationary CT‐images using four deformable image registration algorithms including demons, fast‐demons, Horn‐Schunk and Locas‐Kanade from DIRART software.Results:The DVF calculated by the different algorithms correlate well with the motion amplitudes that are applied on the mobile phantom where maximal DVF increase linearly with the motion amplitudes of the mobile phantom in CBCT. Similarly in HCT, DVF increase linearly with motion amplitude, however, its correlation is weaker than CBCT. In ACT, the DVF's do not correlate well with the motion amplitudes where motion induces strong image artifacts and DIR algorithms are not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR‐algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from fast‐demons deviated strongly from other algorithms at large motion amplitudes.Conclusion:In CBCT and HCT, the DVF correlate well with the motion amplitude of the mobile phantom. However, in ACT, DVF do not correlate with motion amplitudes. Correlations of DVF with motion amplitude as in CBCT and HCT imaging techniques can provide information about unknown motion parameters of the mobile organs in real patients as demonstrated in this phantom visibility study.
SU‐F‐J‐79: Extension of Fixed‐Point Iteration Algorithm for Inverse Deformation
Authors: R Anders M , Chen M , Jiang S , Lu W 
Published: July 2016

Purpose:(I) To present a modification of the fixed point iteration algorithm to assure the convergence for a wide range of cases such as rotation and scaling deformations and (II) to provide implementation details with results from simulations and clinical images and emphasize more on practical considerations, such as how to choose good initial values to improve the speed of convergence. Inverse deformations are often applied to images, contours, and dose.Methods:The 2D or 3D inverse deformation field is iteratively found by taking a partial step size parameter (< 1) in a given direction which depends on the forward deformation field as opposed to a full step size parameter (= 1) as in the original fixed‐point inverse deformation paper. The step size parameter controls the size of the change in the inverse deformation field during each iteration. The step size parameter is also dynamically changed to achieve convergence faster.Results:The new method was implemented in Matlab and tested for both 2D simulated cases and 3D clinical cases. We started the main iteration loop with step size parameter = 1 and then dynamically changed it to achieve convergence of maximum voxel error of 0.01. The same method was implemented for deformation fields from deformable registration of three patients to another set of patient data. Convergence of the inverse deformation fields was also achieved for the patient cases.Conclusion:If the step size parameter is small enough, then the improved iterative algorithm achieves convergence for a wider array of cases such as rotations and scaling of the image. The method is demonstrated to be successful for 2D simulated data and 3D patient data sets and allows for the finding of inverse deformation fields for a wider range of cases.Supported by Cancer Prevention & Research Institute of Texas (CPRIT) ‐ ID RP150485