bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐44: A Novel Approach to Quantify Patient Setup and Target Motion for Real‐Time Image‐Guided Radiotherapy (IGRT)
Authors: Li S , Charpentier P , Sayler E , Micaily B , Miyamoto C , Geng J 
Published: June 2015

PurposeIsocenter shifts and rotations to correct patient setup errors and organ motion cannot remedy some shape changes of large targets. We are investigating new methods in quantification of target deformation for realtime IGRT of breast and chest wall cancer.MethodsNinety‐five patients of breast or chest wall cancer were accrued in an IRB‐approved clinical trial of IGRT using 3D surface images acquired at daily setup and beam‐on time via an in‐room camera. Shifts and rotations relating to the planned reference surface were determined using iterative‐closest‐point alignment. Local surface displacements and target deformation are measured via a ray‐surface intersection and principal component analysis (PCA) of external surface, respectively. Isocenter shift, upper‐abdominal displacement, and vectors of the surface projected onto the two principal components, PC1 and PC2, were evaluated for sensitivity and accuracy in detection of target deformation. Setup errors for some deformed targets were estimated by superlatively registering target volume, inner surface, or external surface in weekly CBCT or these outlines on weekly EPI.ResultsSetup difference according to the inner‐surface, external surface, or target volume could be 1.5 cm. Video surface‐guided setup agreed with EPI results to within < 0.5 cm while CBCT results were sometimes (∼20%) different from that of EPI (>0.5 cm) due to target deformation for some large breasts and some chest walls undergoing deep‐breath‐hold irradiation. Square root of PC1 and PC2 is very sensitive to external surface deformation and irregular breathing.ConclusionPCA of external surfaces is quick and simple way to detect target deformation in IGRT of breast and chest wall cancer. Setup corrections based on the target volume, inner surface, and external surface could be significant different. Thus, checking of target shape changes is essential for accurate image‐guided patient setup and motion tracking of large deformable targets.NIH grant for the first author as cionsultant and the last author as the PI
SU‐E‐J‐52: Decreasing Frequency of Performing TG‐142 Imaging QA – 5 Year Experience
Authors: Lin T , Ma C 
Published: June 2015

PurposeThis study is an update to check if the frequency of imaging QA suggested by AAPM Task Group Report 142 (TG142) is necessary with our 5 year experience. TG142 presents recommendations for QA criteria of IGRT treatment. ACR has adopted it to be the requirements for any radiatiotherapy practices; however, we propose to reduce the frequency on image quality QA according to this 5 year study.Method and Materials: This study uses VarianIX2100 and Siemens Artiste Linacs to perform QAs on KV, MV, CBCT modalities. The QA was designed following under the recommendations of TG142. This study reports the daily imaging positioning/repositioning and imaging and treatment coordinate coincidence. QA results on kV, MV and CBCT from 4/7/2010∼3/11/15 are analyzed. KV, MV, CBCT images are taken with the Varian isocube localized at the isocenter. Digital graticule is used in the software to verify the isocenter position. CBCT images are taken with the cube placed at 1cm superior, lateral and anterior of the isocenter. In‐line fusion software is used to verify the contrived shift. Digital ruler provided at the on‐board‐imaging software or adaptive‐targeting software was used to measure the position differences. The position differences were recorded at AP,LR,SI directions.Results5 year records on kV, MV, CBCT show the shifts in all three directions are within the tolerance of 1mm suggested in TG142 for stereotactic radiation treatment(SRS/SRT). There is no occasion where shifts are outside 1mm tolerance.ConclusionsThe daily imaging QA suggested in TG142 is useful in ensuring the accuracy needed for SRS/SRT in IGRT. 5 year measurements presented suggest that decreasing the frequency of imaging QA may be acceptable, in particular for institutions reporting no violation of tolerance over periods of few years.
SU‐E‐J‐53: A Phantom Design to Assist Patient Position Verification System in Daily Image‐Guided RT and Comprehensive QA Measurements
Authors: Syh J , Wu H 
Published: June 2015

PurposeThis study is to implement a homemade novel device with surface locking couch index to check daily radiograph (DR) function of adaPTInsight™, stereoscopic image guided system (SIGS), for proton therapy. The comprehensive daily QA checks of proton pencil beam output, field size, flatness and symmetry of spots and energy layers will be followed by using MatriXX dosimetry device.MethodsThe iBa MatriXX device was used to perform daily dosimetry which is also used to perform SIGS checks. A set of markers were attached to surface of MatriXX device in alignment of DRR of reconstructed CT images and daily DR. The novel device allows MatriXX to be fit into the cradle which was locked by couch index bars on couch surface. This will keep the MatriXX at same XY plane daily with exact coordinates. Couch height Z will be adjusted according to imaging to check isocenter‐laser coincidence accuracy.ResultsadaPTInsight™ provides robotic couch to move in 6‐degree coordinate system to align the dosimetry device to be within 1.0 mm / 1.0°. The daily constancy was tightened to be ± 0.5 mm / 0.3° compared to 1.0 mm / 1.0° before. For gantry at 0° and couch all 0° angles (@ Rt ARM 0 setting), offsets measured of the couch systems were ≤ 0.5° in roll, yaw and pitch dimensions.ConclusionSimplicity of novel device made daily image guided QA consistent with accuracy. The offset of the MatriXX isocenter‐laser coincident was reproducible. Such easy task not only speeds up the setup, but it increases confidence level in detailed daily comprehensive measurements. The total SIGS alignment time has been shortened with less setup error. This device will enhance our experiences for the future QA when cone beam CT imaging modality becomes available at proton therapy center.
SU‐E‐J‐113: Effects of Deformable Registration On First‐Order Texture Maps Calculated From Thoracic Lung CT Scans
Authors: Smith C , Cunliffe A , Al‐Hallaq H , Armato S 
Published: June 2015

Purpose:To determine the stability of eight first‐order texture features following the deformable registration of serial computed tomography (CT) scans.Methods:CT scans at two different time points from 10 patients deemed to have no lung abnormalities by a radiologist were collected. Following lung segmentation using an in‐house program, texture maps were calculated from 32×32‐pixel regions of interest centered at every pixel in the lungs. The texture feature value of the ROI was assigned to the center pixel of the ROI in the corresponding location of the texture map. Pixels in the square ROI not contained within the segmented lung were not included in the calculation. To quantify the agreement between ROI texture features in corresponding pixels of the baseline and follow‐up texture maps, the Fraunhofer MEVIS EMPIRE10 deformable registration algorithm was used to register the baseline and follow‐up scans. Bland‐Altman analysis was used to compare registered scan pairs by computing normalized bias (nBias), defined as the feature value change normalized to the mean feature value, and normalized range of agreement (nRoA), defined as the range spanned by the 95% limits of agreement normalized to the mean feature value.Results:Each patient's scans contained between 6.8–15.4 million ROIs. All of the first‐order features investigated were found to have an nBias value less than 0.04% and an nRoA less than 19%, indicating that the variability introduced by deformable registration was low.Conclusion:The eight first‐order features investigated were found to be registration stable. Changes in CT texture maps could allow for temporal‐spatial evaluation of the evolution of lung abnormalities relating to a variety of diseases on a patient‐by‐patient basis.SGA and HA receives royalties and licensing fees through the University of Chicago for computer‐aided diagnosis technology. Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number R25GM109439.
SU‐E‐J‐116: Uncertainties Associated with Dose Summation of High‐Dose Rate Brachytherapy and Intensity Modulated Radiotherapy for Gynecological Cases
Authors: Kauweloa K , Bergamo A , Gutierrez A , Stathakis S , Mavroidis P , Papanikolaou N , Kirby N 
Published: June 2015

Purpose:Determining the cumulative dose distribution (CDD) for gynecological patients treated with both high‐dose rate (HDR) brachytherapy and intensity‐modulated radiotherapy (IMRT) is challenging. The purpose of this work is to study the uncertainty of performing this with a structure‐guided deformable (SGD) approach in Velocity.Methods:For SGD, the Hounsfield units inside specified contours are overridden to set uniform values. Deformable image registration (DIR) is the run on these process images, which forces the DIR to focus on these contour boundaries. 18 gynecological cancer patients were used in this study. The original bladder and rectum planning contours for these patients were used to drive the SGD. A second set of contours were made of the bladder by the same person with the intent of carefully making them completely consistent with each other. This second set was utilized to evaluate the spatial accuracy of the SGD. The determined spatial accuracy was then multiplied by the local dose gradient to determine a dose uncertainty associated with the SGD dose warping. The normal tissue complication probability (NTCP) was then calculated for each dose volume histogram (DVH) that included four different probabilistic uncertainties associated with the spatial errors (e.g., 68.3% and 95.4%).Results:The NTCPs for each DVH (e.g., NTCP_−95.4%, NTCP_−68.3%, NTCP_68.3%, NTCP_95.4%) differed amongst patients. All patients had an NTCP_−95.4% close to 0%, while NTCP_95.4% ranged from 0.67% to 100%. Nine patients had an NTCP_−95.4% less than 50% while the remaining nine patients had NTCP_95.4% greater than 50%.Conclusion:The uncertainty associated with this CDD technique renders a large NTCP uncertainty. Thus, it is currently not practical for clinical use. The two ways to improve this would be to use more precise contours to drive the SGD and to use a more accurate DIR algorithm.
SU‐E‐J‐124: 18F‐FDG PET Imaging to Improve RT Treatment Outcome for Locally Advanced Lung Cancer
Authors: Shusharina N , Khan F , Sharp G , Choi N 
Published: June 2015

Purpose:To investigate spatial correlation between high uptake regions of pre‐ and 10‐days‐post therapy1 8F‐FDG PET in recurrent lung cancer and to evaluate the feasibility of dose escalation boosting only regions with high FDG uptake identified on baseline PET.Methods:Nineteen patients with stages II– IV inoperable lung cancer were selected. Volumes of interest (VOI) on pre‐therapy FDG‐PET were defined using an isocontour at ≥50% of SUVmax. VOI of pre‐ and post‐therapy PET images were correlated for the extent of overlap. A highly optimized IMRT plan to 60 Gy prescribed to PTV defined on the planning CT was designed using clinical dose constraints for the organs at risk. A boost of 18 Gy was prescribed to the VOI defined on baseline PET. A composite plan of the total 78 Gy was compared with the base 60 Gy plan. Increases in dose to the lungs, spinal cord and heart were evaluated. IMRT boost plan was compared with proton RT and SBRT boost plans.Results:Overlap fraction of baseline PET VOI with the VOI on 10 days‐post therapy PET was 0.8 (95% CI: 0.7 – 0.9). Using baseline VOI as a boosting volume, dose could be escalated to 78 Gy for 15 patients without compromising the dose constraints. For 4 patients, the dose limiting factors were V20Gy and Dmean for the total lung, and Dmax for the spinal cord. An increase of the dose to OARs correlated significantly with the relative size of the boost volume.Conclusion:VOI defined on baseline 18F‐FDG PET by the SUVmax‐≥50% isocontour may be a biological target volume for escalated radiation dose. Dose escalation to this volume may provide improved tumor control without breaching predefined dose constraints for OARs. The best treatment outcome may be achieved with proton RT for large targets and with SBRT for small targets.
SU‐E‐J‐128: Two‐Stage Atlas Selection in Multi‐Atlas‐Based Image Segmentation
Authors: Zhao T , Ruan D 
Published: June 2015

Purpose:In the new era of big data, multi‐atlas‐based image segmentation is challenged by heterogeneous atlas quality and high computation burden from extensive atlas collection, demanding efficient identification of the most relevant atlases. This study aims to develop a two‐stage atlas selection scheme to achieve computational economy with performance guarantee.Methods:We develop a low‐cost fusion set selection scheme by introducing a preliminary selection to trim full atlas collection into an augmented subset, alleviating the need for extensive full‐fledged registrations. More specifically, fusion set selection is performed in two successive steps: preliminary selection and refinement. An augmented subset is first roughly selected from the whole atlas collection with a simple registration scheme and the corresponding preliminary relevance metric; the augmented subset is further refined into the desired fusion set size, using full‐fledged registration and the associated relevance metric. The main novelty of this work is the introduction of an inference model to relate the preliminary and refined relevance metrics, based on which the augmented subset size is rigorously derived to ensure the desired atlases survive the preliminary selection with high probability.Results:The performance and complexity of the proposed two‐stage atlas selection method were assessed using a collection of 30 prostate MR images. It achieved comparable segmentation accuracy as the conventional one‐stage method with full‐fledged registration, but significantly reduced computation time to 1/3 (from 30.82 to 11.04 min per segmentation). Compared with alternative one‐stage cost‐saving approach, the proposed scheme yielded superior performance with mean and medium DSC of (0.83, 0.85) compared to (0.74, 0.78).Conclusion:This work has developed a model‐guided two‐stage atlas selection scheme to achieve significant cost reduction while guaranteeing high segmentation accuracy. The benefit in both complexity and performance is expected to be most pronounced with large‐scale heterogeneous data.
SU‐E‐J‐129: Atlas Development for Cardiac Automatic Contouring Using Multi‐Atlas Segmentation
Authors: Zhou R , Yang J , Pan T , Milgrom S , Pinnix C , Shi A , Yang J , Liu Y , Nguyen Q , Gomez D , Dabaja B , Balter P , Court L , Liao Z 
Published: June 2015

Purpose:To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity.Methods:Six thoracic cancer patients with both contrast and non‐contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non‐contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fused using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in‐house multi‐atlas segmentation. Using leave‐one‐out test, we generated auto‐segmented contours for each ROI and each patient. The auto‐segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD).Results:Inter‐observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto‐segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm.Conclusion:A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification may need for auto‐segmented contours of indistinguishable small structures.
SU‐E‐J‐130: Automating Liver Segmentation Via Combined Global and Local Optimization
Authors: Li Dengwang , Wang Jie , Kapp Daniel S. , Xing Lei 
Published: June 2015

Purpose:The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor.Methods:200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H‐PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate).Results:The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%.Conclusion:The proposed automatic segmentation method provides a sensible technique for segmentation of CT images.This work is supported by NIH/NIBIB (1R01‐EB016777), National Natural Science Foundation of China (No.61471226 and No.61201441), Research funding from Shandong Province (No.BS2012DX038 and No.J12LN23), and Research funding from Jinan City (No.201401221 and No.20120109).
SU‐E‐J‐137: Image Registration Tool for Patient Setup in Korea Heavy Ion Medical Accelerator Center
Authors: Kim M , Cho W , Jung W , Suh T 
Published: June 2015

Purpose:A potential validation tool for compensating patient positioning error was developed using 2D/3D and 3D/3D image registration.Methods:For 2D/3D registration, digitally reconstructed radiography (DRR) and three‐dimensional computed tomography (3D‐CT) images were applied. The ray‐casting algorithm is the most straightforward method for generating DRR. We adopted the traditional ray‐casting method, which finds the intersections of a ray with all objects, voxels of the 3D‐CT volume in the scene. The similarity between the extracted DRR and orthogonal image was measured by using a normalized mutual information method. Two orthogonal images were acquired from a Cyber‐Knife system from the anterior‐posterior (AP) and right lateral (RL) views. The 3D‐CT and two orthogonal images of an anthropomorphic phantom and head and neck cancer patient were used in this study. For 3D/3D registration, planning CT and in‐room CT image were applied. After registration, the translation and rotation factors were calculated to position a couch to be movable in six dimensions.Results:Registration accuracies and average errors of 2.12 mm ± 0.50 mm for transformations and 1.23° ± 0.40° for rotations were acquired by 2D/3D registration using an anthropomorphic Alderson‐Rando phantom. In addition, registration accuracies and average errors of 0.90 mm ± 0.30 mm for transformations and 1.00° ± 0.2° for rotations were acquired using CT image sets.Conclusion:We demonstrated that this validation tool could compensate for patient positioning error. In addition, this research could be the fundamental step for compensating patient positioning error at the first Korea heavy‐ion medical accelerator treatment center.
SU‐E‐J‐142: Prompt Gamma Emission Measurements From a Passively Scattered Proton Beam On Targets Containing 16O, 12C and 14N
Authors: Jeyasugiththan J , Peterson S 
Published: June 2015

Purpose:To measure the prompt gamma emission from the important elements found in tissue (1 6O,1 2C and1 4N) in a clinical passive‐scatter treatment environment.Methods:The targets (composed of water, Perspex, graphite and liquid nitrogen) were irradiated with a 200 MeV passive‐scatter proton beam and the discrete prompt gamma energy spectra was detected by a high resolution 2′ × 2′ LaBr. detector. In order to reduce the high level of radiation produced by the beam line elements, the detector was surrounded by 10 cm of lead to attenuate the scattered gamma‐rays entering the detector with an extra 5 cm thick layer of lead added along the beam direction. A 10 cm thick collimator with a 5 cm × 10 cm rectangular opening was also used.Results:The prompt gamma peaks at 6.13 MeV and 4.44 MeV were clearly identified as a Result of the inelastic nuclear reaction between the protons and the 16O atoms found in the water target. The 6.13 MeV peak was 5% higher than the peak at 4.44 MeV for the water target. The 4.44 MeV peak was the only identified emission in the prompt gamma energy spectra from the graphite target (1 2C). The expected 2.313 MeV peak form the1 4N (liquid nitrogen target) was identified, but the other expected1 4N peaks could not be resolved.Conclusion:Prompt gamma measurements with a passive‐scatter proton beam are possible, but the presence of a high amount of background radiation from the patient final collimator presents a challenge at the treatment isocenter. The prominent prompt gamma peaks at 6.13 MeV and 4.44 MeV were identified from the water, Perspex and graphite targets. The prompt gammas from the liquid nitrogen target were difficult to see, but may not be significant in the in‐vivo verification process.