bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐126: An Online Replanning Method for FFF Beams Without Couch Shift
Authors: Ahunbay E , Ates O , Li X 
Published: June 2015

Purpose:In a situation that couch shift for patient positioning is not preferred or prohibited (e.g., MR‐Linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening filter free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here we propose a new 2‐step process to address both the translational effect of FFF beams and the target deformation.Methods:The replanning method consists of an offline and an online steps. The offline step is to create a series of pre‐shifted plans (PSP) obtained by a so called “warm start” optimization (starting optimization from the original plan, rather from scratch) at a series of isocenter shifts with fixed distance (e.g. 2 cm, at x,y,z = 2,0,0; 2,2,0; 0,2,0; …;− 2,0,0). The PSPs all have the same number of segments with very similar shapes, since the warm‐start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two‐step process is completely automated, and instantaneously fast (no optimization or dose calculation needed). The previously‐developed SAM algorithm is then applied for daily deformation. We tested the method on sample prostate and pancreas cases.Results:The two‐step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. The whole process takes the same time as the previously reported SAM process (5–10 min).Conclusion:The new two‐step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation requiring no additional time from the SAM process.This research was supported by Elekta inc. (Crawley, UK)
SU‐E‐J‐131: Augmenting Atlas‐Based Segmentation by Incorporating Image Features Proximal to the Atlas Contours
Authors: Li Dengwang , Liu Li , Kapp Daniel S. , Xing Lei 
Published: June 2015

Purpose:For facilitating the current automatic segmentation, in this work we propose a narrow‐shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas‐based approach.Methods:In setting up an atlas‐based library, we include not only the coordinates of contour points, but also the image features adjacent to the contour. 139 planning CT scans with normal appearing livers obtained during their radiotherapy treatment planning were used to construct the library. The CT images within the library were registered each other using affine registration. A nonlinear narrow shell with the regional thickness determined by the distance between two vertices alongside the contour. The narrow shell was automatically constructed both inside and outside of the liver contours. The common image features within narrow shell between a new case and a library case were first selected by a Speed‐up Robust Features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the images of the new patient by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy function within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto‐segmentation results with that delineated by a physician.Results:Application of the technique to 30 liver cases suggested that the technique was capable of reliably segment organs such as the liver with little human intervention. Compared with the manual segmentation results by a physician, the average and discrepancies of the volumetric overlap percentage (VOP) was found to be 92.43%+2.14%.Conclusion:Incorporation of image features into the library contours improves the currently available atlas‐based auto‐contouring techniques and provides a clinically practical solution for auto‐segmentation.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‐132: Automated Segmentation with Post‐Registration Atlas Selection Based On Mutual Information
Authors: Ren X , Sharp G , Gao H 
Published: June 2015

Purpose:The delineation of targets and organs‐at‐risk is a critical step during image‐guided radiation therapy, for which manual contouring is the gold standard. However, it is often time‐consuming and may suffer from intra‐ and inter‐rater variability. The purpose of this work is to investigate the automated segmentation.Methods:The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave‐one‐out cross‐validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour.Results:MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies.Conclusion:MI has the highest correlation with DC, and therefore is an appropriate choice for post‐registration atlas selection in atlas‐based segmentation.Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500).
SU‐E‐J‐134: Optimizing Technical Parameters for Using Atlas Based Automatic Segmentation for Evaluation of Contour Accuracy Experience with Cardiac Structures From NRG Oncology/RTOG 0617
Authors: Yu J , Gong Y , Bar‐Ad V , Giaddui T , Hu C , Gore E , Wheatley M , Witt J , Robinson C , Kong F , Bradley J , Galvin J , Xiao Y 
Published: June 2015

Purpose:Accurate contour delineation is crucial for radiotherapy. Atlas based automatic segmentation tools can be used to increase the efficiency of contour accuracy evaluation. This study aims to optimize technical parameters utilized in the tool by exploring the impact of library size and atlas number on the accuracy of cardiac contour evaluation.Methods:Patient CT DICOMs from RTOG 0617 were used for this study. Five experienced physicians delineated the cardiac structures including pericardium, atria and ventricles following an atlas guideline. The consistency of cardiac structured delineation using the atlas guideline was verified by a study with four observers and seventeen patients. The CT and cardiac structure DICOM files were then used for the ABAS technique.To study the impact of library size (LS) and atlas number (AN) on automatic contour accuracy, automatic contours were generated with varied technique parameters for five randomly selected patients. Three LS (20, 60, and 100) were studied using commercially available software. The AN was four, recommended by the manufacturer. Using the manual contour as the gold standard, Dice Similarity Coefficient (DSC) was calculated between the manual and automatic contours. Five‐patient averaged DSCs were calculated for comparison for each cardiac structure.In order to study the impact of AN, the LS was set 100, and AN was tested from one to five. The five‐patient averaged DSCs were also calculated for each cardiac structure.Results:DSC values are highest when LS is 100 and AN is four. The DSC is 0.90±0.02 for pericardium, 0.75±0.06 for atria, and 0.86±0.02 for ventricles.Conclusion:By comparing DSC values, the combination AN=4 and LS=100 gives the best performance.This project was supported by NCI grants U24CA12014, U24CA180803, U10CA180868, U10CA180822, PA CURE grant and Bristol‐Myers Squibb and Eli Lilly.
SU‐E‐J‐135: Feasibility of Using Quantitative Cone Beam CT for Proton Adaptive Planning
Authors: Jingqian W , Tsui T , Wei J , Wang Q , Zhang X , Wen Z , Zhu X , Frank S , Li H , Zhu L 
Published: June 2015

Purpose:To investigate the feasibility of using scatter corrected cone beam CT (CBCT) for proton adaptive planning.Methods:Phantom study was used to evaluate the CT number difference between the planning CT (pCT), quantitative CBCT (qCBCT) with scatter correction and calibrated Hounsfield units using adaptive scatter kernel superposition (ASKS) technique, and raw CBCT (rCBCT). After confirming the CT number accuracy, prostate patients, each with a pCT and several sets of weekly CBCT, were investigated for this study. Spot scanning proton treatment plans were independently generated on pCT, qCBCT and rCBCT. The treatment plans were then recalculated on all images. Dose‐volume‐histogram (DVH) parameters and gamma analysis were used to compare between dose distributions.Results:Phantom study suggested that Hounsfield unit accuracy for different materials are within 20 HU for qCBCT and over 250 HU for rCBCT. For prostate patients, proton dose could be calculated accurately on qCBCT but not on rCBCT. When the original plan was recalculated on qCBCT, tumor coverage was maintained when anatomy was consistent with pCT. However, large dose variance was observed when patient anatomy change. Adaptive plan using qCBCT was able to recover tumor coverage and reduce dose to normal tissue.Conclusion:It is feasible to use qu antitative CBCT (qCBCT) with scatter correction and calibrated Hounsfield units for proton dose calculation and adaptive planning in proton therapy.Partly supported by Varian Medical Systems.
SU‐E‐J‐139: One Institution's Experience with Surface Imaging in Proton Therapy
Authors: Zhao L , Singh H , Zheng Y 
Published: June 2015

Purpose:X‐ray system is commonly used for IGRT in proton therapy, however image acquisition not only increases treatment time but also adds imaging dose. We studied a 3D surface camera system (AlignRT) performance for proton therapy.Methods:System accuracy was evaluated with rigid phantom under two different camera location configurations. For initial clinical applications, post mastectomy chest wall and partial breast treatments were studied. X‐ray alignment was used as our ground truth. Our studies included: 1) comparison of daily patient setup shifts between X‐ray alignment and SI calculation; 2) interfractional breast surface position variation when aligning to bony landmark on X‐ray; 3) absolute positioning using planning CT DICOM data; 4) shifts for multi‐isocenter treatment plan; 5) couch isocentric rotation accuracy.Results:Camera locations affected the system performance. After camera relocation, the accuracy of the system for the rigid phantom was within 1 mm (fixed couch), and 1.5 mm (isocentric rotation). For intrafractional patient positioning, X‐ray and AlignRT shifts were highly correlated (r=0.99), with the largest difference (mean ± SD) in the longitudinal direction (2.14 ± 1.02 mm). For interfractional breast surface variation and absolute positioning, there were still larger disagreements between the two modalities due to different focus on anatomical landmarks, and 95% of the data lie within 5mm with some outliers at 7 mm–9 mm. For multi‐isocenter shifts, the difference was 1 ± 0.56 mm over an 11 cm shift in longitudinal direction. For couch rotation study, the differences was 1.36 ± 1.0 mm in vertical direction, 3.04 ± 2.11 mm in longitudinal direction, and 2.10 ± 1.66 mm in lateral direction, with all rotation differences < 1.5 degree.Conclusion:Surface imaging is promising for intrafractional treatment application in proton therapy to reduce X‐ray frequency. However the interfractional discrepancy between the X‐ray and SI requires future validation.
SU‐E‐J‐140: Simulation Study Using Thermoacoustics to Image Proton Dose and Range in Water and Skull Phantom
Authors: Stantz K , Moskvin V 
Published: June 2015

Purpose:In this study, thermoacoustic pressure signals generated from a proton beam were simulated in water and currently within a skull phantom to investigate the sensitivity of radioacoustic CT imaging in the brain.Methods:Thermoacoustically generated pressure signals from a pulse pencil proton beam (12, 15, 20, and 27cm range) were simulated in water. These simulated pressure signal are detected using a (71) transducer array placed along the surface of a cylinder (30cm × 40cm) and rotated over 2π (in 2 degree increments), where the normal vector to the surface of each transducer intersects the isocenter of the scanner. Currently, a software skull phantom is positioned at isocenter, where the scattering, absorption and speed of dispersion of the thermoacoustic signal through a three layer cortical‐trabecular‐cortical structure is being simulated. Based on data obtained from the literature, the effects of acoustic attenuation and speed‐of‐sound (dispersion) will be applied within the 3D FBP algorithm to obtain dosimetric images.Results:Based on hydrophone detector specifications, a 0.5MHz bandwidth and 50dB re 1μPa per Hz^1/2, a 1.6cGy sensitivity at the Bragg peak was demonstrated while maintaining a 1.0 mm (FWHM) range resolution along the central axis of the beam. Utilizing this same information, the integral dose within the Bragg peak and distal edge compared to MC had a 2% (statistical) and 5% voxel‐based RMS at this same dose sensitivity. We plan to present preliminary data determining the range sensitivity for a head phantom for this scanner design and the feasibility of imaging the proton dose in patients with a brain tumor undergoing therapy.Conclusion:RACT scanner provides 3D dosimetric images with 1.6cGy (Bragg peak) sensitivity with 1mm range sensitivity. Simulations will be performed to determine feasibility to treat brain cancer patients.
SU‐E‐J‐150: Four‐Dimensional Cone‐Beam CT Algorithm by Extraction of Physical and Motion Parameter of Mobile Targets Retrospective to Image Reconstruction with Motion Modeling
Authors: Ali I , Alsbou N , Ahmad S 
Published: June 2015

Purpose:To develop 4D‐cone‐beam CT (CBCT) algorithm by motion modeling that extracts actual length, CT numbers level and motion amplitude of a mobile target retrospective to image reconstruction by motion modeling.Methods:The algorithm used three measurable parameters: apparent length and blurred CT number distribution of a mobile target obtained from CBCT images to determine actual length, CT‐number value of the stationary target, and motion amplitude. The predictions of this algorithm were tested with mobile targets that with different well‐known sizes made from tissue‐equivalent gel which was inserted into a thorax phantom. The phantom moved sinusoidally in one‐direction to simulate respiratory motion using eight amplitudes ranging 0–20mm.Results:Using this 4D‐CBCT algorithm, three unknown parameters were extracted that include: length of the target, CT number level, speed or motion amplitude for the mobile targets retrospective to image reconstruction. The motion algorithms solved for the three unknown parameters using measurable apparent length, CT number level and gradient for a well‐defined mobile target obtained from CBCT images. The motion model agreed with measured apparent lengths which were dependent on the actual target length and motion amplitude. The gradient of the CT number distribution of the mobile target is dependent on the stationary CT number level, actual target length and motion amplitude. Motion frequency and phase did not affect the elongation and CT number distribution of the mobile target and could not be determined.Conclusion:A 4D‐CBCT motion algorithm was developed to extract three parameters that include actual length, CT number level and motion amplitude or speed of mobile targets directly from reconstructed CBCT images without prior knowledge of the stationary target parameters. This algorithm provides alternative to 4D‐CBCT without requirement to motion tracking and sorting of the images into different breathing phases which has potential applications in diagnostic CT imaging and radiotherapy.
SU‐E‐J‐152: Synthetic Gated CBCT Imaging for Lung SBRT
Authors: Li X , Zhang P , Gewanter R , Chan M , Tang X , Hunt M 
Published: June 2015

Purpose:Due to slow scanning speed, when free breathing CBCT (FBCBCT) is used to localize target in lung SBRT treatment, respiratory motion induced image artifacts may degrade the image quality and compromise the target positioning accuracy. The purpose of this study is to investigate the feasibility of synthetic gated CBCT (SGCBCT) imaging for lung SBRT treatment.Methods:Two Lung SBRT patients were selected for this study. The amplitudes of tumor respiratory motion were 1.2cm and 3.0cm (Superior‐Inferior).Each Lung SBRT patient underwent FBCBCT scan, and then the acquired projection images were exported to an in‐house program for off‐line analysis. Firstly a semi‐automatic algorithm was used to trace the diaphragm movement from those projection images in order to correlate respiratory phases with each projection image. Secondly, a Feldkamp‐Davis‐Kress (FDK) algorithm was utilized to reconstruct the gated CBCT (GCBCT) images based on the projection images within the gating windows (35∼65% for exhale; 85∼15% for inhale). Thirdly, the bony structures were automatically segmented from FBCBCT images using Fuzzy C‐mean segmentation method. Finally, the SGCBCT was generated by replacing the HU value of the GCBCT image with the value of FBCBCT image for the voxels that belong to the bony structure based on the above segmentation results.Results:Due to the respiratory motion effect, the targets determined by FBCBCT have reduced image contrast and blurred boundary between tumor and surrounding normal lung tissues. Compared to FBCBCT, the SGCBCT images at exhale and inhale not only delineate the target with enhanced image contrast and much clearer boundary with less motion artifacts, but also determine the amplitudes of tumor respiratory motion.Conclusion:SGCBCT provides better representation of the moving lung tumor with less motion artifacts, and has a potential to improve the positioning accuracy in lung SBRT treatment.
SU‐E‐J‐153: Reconstructing 4D Cone Beam CT Images for Clinical QA of Lung SABR Treatments
Authors: Beaudry J , Cropp R , Bergman A 
Published: June 2015

Purpose:To verify that the planned Primary Target Volume (PTV) and Internal Gross Tumor Volume (IGTV) fully enclose a moving lung tumor volume as visualized on a pre‐SABR treatment verification 4D Cone Beam CT.Methods:Daily 3DCBCT image sets were acquired immediately prior to treatment for 10 SABR lung patients using the on‐board imaging system integrated into a Varian TrueBeam (v1.6: no 4DCBCT module available). Respiratory information was acquired during the scan using the Varian RPM system. The CBCT projections were sorted into 8 bins offline, both by breathing phase and amplitude, using in‐house software. An iterative algorithm based on total variation minimization, implemented in the open source reconstruction toolkit (RTK), was used to reconstruct the binned projections into 4DCBCT images. The relative tumor motion was quantified by tracking the centroid of the tumor volume from each 4DCBCT image. Following CT‐CBCT registration, the planning CT volumes were compared to the location of the CBCT tumor volume as it moves along its breathing trajectory. An overlap metric quantified the ability of the planned PTV and IGTV to contain the tumor volume at treatment.Results:The 4DCBCT reconstructed images visibly show the tumor motion. The mean overlap between the planned PTV (IGTV) and the 4DCBCT tumor volumes was 100% (94%), with an uncertainty of 5% from the 4DCBCT tumor volume contours. Examination of the tumor motion and overlap metric verify that the IGTV drawn at the planning stage is a good representation of the tumor location at treatment.Conclusion:It is difficult to compare GTV volumes from a 4DCBCT and a planning CT due to image quality differences. However, it was possible to conclude the GTV remained within the PTV 100% of the time thus giving the treatment staff confidence that SABR lung treatements are being delivered accurately.
SU‐E‐J‐157: Comparison Between Internal Respiratory Signal and External Respiratory Signal in 4D CT
Authors: Suh Y , Hui C , Robertson D , Pan T , Das P , Crane C , Beddar S 
Published: June 2015

Purpose:To investigate the effectiveness of using a Fourier transformbased internal respiratory signal (IRS) to sort four‐dimensional (4D) CT images, compared to images sorted using the external respiratory signal from the real‐time position management (RPM) system.Methods:80 patients with abdominal or thoracic cancers were retrospectively selected for this study. The IRS were computed using an algorithm previously described by our group. 4D images were then sorted using the end inspiration times calculated from the IRS. We compared the IRS to the RPM signals by assessing their similarities using the normalized cross correlation (NCC), and the end inspiration times identified from the respective signals. The IRS‐sorted 4D images were also compared to the RPM‐sorted clinical images using a correlation‐based metric to compare their image qualities.Results:In 90% of the data sets, the IRS matched with the RPM signal with NCC >= 0.5. In this cohort of matching signals, the average difference between end inspiration times from the IRS and RPM signal was 0.11 s. In the remaining 10% of the data sets, the IRS did not match the RPM signal in at least one couch position (NCC < 0.5). The average difference between end inspiration times was 0.73 s in the non‐matching couch positions. The correlation‐based metric indicated poorer matching of neighboring couch positions in the RPM‐sorted images. This implied that, when the IRS did not match the RPM signal, the images sorted using the IRS showed fewer artifacts than the clinical images sorted using the RPM signal.Conclusion:The IRS is in good agreement with the RPM signal in most cases. In some cases, the IRS provides better sorting than the RPM signal, especially when the RPM system fails. The IRS can therefore be a viable complementary tool or even a replacement for the RPM system.