bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐E‐J‐189: Credentialing of IGRT Equipment and Processes for Clinical Trials
Authors: Court L , Aristophanous M , Bellezza D , Followill D , Kirsner S , Kisling K , Massingill B , Papanikolaou N , Parker B , Pidikiti R , Wong P , Zhen H , Balter P 
Published: July 2014

Purpose:Current dosimetry phantoms used for clinical trial credentialing do not directly assess IGRT processes. This work evaluates a custom‐built IGRT phantom for credentialing of multiple IGRT modalities and processes.Methods:An IGRT phantom was built out of a low‐density body with two inserts. Insert A is used for the CT simulation. Insert B is used for the actual treatment. The inserts contain identical targets in different locations. Relative positions are unknown to the user. The user simulates the phantom (with insert A) as they would a patient, including marking the phantom. A treatment plan is created and sent to the treatment unit. The phantom (with insert B) is then positioned using local IGRT practice. Shifts (planned isocenter, if applicable, and final isocenter) are marked on the phantom using room lasers. The mechanical reproducibility of re‐inserting the inserts within the phantom body was tested using repeat high‐resolution CT scans. The phantom was tested at 7 centers, selected to include a wide variety of imaging equipment.Results:Mechanical reproducibility was measured as 0.5‐0.9mm, depending on the direction. Approaches tested to mark (and transfer) simulation isocenter included lasers, fiducials and reflective markers. IGRT approaches included kV imaging (Varian Trilogy, Brainlab ExacTrac), kV CT (CT‐on‐rails), kV CBCT (Varian Trilogy, Varian Truebeam, Elekta Agility) and MV CT (Tomotherapy). Users were able to successfully use this phantom for all combinations of equipment and processes. IGRT‐based shifts agreed with the truth within 0.8mm, 0.8mm and 1.9mm in the LR, AP, and SI directions, respectively.Conclusion:Based on these preliminary results, the IGRT phantom can be used for credentialing of clinical trials with an action level of 1mm in AP and LR directions, and 2mm in the SI direction, consistent with TG142. We are currently testing with additional institutions with different equipment and processes, including Cyberknife.This project was funded by the Cancer Prevention Research Institute of Texas
SU‐E‐J‐190: Characterization of Radiation Induced CT Number Changes in Tumor and Normal Lung During Radiation Therapy for Lung Cancer
Authors: Yang C , Liu F , Tai A , Gore E , Johnstone C , Li X 
Published: July 2014

Purpose:To measure CT number (CTN) changes in tumor and normal lung as a function of radiation therapy (RT) dose during the course of RT delivery for lung cancer using daily IGRT CT images and single respiration phase CT images.Methods:4D CT acquired during planning simulation and daily 3D CT acquired during daily IGRT for 10 lung cancer cases randomly selected in terms of age, caner type and stage, were analyzed using an in‐house developed software tool. All patients were treated in 2 Gy fractions to primary tumors and involved nodal regions. Regions enclosed by a series of isodose surfaces in normal lung were delineated. The obtained contours along with target contours (GTVs) were populated to each singlephase planning CT and daily CT. CTN in term of Hounsfield Unit (HU) of each voxel in these delineated regions were collectively analyzed using histogram, mean, mode and linear correlation.Results:Respiration induced normal lung CTN change, as analyzed from single‐phase planning CTs, ranged from 9 to 23 (±2) HU for the patients studied. Normal lung CTN change was as large as 50 (±12) HU over the entire treatment course, was dose and patient dependent and was measurable with dose changes as low as 1.5 Gy. For patients with obvious tumor volume regression, CTN within the GTV drops monotonically as much as 10 (±1) HU during the early fractions with a total dose of 20 Gy delivered. The GTV and CTN reductions are significantly correlated with correlation coefficient >0.95.Conclusion:Significant RT dose induced CTN changes in lung tissue and tumor region can be observed during even the early phase of RT delivery, and may potentially be used for early prediction of radiation response. Single respiration phase CT images have dramatically reduced statistical noise in ROIs, making daily dose response evaluation possible.
SU‐E‐J‐191: Automated Detection of Anatomic Changes in H' N Patients
Authors: Usynin A , Ramsey C 
Published: July 2014

Purpose:To develop a novel statistics‐based method for automated detection of anatomical changes using cone‐beam CT data. A method was developed that can provide a reliable and automated early warning system that enables a “just‐in‐time” adaptation of the treatment plan.Methods:Anatomical changes were evaluated by comparing the original treatment planning CT with daily CBCT images taken prior treatment delivery. The external body contour was computed on a given CT slice and compared against the corresponding contour on the daily CBCT. In contrast to threshold‐based techniques, a statistical approach was employed to evaluate the difference between the contours using a given confidence level. The detection tool used the two‐sample Kolmogorov‐Smirnov test, which is a non‐parametric technique that compares two samples drawn from arbitrary probability distributions. 11 H' N patients were retrospectively selected from a clinical imaging database with a total of 186 CBCT images. Six patients in the database were confirmed to have anatomic changes during the course of radiotherapy. Five of the H' N patients did not have significant changes. The KS test was applied to the contour data using a sliding window analysis. The confidence level of 0.99 was used to moderate false detection.Results:The algorithm was able to correctly detect anatomical changes in 6 out of 6 patients with an excellent spatial accuracy as early as at the 14th elapsed day. The algorithm provided a consistent and accurate delineation of the detected changes. The output of the anatomical change tool is easy interpretable, and can be shown overlaid on a 3D rendering of the patient's anatomy.Conclusion:The detection method provides the basis for one of the key components of Adaptive Radiation Therapy. The method uses tools that are readily available in the clinic, including daily CBCT imaging, and image co‐registration facilities.
SU‐E‐J‐195: Quantification of Rotations and Deformations in Head and Heck Radiotherapy
Authors: Kim J , Liu C , Kumarasiri A , Chetvertkov M , Gordon J , Siddiqui F , Chetty I J 
Published: July 2014

Purpose:To quantify the magnitude of patient rotations and deformations over the course of head and neck radiotherapy.Methods:45 CBCT images acquired weekly were selected retrospectively from 5 head and neck cancer patients. CBCT images were registered to the corresponding planning CT images using translation, rigid‐body, and cubic B‐spline deformable transformations. Elastix (elastix.isi.uu.nl), an open‐source public domain registration algorithm, was used for all registrations with mutual information as similarity metric and gradient descent as optimization. Four‐level multiresolution approach was employed with 2 cm B‐spline grid spacing at the finest resolution. Registration qualities were visually verified. Rotational occurrences were quantified from the rigid‐body registrations. The magnitude of deformations in planning target volumes (PTV) and organs were quantified from the deformation vector fields with rigid registrations as baseline.Results:The measured rotations (mean±std) were 1.0±1.0 deg, – 0.3±1.0 deg, and 0.1±0.8 deg in the sagittal, coronal, and axial plains respectively. The estimated magnitudes of deformations (mean±std) were 3.4±2.1 mm for PTVs, 2.9±1.2 mm for parotid glands, 2.8±1.2 mm for mandible, and 2.2±1.1 mm for spinal cord. The overall deformation in the body contour was 4.3±4.3 mm. The organ spatial deviation due to rotation was small and approximately 8.4% of that from deformations. Results varied among patients.Conclusion:Rotations occurred about 1.0 deg (1SD) in the H' N area. Daily geometric deviation of target volumes was considerable and mainly from tissue deformation than rotations. Proper safety margin is necessary for adequate target coverage. Online plan adaptation may mitigate the daily differences between the plan and actual patient poses.Partially supported by a grant from Varian Medical Systems, Palo Alto, CA.
SU‐E‐J‐196: New Visualization Methods for Longitudinal MRI Registrations and Segmentations
Authors: Veeraraghavan H , Deasy J 
Published: July 2014

Purpose:To develop visualization techniques to facilitate easy assessment of (a) registration and (b) tracking volumetric changes in structures during radiation therapy from MRI.Method:The frequently used method for visualizing registrations between scans is a multi‐color overlay technique or deformation vector fields. However, the overlay technique is unintuitive and does not help to appreciate the quality of registration particularly when the registration mismatches are not very large. Similarly, the deformation fields give an indication of extent of deformation but do not help to assess the differences in registration. We present a mirroring and edge‐augmented mirroring technique that places the fixed and moving image next to each other and allows the user to quickly assess the small differences in registration. Next, we present a volumetric intersection based 3D model to visualize the changes in diseased lymph node volumes in head and neck cancer. 3D model‐based visualization provides more information about the location‐specific changes in volume rather than the simplistic one dimensional information obtained from 2D plot of nodal volume changes.Result:We show results comparing our approach with the standard colorbased overlay method for comparing registrations of intra‐patient registrations using T2‐MRI. Upon comparing the mirroring technique with the color‐overlay, one can more easily appreciate the differences in registration. Adding edge‐based mirroring seems to further assist in evaluating the registration. Our approach for viewing registrations seems to be more intuitive and easy to use in order to help assess the quality of registration compared to color‐based overlays. Similarly, the change volumetric model together with a 2D plot reveals more information including the locations undergoing changes and responding to treatment.Conclusions:Better approaches are necessary for assessing the quality of registrations and changes in diseased structures over time. Our approach presents some novel visualization solutions for registration and segmentation.
SU‐E‐J‐201: Position Verification in Breast Cancer Radiotherapy Using Tantalum Clips in the Lumpectomy Cavity
Authors: van Santvoort J , Van der Drift M , Kuipers J , Mast M , Van Egmond J , Struikmans H 
Published: July 2014

Purpose:To find out whether tantalum surgical clips can be used for online position verification in treatment of the lumpectomy cavity (LC) in breast cancer patients. Tantalum is a high density metal that could be visible on Electronic Portal Images (EPIs) and be an affordable alternative to gold markers. Clips are considered more representative for the LC position than nearby bony structures.Methods:In twelve patients the surgeon had placed 2 to 5 tantalum clips in the LC. The AP and lateral fields used for portal imaging, were adapted. In doing so, both bony structures and tantalum clips were visible on EPIs. The following analyses were performed:1. Image degradation, with respect to delineating the CTV, of the axial CT slices by artefacts because of the tantalum clips was evaluated by a radiation oncologist;2. The visibility of the tantalum clips on the EPIs was evaluated by four radiation therapists (RTTs);3. Bony anatomy and tantalum clip matches were performed on the same images independently by two observers.Results:1. Delineation of the CTV by the radiation oncologist was not hampered by CT image artefacts because of the clips.2. The mean score for visibility of the clips on the EPIs, analysed by the four RTTs, was 5.6 on a scale of 10 (range 3.9 – 8.0).3. In total 12 patients with 16 fractions each were analysed. The differences between clip match and bone match are significant with a mean vector length of 5.2 mm (SD 1.9 mm) for the difference.Conclusion:Results of matches on tantalum clips as compared to matches on bony structures differ substantially. Therefore clip matches can result in smaller CTV to PTV margins than bone matches. Visibility of the clips on EPIs is sufficient, so they can be an alternative to gold markers.
SU‐E‐J‐202: Is Pretreatment Imaging at Each Treatment Fraction Needed in Spine SBRT to Enable Margin Reduction?
Authors: Mynampati D , Yaparpalvi R , Garg M , Hong L , Kuo H , Tome' W 
Published: July 2014

Purpose:To study translational setup errors of Spine SBRT treatments using BodyFIX immobilization system and the potential for margin reduction using pretreatment imaging.Methods:148 online match results of 39 Spine SBRT patients treated at our institution were reviewed. All treatments performed using Varian, TrueBeam and MI™, BodyFIX immobilization device. Prescriptions (dose‐fractionation schedules) for the patients in the study are 8 Gy X 3 (n=24), 6 Gy X 4 (n=10) and 16Gy X 1 (n=5). For all patients, OBI was performed on verification day and before each treatment. Recorded shifts are from adopted couch positions after physician online review of KV image‐pair and CBCT. Data analyzed by computing Mean error (M), Systematic error (Σ) and Random error (σ) for three translation directional shifts and 3D Vector length. To estimate correlation between imaging sessions, Anova‐test and T‐Test with alpha of 0.05 performed over the data.Results:Mean error (M), Systematic error (Σ) and Random error (σ) of Vertical direction are ‐1.81mm, 4.05mm and 2.01mm respectively. Similarly, Errors in Longitudinal direction are 0.76mm (M), 4.21mm (Σ) and 1.34mm (σ). Errors in Lateral direction are – 0.16mm (M), 3.22mm (Σ) and 1.83 mm (σ). Mean, SD and CI95% of 3D Vector lengths are 7.93mm, 4.41mm and 0.7mm respectively. Anova‐test results show, P‐Values for longitudinal and lateral shifts are > 0.3 and P‐value of vertical shift is 0.054. This signifies mean of the each imaging session are not significantly different. T‐Test on treatment sessions data of vertical shifts with respect to verification day shows, mean of Vertical shifts on verification day are different from Vertical shifts on treatment sessions.Conclusion:Results shows immobilization with BodyFIX is reproducible in the mm range. To eliminate systematic error component and to enable margin reduction, pretreatment imaging prior to each treatment fraction is indispensable.
SU‐E‐J‐204: Radiation Dose to Patients Resulting From Image Guidance Procedures and AAPM TG‐180 Update
Authors: Ding G , Alaei P 
Published: July 2014

Purpose:Image‐guided radiation therapy (IGRT) is the new paradigm for patient positioning and target localization in radiotherapy. Daily imaging procedures add additional dose to the patient's treatment volume and normal tissues and may expose the organs at risk to unaccounted doses. This presentation is to update the progress of AAPM TG‐180 which aims to provide strategies to quantify and account the dose from both MV and kV imaging in patient treatment planning.Methods:Our current knowledge on image guidance dose is presented. A summary of doses from image guidance procedures delivered to patients in relationship with therapeutic doses is given. Different techniques in reducing the image guidance dose are summarized. Typical organ doses resulting from different image acquisition procedures used in IGRT are tabulated.Results:Many techniques to reduce the imaging doses are available in clinical applications. There are large variations between dose to bone and dose to soft tissues for x‐rays at kilovoltage energy range. Methods for clinical implementation of accounting for the imaging dose from an imaging procedure are available. Beam data from imaging systems can be generated by combining Monte Carlo simulations and experimental measurements for commissioning imaging beams in the treatment planning.Conclusion:The current treatment planning systems are not yet equipped to perform patient specific dose calculations resulting from kV imaging procedures. The imaging dose from current kV image devices has been significantly reduced and is generally much less than that resulting from MV. Because the magnitude of kV imaging dose is significantly low and the variation between patients is modest, it is feasible to estimate dose based on imaging producers or protocols using tabulated values which provides an alternative to accomplish the task of accounting and reporting imaging doses.
SU‐E‐J‐206: A Comparison of Different Hardware Design Approaches for Feature‐Supported Optical Head‐Tracking with Respect to Angular Dependencies
Authors: Stueber P , Wissel T , Wagner B , Bruder R , Schweikard A , Ernst F 
Published: July 2014

Purpose:Recent research has shown that optical features significantly improve marker‐less optical head‐tracking for cranial radiotherapy. Simulations, however, showed that these optical features, which are used to derive tissue thickness, depend on the incident angle of the IR scanning laser beam and the perspective of the camera analyzing the reflective patterns. We present an experimental analysis determining which is the most robust optical setup concerning angular influences.Methods:In three consecutive experiments, the incident angle of the laser (1), the perspective of the camera (2) or both simultaneously (3, ‘inBeam’‐perspective) were changed with respect to the target. We analyzed how this affects feature intensity. These intensities were determined from seven concentric regions of interest (ROIs) around the laser spot. Two targets were used: a tissue‐like silicone phantom and a human's forehead.Results:For each experiment, the feature intensity generally decreases with increasing angle. We found that the optical properties of the silicone phantom do not fit the properties of human skin. Furthermore, the angular influence of the laser on the features is significantly higher than the perspective of the camera. With the ‘inBeam’‐ perspective, the smoothest decays of feature intensity were found. We suppose that this is because of a fixed relationship between both devices. This smoothness, suggesting a predictable functional relationship, may simplify angle compensation for machine learning algorithms. This is particularly prominent for the medial ROIs. The inner ROIs highly depend on the angle and power of the laser. The outer ROIs show less angular dependency but the signal strength is critically low and prone to artifacts. Therefore and because of the smooth decays, medial ROIs are a suitable tradeoff between susceptibility, signal‐noise‐ratio and distance to the center of the laser spot.Conclusion:For tissue thickness correlated feature acquisition, the medial ROIs with the ‘inBeam’‐setup provide most valuable featuresThis work was supported by Varian Medical Systems Inc. (Palo Alto, CA, USA). In addition, this work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1].
SU‐E‐J‐209: Verification of 3D Surface Registration Between Stereograms and CT Images
Authors: Han T , Smith B , Salehpour M , Gifford K 
Published: July 2014

Purpose:Stereography can provide a visualization of the skin surface for radiation therapy patients. The aim of this study was to verify the registration algorithm in a commercial image analysis software, 3dMDVultus, for the fusion of stereograms and CT images.Methods:CT and stereographic scans were acquired of a head phantom and a deformable phantom. CT images were imported in 3dMDVultus and the surface contours were generated by threshold segmentation. Stereograms were reconstructed in 3dMDVultus. The resulting surfaces were registered with Vultus algorithm and then exported to in‐house registration software and compared with four algorithms: rigid, affine, non‐rigid iterative closest point (ICP) and b‐spline algorithm. RMS (root‐mean‐square residuals of the surface point distances) error between the registered CT and stereogram surfaces was calculated and analyzed.Results:For the head phantom, the maximum RMS error between registered CT surfaces to stereogram was 6.6 mm for Vultus algorithm, whereas the mean RMS error was 0.7 mm. For the deformable phantom, the maximum RMS error was 16.2 mm for Vultus algorithm, whereas the mean RMS error was 4.4 mm. Non‐rigid ICP demonstrated the best registration accuracy, as the mean of RMS errors were both within 1 mm.Conclusion:The accuracy of registration algorithm in 3dMDVultus was verified and exceeded RMS of 2 mm for deformable cases. Non‐rigid ICP and b‐spline algorithms improve the registration accuracy for both phantoms, especially in deformable one. For those patients whose body habitus deforms during radiation therapy, more advanced nonrigid algorithms need to be used.
SU‐E‐J‐210: Thoracic Atlas Segmentation Model Based On Consensus Contours
Authors: Turian J , Wu Z , Miller L , Darwish N , Templeton A , Chu J 
Published: July 2014

Purpose:Generation of a thoracic segmentation atlas based on consensus contours obtained through input from individual expertsMethod and Materials:A cohort of 30 patients treated for lung disease was selected for this study. All patients had 4D‐CT studies with image segmentation based on respiration‐averaged images. For each subject, three experts delineated the eighteen agreed‐upon structures using tools available in Eclipse ver.10.0. Consensus structures were generated using CERR(4.0β2). The EM‐STAPLE method was chosen to generate the consensus contours based on published reports which state that iterative methods yield better results over analytical methods in medical imaging. Once the consensus contours were generated they were re‐imported in Eclipse for further processing and evaluation. For each structure, each expert's contour was scored against the consensus using two scoring indices: Dice similarity coefficient, DSC, and Dice‐Jaccard coefficient, DJC. Visual inspection was always performed to detect large segmentation errorsResults:The respiration‐averaged CT set proved to be adequate for all structures included in the thoracic atlas. As expected, anatomic variability between patients was larger than the variability due to the level of expertise. Across the entire cohort the smallest structure segmented was the brachial plexus with a 9.4cc (+/− 3.5) volume, while the chest wall was the largest (2933cc +/‐560). The DSC and DJC coefficients were smallest for Left Brachial Plexus 0.894 (+/−0.138) and 0.842 (+/− 0.138), respectively, while the largest were 0.992(+/−0.004) and 0.990(+/− 0.004), observed for the right lung.Conclusions:Consensus contours generated using expert input were analyzed using comparison indices and visual evaluation. With appropriate atlas selection (e.g. patient gender, arm position) a robust thoracic atlas segmentation tool can be set up and may be used in conjunction with an automatic segmentation algorithm.Project supported by Varian Medical System