bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐A‐12A‐06: Intra‐Observer Variability in Delineation of Target Volumes in Breast Radiotherapy and Its Effect On Accuracy of Deformation Measurements
Authors: Juneja P , Bonora M , Evans P , Harris E 
Published: July 2014

Purpose:In breast radiotherapy, the target volume may change during treatment and need adaptation of the treatment plan. This is possible for both tumour bed (TB) and whole breast (WB) target volumes. Delineation of the target (to detect changes) is also subject to uncertainty due to intra‐ and inter‐observer variability. This work measured the uncertainty, due to intraobserver variability, in the quantification of tissue deformation.Methods:Datasets consisting of paired prone and supine CT scans of three patients were used. Significant deformation in target volumes is expected between prone and supine patient positions. The selected cases had 1) no seroma, 2) some seroma, and 3) large seroma. The TB and WB were outlined on each dataset three times by one clinician. Delineation variability was defined as the standard deviations of the distances between observer outlines. For each target volume and each case, tissue deformation between prone and supine delineations was quantified using the Dice similarity coefficient (DSC) and the average surface distance (ASD). The uncertainty in the tissue deformation (due to delineation variability) was quantified by measuring the ranges of DSC and ASD using all combinations of pairs of outlines (9 pairs).Results:For the TB, the range of delineation variability was 0.44‐1.16 mm. The deformation, DSC and ASD, (and uncertainty in measurement) of the TB between prone and supine position of the cases were: 1) 0.21 (0.17‐0.28) and 12.4 mm (11.8‐13 mm); 2) 0.54 (0.51‐0.57) and 3.3 mm (3.1‐3.5 mm); 3) 0.62 (0.61‐0.64) and 4.9 mm (4.6‐5.2 mm). WB deformation measurements were subject to less uncertainty due to delineation variability than TB deformation measurements.Conclusion:For the first time, the uncertainty, due to observer variability, in the measurement of the deformation of breast target volumes was investigated. Deformations in these ranges would be difficult to detect.This work was supported in part by Cancer Research‐UK under Programme Grant C46/A10588 and in part by the National Institute for Health Research (NIHR) through funding of the biomedical research imaging centre. P. Juneja is supported by the EPSRC Platform Grant EP/H046526/1.
TU‐A‐12A‐09: Absolute Blood Flow Measurement in a Cardiac Phantom Using Low Dose CT
Authors: Ziemer B , Hubbard L , Lipinski J , Molloi S 
Published: July 2014

Purpose:To investigate a first pass analysis technique to measure absolute flow from low dose CT images in a cardiac phantom. This technique can be combined with a myocardial mass assignment to yield absolute perfusion using only two volume scans and reduce the radiation dose to the patient.Methods:A four‐chamber cardiac phantom and perfusion chamber were constructed from poly‐acrylic and connected with tubing to approximate anatomical features. The system was connected to a pulsatile pump, input/output reservoirs and power contrast injector. Flow was varied in the range of 1‐2.67 mL/s with the pump operating at 60 beats/min. The system was imaged once a second for 14 seconds with a 320‐row scanner (Toshiba Medical Systems) using a contrast‐enhanced, prospective‐gated cardiac perfusion protocol. Flow was calculated by the following steps: subsequent images of the perfusion volume were subtracted to find the contrast entering the volume; this was normalized by an upstream, known volume region to convert Hounsfield (HU) values to concentration; this was divided by the subtracted images time difference. The technique requires a relatively stable input contrast concentration and no contrast can leave the perfusion volume before the flow measurement is completed.Results:The flow calculated from the images showed an excellent correlation with the known rates. The data was fit to a linear function with slope 1.03, intercept 0.02 and an R2 value of 0.99. The average root mean square (RMS) error was 0.15 mL/s and the average standard deviation was 0.14 mL/s. The flow rate was stable within 7.7% across the full scan and served to validate model assumptions.Conclusion:Accurate, absolute flow rates were measured from CT images using a conservation of mass model. Measurements can be made using two volume scans which can substantially reduce the radiation dose compared with current dynamic perfusion techniques.
TU‐B‐19A‐01: Image Registration II: TG132‐Quality Assurance for Image Registration
Authors: Brock K , Mutic S 
Published: July 2014

AAPM Task Group 132 was charged with a review of the current approaches and solutions for image registration in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes. As the results of image registration are always used as the input of another process for planning or delivery, it is important for the user to understand and document the uncertainty associate with the algorithm in general and the Result of a specific registration. The recommendations of this task group, which at the time of abstract submission are currently being reviewed by the AAPM, include the following components. The user should understand the basic image registration techniques and methods of visualizing image fusion. The disclosure of basic components of the image registration by commercial vendors is critical in this respect. The physicists should perform end‐to‐end tests of imaging, registration, and planning/treatment systems if image registration is performed on a stand‐alone system. A comprehensive commissioning process should be performed and documented by the physicist prior to clinical use of the system. As documentation is important to the safe implementation of this process, a request and report system should be integrated into the clinical workflow. Finally, a patient specific QA practice should be established for efficient evaluation of image registration results. The implementation of these recommendations will be described and illustrated during this educational session.Learning Objectives:

Highlight the importance of understanding the image registration techniques used in their clinic.
Describe the end‐to‐end tests needed for stand‐alone registration systems.
Illustrate a comprehensive commissioning program using both phantom data and clinical images.
Describe a request and report system to ensure communication and documentation.
Demonstrate an clinically‐efficient patient QA practice for efficient evaluation of image registration.
TU‐B‐16A‐01: To Which Journal Should I Submit My Paper
Authors: Williamson J , Mills M , Klein E , Pawlicki T 
Published: July 2014

Research papers authored by Medical Physicists address a large spectrum of oncologic, imaging, or basic research problems; exploit a wide range of physical and engineering methodologies; and often describe the efforts of a multidisciplinary research team. Given the large number (about 100) competing journals accepting medical physics articles, it may not be clear to an individual author which journal is the best venue for disseminating their work to the scientific community. Relevant factors usually include the Journal’s audience and scientific impact, but also such factors as perceived acceptance rate, interest in their topic, and quality of service. The purpose of this symposium is to provide the medical physics community with an overview of scope, review processes, and article guidelines for the following journals: Medical Physics, International Journal of Radiation Biology and Physics, Journal of Applied Clinical Medical Physics, and Practical Radiation Oncology. The senior editors for each journal will provide details as to the journals review process, for example: single blind versus double blind reviews; the hierarchy of the review process in terms of editorial board structure; the reality of acceptance, in terms of acceptance rate; and the types of research the journal prefers to publish. The goal is to provide for authors guidance before they begin to write their papers, not only for proper formatting, but also that the readership is appropriate for the particular paper, hopefully increasing the likelihood of publication.Learning Objectives:

To review each Journal’s submission and review process
Guidance as to how to increase chances of acceptance
To help decipher which journal is appropriate for a given work
TU‐C‐BRD‐01: Image Guided SBRT I: Multi‐Modality 4D Imaging
Authors: Cai J , Mageras G , Pan T 
Published: July 2014

Motion management is one of the critical technical challenges for radiation therapy. 4D imaging has been rapidly adopted as essential tool to assess organ motion associated with respiratory breathing. A variety of 4D imaging techniques have been developed and are currently under development based on different imaging modalities such as CT, MRI, PET, and CBCT. Each modality provides specific and complementary information about organ and tumor respiratory motion. Effective use of each different technique or combined use of different techniques can introduce a comprehensive management of tumor motion. Specifically, these techniques have afforded tremendous opportunities to better define and delineate tumor volumes, more accurately perform patient positioning, and effectively apply highly conformal therapy techniques such as IMRT and SBRT. Successful implementation requires good understanding of not only each technique, including unique features, limitations, artifacts, imaging acquisition and process, but also how to systematically apply the information obtained from different imaging modalities using proper tools such as deformable image registration. Furthermore, it is important to understand the differences in the effects of breathing variation between different imaging modalities. A comprehensive motion management strategy using multi‐modality 4D imaging has shown promise in improving patient care, but at the same time faces significant challenges. This session will focuses on the current status and advances in imaging respiration‐induced organ motion with different imaging modalities: 4D‐CT, 4D‐MRI, 4D‐PET, and 4D‐CBCT/DTS.Learning Objectives:

Understand the need and role of multimodality 4D imaging in radiation therapy.
Understand the underlying physics behind each 4D imaging technique.
Recognize the advantages and limitations of each 4D imaging technique.
TU‐C‐17A‐03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotheray
Authors: Chen H , Tan J , Kavanaugh J , Dolly S , Gay H , Thorstad W , Anastasio M , Altman M , Mutic S , Li H 
Published: July 2014

Purpose:Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process.Methods:The contouring tool was developed using an object‐oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3‐D mesh models from critical RT structures in real‐time and 360° visualization. Principal component analysis (PCA) was used for system self‐updating geometry variations of normal structures based on physician‐approved RT contours as a training dataset. The inhouse design of supervised PCA‐based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer.Results:The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3‐D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi‐slice view, which allows physicians to visually check and edit abnormally contoured structures.Conclusion:This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand‐alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.
TU‐C‐17A‐04: BEST IN PHYSICS (THERAPY) – A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head‐ Neck Cancer
Authors: Chen H , Kavanaugh J , Tan J , Dolly S , Gay H , Thorstad W , Anastasio M , Altman M , Mutic S , Li H 
Published: July 2014

Purpose:Precise contour delineation of tumor targets and critical structures from CT simulations is essential for accurate radiotherapy (RT) treatment planning. However, manual and automatic delineation processes can be error prone due to limitations in imaging techniques and individual anatomic variability. Tedious and laborious manual verification is hence needed. This study develops a general framework for automatically assessing RT contours for head‐neck cancer patients using geometric attribute distribution models (GADMs).Methods:Geometric attributes (centroid and volume) were computed from physician‐approved RT contours of 29 head‐neck patients. Considering anatomical correlation between neighboring structures, the GADM for each attribute was trained to characterize intra‐ and interpatient structure variations using principal component analysis. Each trained GADM was scalable and deformable, but constrained by the principal attribute variations of the training contours. A new hierarchical model adaptation algorithm was utilized to assess the RT contour correctness for a given patient. Receiver operating characteristic (ROC) curves were employed to evaluate and tune system parameters for the training models.Results:Experiments utilizing training and non‐training data sets with simulated contouring errors were conducted to validate the framework performance. Promising assessment results of contour normality/abnormality for the training contour‐based data were achieved with excellent accuracy (0.99), precision (0.99), recall (0.83), and F‐score (0.97), while corresponding values of 0.84, 0.96, 0.83, and 0.9 were achieved for the non‐training data. Furthermore, the areas under the ROC curves were above 0.9, validating the accuracy of this test.Conclusion:The proposed framework can reliably identify contour normality/abnormality based upon intra‐ and inter‐structure constraints derived from clinically‐approved contours. It also allows physicians to analytically determine the system parameters to fit various clinic requirements (e.g. as‐low‐as‐possible false positives). It has great potential for improving RT work flow. More geometric attributes and training sets will be investigated to improve framework performance in the future.
TU‐C‐17A‐06: Evaluating IMRT Plan Deliverability Via PTV Shape and MLC Motion
Authors: McGurk R , Smith VA , Price M 
Published: July 2014

Purpose:For step‐and‐shoot intensity‐modulated radiation therapy (IMRT) plans, the dosimetry and deliverability can be affected by the number and shape of the segments used. Thus, plan deliverability is likely related to target volume and shape. We investigated whether the sphericity of target volumes and the previously proposed Modulation Complexity Score (MCS) could be used together to improve the detection of IMRT fields that failed quality assurance (QA).Methods:526 and 353 IMRT fields from 32 prostate and 28 head‐and‐neck (H' N) patients, respectively, were analyzed. MCS was used to quantify the complexity of multi‐leaf collimator shapes and motion patterns for each field. Sphericity was calculated using the surface area and volume of each patient’s planning target volume (PTV). Logistic regression models with MCS‐alone or MCS and sphericity terms were fit to PlanUNC IMRT pass/fail results (5% dose difference, 4mm distance‐to‐agreement criteria) using SAS 9.3 (Cary, NC). Model concordance, discordance and area under the curve (AUC) were used to quantify model accuracy.Results:Mean (±1 standard deviation) MCS for prostate and H' N were 0.58(±0.15) and 0.40 (±0.14), respectively. Mean sphericity scores were 0.75(±0.05) for prostate and 0.63 (±0.12) for H' N. Both metrics were significantly different between treatment locations (p<0.01, Wilcoxon Rank Sum Test) indicating greater complexity in shape and variations for H' N PTVs. For prostate, concordance, discordance and AUC using MCS alone were 80.8%, 18.7% and 0.811. Including sphericity in the model improved these to 81.7%, 17.7% and 0.820. For H' N, the original concordance, discordance and AUC were of 72.9%, 26.9% and 0.729. Including sphericity into the model improved these metrics to 76.5%, 23.2% and 0.729.Conclusion:Sphericity provides a quantitative measure of PTV shape. While improvement in IMRT QA failure detection was modest for both prostate and H' N plans, including sphericity in the model does improve IMRT field failure prediction.
TU‐C‐17A‐11: Progressive Knowledge Modeling for Pelvic IMRT/VMAT Treatment Planning
Authors: Lu S , Yuan L , Craciunescu O , Ge Y , Yin F , Wu Q 
Published: July 2014

Purpose:To investigate the feasibility of progressive knowledge modeling for IMRT/VMAT treatment planning for multiple cancer types in the pelvic region.Methods:The treatment planning knowledge model quantifies correlations between patient anatomical features and the OAR dose sparing features. The model is trained with prior plans using a stepwise regression machine learning technique. The progressive modeling process starts with 20 low risk prostate plans (type 1) which offer simplest PTV‐OAR geometry. Cases with more complex PTV‐OAR anatomies (prostate with lymph node, type 2 and anal rectal, type 3) are added to the training dataset one by one until the model prediction accuracies reach a plateau. The starting point of the plateau also defines the minimum numbers of type 2 and 3 training cases required for modelling. The DVHs predicted by the knowledge model for bladder, femoral heads and rectum were validated by 20, 9 and 18 cases with type 1, 2, and 3 geometries, respectively (rectum DVHs are omitted for type 3). Mean and standard deviation of differences between the dosimetric parameters sampled from the DVHs and the corresponding actual plan values measure the prediction accuracy of the model. Further, the accuracy was also compared with the single‐type models which were trained by single type cases.Results:Prediction accuracy reaches plateau when 6 type 2 and 8 type 3 cases were added to the training dataset. The determination coefficients R2 (should be square, font) for the OARs are: Bladder 0.90, rectum 0.64, and femoral heads 0.82. The prediction accuracies by the multiple‐type model and single‐type model have no significant differences by F‐test (p‐value: bladder: 0.58, femoral head: 0.44, rectum: 0.97).Conclusion:Progressive knowledge modeling of OAR sparing for multiple cancer types in pelvic region is feasible and has comparable accuracy to single cancer type modeling.Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.
TU‐C‐12A‐02: Development of a Multiparametric Statistical Response Map for Quantitative Imaging
Authors: Bosca R , Mahajan A , Johnson VE , Brown PD , Dong L , Stafford RJ , Jackson EF 
Published: July 2014

Purpose:Quantitative imaging biomarkers (QIB) are becoming increasingly utilized in early phase clinical trials as a means of non‐invasively assessing treatment response and associated response heterogeneity. The aim of this study was to develop a flexible multiparametric statistical framework to predict voxel‐by‐voxel response of several potential MRI QIBs.Methods:Patients with histologically proven glioblastomas (n=11) were treated with chemoradiation (with/without bevacizumab) and underwent one baseline and two mid‐treatment (3–4wks) MRIs. Dynamic contrast‐enhanced (3D FSPGR, 6.3sec/phase, 0.1 mmol/kg Gd‐DTPA), dynamic susceptibility contrast (2D GRE‐EPI, 1.5sec/phase, 0.2mmol/kg Gd‐DTPA), and diffusion tensor (2D DW‐EPI, b=0, 1200 s/mm2, 27 directions) imaging acquisitions were obtained during each study. Mid‐treatment and pre‐treatment images were rigidly aligned, and regions of partial response (PR), stable disease (SD), and progressive disease (PD) were contoured in consensus by two experienced radiation oncologists. Voxels in these categories were used to train ordinal (PR<SD<PD) and logistic (PR/SD<PD) models incorporating the apparent diffusion coefficient, fractional anisotropy, Ktrans, ve, vp, and relative cerebral blood volume imaging biomarkers, as well as treatment type. Leave‐one‐out cross‐validation was performed at the patient level to assess model prediction accuracy.Results:Ordinal regression resulted in model prediction accuracies of 60% (PR), 0% (SD), 81% (PD), and 69% (overall), with coefficients of variation (COV) of 9.4%, 9.6%, and 23.6%, respectively. Logistic regression resulted in accuracies of 82.0% (PR/SD), 46.2% (PD), and 76.2% (overall) with COVs of 22.4%, 45.7%, and 23.8%, respectively.Conclusion:Despite limited patient numbers, this feasibility pilot study demonstrates that ordinal and logistic regression models potentially provide a flexible statistical framework for incorporating longitudinal multiparametric quantitative imaging data and, for these particular data, can potentially distinguish PR from PD. Additional training data exhibiting a full range of treatment responses is expected to allow prediction of all three treatment outcomes. Moreover, these models are extensible to multi‐modality, multiparametric analyses.