bluuebunny/crossref_metadata_2025_split huggingface dataset:
MO‐A‐16A‐01: QA Procedures and Metrics: In Search of QA Usability
Authors: Sathiaseelan V , Thomadsen B 
Published: July 2014

Radiation therapy has undergone considerable changes in the past two decades with a surge of new technology and treatment delivery methods. The complexity of radiation therapy treatments has increased and there has been increased awareness and publicity about the associated risks. In response, there has been proliferation of guidelines for medical physicists to adopt to ensure that treatments are delivered safely. Task Group recommendations are copious, and clinical physicists' hours are longer, stretched to various degrees between site planning and management, IT support, physics QA, and treatment planning responsibilities.Radiation oncology has many quality control practices in place to ensure the delivery of high‐quality, safe treatments. Incident reporting systems have been developed to collect statistics about near miss events at many radiation oncology centers. However, tools are lacking to assess the impact of these various control measures. A recent effort to address this shortcoming is the work of Ford et al (2012) who recently published a methodology enumerating quality control quantification for measuring the effectiveness of safety barriers. Over 4000 near‐miss incidents reported from 2 academic radiation oncology clinics were analyzed using quality control quantification, and a profile of the most effective quality control measures (metrics) was identified. There is a critical need to identify a QA metric to help the busy clinical physicists to focus their limited time and resources most effectively in order to minimize or eliminate errors in the radiation treatment delivery processes. In this symposium the usefulness of workflows and QA metrics to assure safe and high quality patient care will be explored. Two presentations will be given:Quality Metrics and Risk Management with High Risk Radiation Oncology ProceduresStrategies and metrics for quality management in the TG‐100 EraLearning Objectives:

Provide an overview and the need for QA usability metrics: Different cultures/practices affecting the effectiveness of methods & metrics.
Show examples of quality assurance workflows, Statistical process control, that monitor the treatment planning and delivery process to identify errors.
To learn to identify and prioritize risks and QA procedures in radiation oncology.
Try to answer the question: Can a quality assurance program aided by quality assurance metrics help minimize errors and ensure safe treatment delivery. Should such metrics be institution specific.
MO‐A‐BRD‐02: Noise Suppression for Dual‐Energy CT Through Entropy Minimization
Authors: Petrongolo M , Niu T , Zhu L 
Published: July 2014

Purpose:In dual energy CT (DECT), noise amplification during signal decomposition significantly limits the utility of basis material images. Since clinically relevant objects contain a limited number of materials, we propose to suppress noise in decomposed images through entropy minimization within a 2D transformation space. Distinct from other noise suppression techniques, the entropy minimization method does not estimate and suppress noise based on spatial variations of signals and thus maximally preserves image spatial resolution.Methods:From decomposed images, we first generate a 2D plot of scattered data points, using basis material densities as coordinates. Data points representing the same material generate a cluster with a highly asymmetric shape. We orient an axis by minimizing the entropy in a 1D histogram of these points projected onto the axis. To suppress noise, we replace the pixel values of decomposed images with center‐of‐mass values in the direction perpendicular to the optimized axis. The proposed method's performance is assessed using a Catphan 600 phantom and an anthropomorphic head phantom. Electron density calculations are used to quantify its accuracy. Our results are compared to those without noise suppression, with a filtering method, and with a recently developed iterative method.Results:On both phantoms, the proposed method reduces noise standard deviations of the decomposed images by at least on order of magnitude. In the Catphan study, this method retains the spatial resolution of the CT images and increases the accuracy of electron density calculations. In the head phantom study, the proposed method outperforms the others in retaining fine, intricate structures.Conclusion:This work shows that the proposed method of noise suppression through entropy minimization for DECT suppresses noise without loss of spatial resolution while increasing electron density calculation accuracy. Future investigations will analyze possible bias and incorporate spatial information into the technique for further noise reduction.The work is partially supported by the NIH under the grant number R21EB012700.
MO‐A‐BRD‐05: Evaluation of Composed Lung Ventilation with 4DCT and Image Registration
Authors: Du K , Reinhardt J , Christensen G , Ding K , Zhao B , Bayouth J 
Published: July 2014

Purpose:Regional pulmonary function can be derived using fourdimensional computed tomography (4DCT) combined with deformable image registration. However, only peak inhale and exhale phases have been used thus far while the lung ventilation during intermediate phases is not considered. In our previous work, we have investigated the spatiotemporal heterogeneity of lung ventilation and its dependence on respiration effort. In this study, composed ventilation is introduced using all inspiration phases and compared to direct ventilation. Both methods are evaluated against Xe‐CT derived ventilation.Methods:Using an in‐house tissue volume preserving deformable image registration, unlike the direct ventilation method, which computes from end expiration to end inspiration, Jacobian ventilation maps were computed from one inhale phase to the next and then composed from all inspiration steps. The two methods were compared in both patients prior to RT and mechanically ventilated sheep subjects. In addition, they wereassessed for the correlation with Xe‐CT derived ventilation in sheep subjects. Annotated lung landmarks were used to evaluate the accuracy of original and composed deformation field.Results:After registration, the landmark distance for composed deformation field was always higher than that for direct deformation field (0IN to 100IN average in human: 1.03 vs 1.53, p=0.001, and in sheep: 0.80 vs0.94, p=0.009), and both increased with longer phase interval. Direct and composed ventilation maps were similar in both sheep (gamma pass rate 87.6) and human subjects (gamma pass rate 71.9),and showed consistent pattern from ventral to dorsal when compared to Xe‐CT derived ventilation. Correlation coefficient between Xe‐CT and composed ventilation was slightly better than the direct method but not significant (average 0.89 vs 0.85, p=0.135).Conclusion:More strict breathing control in sheep subjects may explain higher similarity between direct and composed ventilation. When compared to Xe‐CT ventilation, no significant difference was found for the composed method.NIH Grant: R01 CA166703
MO‐A‐9A‐01: Innovation in Medical Physics Practice: 3D Printing Applications
Authors: Ehler E , Perks J , Rasmussen K , Bakic P 
Published: July 2014

3D printing, also called additive manufacturing, has great potential to advance the field of medicine. Many medical uses have been exhibited from facial reconstruction to the repair of pulmonary obstructions. The strength of 3D printing is to quickly convert a 3D computer model into a physical object. Medical use of 3D models is already ubiquitous with technologies such as computed tomography and magnetic resonance imaging. Thus tailoring 3D printing technology to medical functions has the potential to impact patient care. This session will discuss applications to the field of Medical Physics. Topics discussed will include introduction to 3D printing methods as well as examples of real‐world uses of 3D printing spanning clinical and research practice in diagnostic imaging and radiation therapy. The session will also compare 3D printing to other manufacturing processes and discuss a variety of uses of 3D printing technology outside the field of Medical Physics.Learning Objectives:

Understand the technologies available for 3D Printing
Understand methods to generate 3D models
Identify the benefits and drawbacks to rapid prototyping / 3D Printing
Understand the potential issues related to clinical use of 3D Printing
MO‐A‐12A‐01: Joint Imaging Education – Quantitative Imaging Symposium: Genomics and Image‐Omics for Medical Physicists
Authors: Giger M , Cowperthwaite M , Markey M 
Published: July 2014

With the growth of quantitative imaging and quantitative image analysis, image‐based biomarkers (i.e. image‐based phenotypes) are becoming potential descriptors in personalized medicine, as well as in cancer discovery and neuroscience research. Thus, there is a need to understand (i) the quantitation and standardization involved with image‐based phenotypes and (ii) the aspects of datamining and association studies involved in “conventional” phenotype‐genotype studies. Imaging scientists and genomic scientists need to be able to speak the same language in this common era of Big Data.Learning Objectives:

Understand what are image‐based phenotypes and their potential medical significance.
Appreciate the complexity of Big Data.
Learn about genetic association studies.
MO‐B‐18C‐01: Proton Therapy II: Proton Stereotactic Radiotherapy
Authors: Winey B , Daartz J 
Published: July 2014

Proton stereotactic radiotherapy shares fundamental principles with general proton therapy physics, specifically range uncertainties and broad beam measurement techniques. Significant differences emerge when treating with smaller field sizes that suffer lateral disequilibrium and when fractions are reduced. This session will explore the history and scope of proton stereotactic radiotherapy in clinical practice. Uncertainties and treatment planning methods specific to stereotactic treatments will be discussed. The session will include an overview of the physical properties of small proton fields and resulting needs for accurate measurements and modeling of dose distributions for radiosurgery treatment planning.Learning Objectives:

Understand the clinical rationale for proton radiosurgery.
Understand the similarities and differences from general proton therapy.
Understand the similarities and differences from photon stereotactic radiosurgery.
Understand the basic physics and clinical physics methods for measuring and commissioning a radiosurgery program.
MO‐B‐16A‐01: Memorial to Donald D. Tolbert – Memorial Lecture
Authors: Morin R 
Published: July 2014

The Medical Physics community lost one of its prominent leaders in April, 2013 with the passing of Donald D. Tolbert, PhD. He received his Doctorate at the University of Kansas followed by post Doctoral training at Florida State University and the University of Wisconsin. He was Chief of Radiation Therapy Medical Physics at the University of Wisconsin Hospital for 7 years before relocating to Honolulu Hawaii, where he founded the consulting group Mid‐Pacific Medical Physics.Don was a leader in both the AAPM and the ACR, chairing the Professional Council and the Commission on Medical Physics. He was active on the AAPM Board of Directors and a member of the ACR Board of Chancellors. Dr. Tolbert's approach to the difficult problems of the times was admired and respected by colleagues in Medical Physics, Radiation Oncology, and Diagnostic Radiology. He always rose above the heated political rhetoric and led the discussion to higher ground. His wisdom was continually sought to solve complicated problems.Following retirement, he returned to homes in Kansas and Colorado, devoting his time to writing about coping with diabetes and providing support for Seniors in Beloit Kansas.Don is survived by his wife, Mattie, his 3 children and 5 grandchildren. He will be greatly missed.
MO‐C‐BRE‐01: The WMIS‐AAPM Joint Symposium: Advances in Molecular Imaging
Authors: Contag C , Pogue B , Lewis J 
Published: July 2014

This joint symposium of the World Molecular Imaging Society (WMIS) and the AAPM includes three luminary speakers discussing work in new paradigms of molecular imaging in cancer (Contag), applications of optical imaging technologies to radiation therapy (Pogue) and an update on PET imaging as a surrogate biomarker for cancer progression and response to therapy.Learning Objectives:

Appreciate the current trends in molecular and systems imaging.
Understand how optical imaging technologies, and particularly Cerenkov detectors, can be used in advancing radiation oncology.
Stay current on new PET tracers – and targets – of interest in cancer treatment.
MO‐C‐17A‐04: Forecasting Longitudinal Changes in Oropharyngeal Tumor Morphology Throughout the Course of Head and Neck Radiation Therapy
Authors: Yock A , Rao A , Dong L , Beadle B , Garden A , Kudchadker R , Court L 
Published: July 2014

Purpose:To generate, evaluate, and compare models that predict longitudinal changes in tumor morphology throughout the course of radiation therapy.Methods:Two morphology feature vectors were used to describe the size, shape, and position of 35 oropharyngeal GTVs at each treatment fraction during intensity‐modulated radiation therapy. The feature vectors comprised the coordinates of the GTV centroids and one of two shape descriptors. One shape descriptor was based on radial distances between the GTV centroid and 614 GTV surface landmarks. The other was based on a spherical harmonic decomposition of these distances. Feature vectors over the course of therapy were described using static, linear, and mean models. The error of these models in forecasting GTV morphology was evaluated with leave‐one‐out cross‐validation, and their accuracy was compared using Wilcoxon signed‐rank tests. The effect of adjusting model parameters at 1, 2, 3, or 5 time points (adjustment points) was also evaluated.Results:The addition of a single adjustment point to the static model decreased the median error in forecasting the position of GTV surface landmarks by 1.2 mm (p<0.001). Additional adjustment points further decreased forecast error by about 0.4 mm each. The linear model decreased forecast error compared to the static model for feature vectors based on both shape descriptors (0.2 mm), while the mean model did so only for those based on the inter‐landmark distances (0.2 mm). The decrease in forecast error due to adding adjustment points was greater than that due to model selection. Both effects diminished with subsequent adjustment points.Conclusion:Models of tumor morphology that include information from prior patients and/or prior treatment fractions are able to predict the tumor surface at each treatment fraction during radiation therapy. The predicted tumor morphology can be compared with patient anatomy or dose distributions, opening the possibility of anticipatory re‐planning.American Legion Auxiliary Fellowship; The University of Texas Graduate School of Biomedical Sciences at Houston
MO‐C‐17A‐06: Online Adaptive Re‐Planning to Account for Independent Motions Between Multiple Targets During Radiotherapy of Lung Cancer
Authors: Liu F , Tai A , Ahunbay E , Gore E , Johnstone C , Li X 
Published: July 2014

Purpose:To quantify interfractional independent motions between multiple targets in radiotherapy (RT) of lung cancer, and to study the dosimetric benefits of an online adaptive replanning method to account for these variations.Methods:Ninety five diagnostic‐quality daily CTs acquired for 9 lung cancer patients treated with IGRT using an in‐room CT (CTVision, Siemens) were analyzed. On each daily CT set, contours of the targets (GTV, CTV, or involved nodes) and organs at risk were generated by populating the planning contours using an auto‐segmentation tool (ABAS, Elekta) with manual editing. For each patient, an IMRT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2Gy fractions. Three plans were generated and compared for each daily CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes and segment weights of the original plan to conform to the daily anatomy, and a new fully re‐optimized plan based on the daily CT using a planning system (Panther, Prowess).Results:The daily deviations of the distance between centers of masses of the targets from the plans varied daily from ‐10 to 8 mm with an average −0.9±4.1 mm (one standard deviation). The average CTV V100 are 99.0±0.7%, 97.9±2.8%, 99.0±0.6%, and 99.1±0.6%, and the lung V20 Gy 928±332 cc, 944±315 cc, 917±300 cc, and 891±295 cc for the original, repositioning, adaptive, and re‐optimized plans, respectively. Wilcoxon signed‐rank tests show that the adaptive plans are statistically significantly better than the repositioning plans and comparable with the reoptimized plans.Conclusion:There exist unpredictable, interfractional, relative volume changes and independent motions between multiple targets during lung cancer RT which cannot be accounted for by the current IGRT repositioning but can be corrected by the online adaptive replanning method.
MO‐C‐17A‐07: Building Atlas for Automatic Prostate IMRT Planning: Anatomical Feature Parameterization and Classification
Authors: Sheng Y , Li T , Zhang Y , Yin F , Ge Y , Wu Q 
Published: July 2014

Purpose:To establish and evaluate a prostate IMRT plan atlas to provide accurate dose guidance for dose‐guided automatic planning (DAP) technique.Methods:25 prostate cancer patients treated with IMRT were studied. The anatomy of prostate cancer cases was parameterized and quantified into two measures: the percent distance‐to‐prostate (PDP) and the concaveness angle. Patients were represented by points at a 2D space spanned by the two anatomical measures. The k‐medoids algorithm was used to elect k cases from 25 points to compose the atlas. To determine the optimal size k, the average silhouette width for each k‐medoids scheme was calculated and the k corresponding to higher average silhouette width was chosen as the atlas size. The other cases were used as query cases to evaluate the atlas efficacy. The DAP technique, which links atlas and query case using deformable image registration, was used to generate the automatic IMRT plan. Dosimetric comparison between DAP plans and clinical plans were performed.Results:Atlases of sizes ranging from 2 to 9 are established. The average silhouette width for each atlas of the size from 2 to 9 is 0.651, 0.558, 0.455, 0.563, 0.620, 0.580, 0.636, and 0.695, respectively. The size of 6 is preliminary chosen as the optimal size for robustness test. Dosimetric similarities are observed between two plans (p>0.05): homogeneity indices are 6.7±1.2% and 7.3±1.6%; rectum‐gEUDs are 40.6±1.8 Gy and 40.6±2.1 Gy, for DAP plans and clinical plans, respectively. Improved conformity index and bladder‐gEUD are observed in the DAP plan (p=0.036, 0.009 respectively).Conclusion:An atlas with 6 expert prostate IMRT plans was identified as the optimal size. DAP plans generated with the dose guidance from the atlas show comparable plan quality with clinical plans. The atlas can cover various anatomical configurations and provide satisfying dose guidance when implemented with the DAP technique.
MO‐C‐17A‐09: Deformation Image Registration Using a Spatial‐Context Regularization Filter
Authors: Zhang Y , Yang J , Zhang L , Balter P , Dong L , Court L 
Published: July 2014

Purpose:Intensity‐based deformable registration with spatial‐invariant regularization will generally fail when distinct motion exists across the interface of different type of tissues. The purpose of this work was to develop a new regularization scheme for deformable registration that is adaptive to motion discontinuities along different types of tissue.Methods:Our approach was built upon a multi‐resolution Demons‐type framework, and used the prior knowledge defined in the image context as an additional constraint to regularize deformation vector fields, which leads to a spatialcontext vector smoothing filter. This filter is spatial‐variant and anisotropic, and can be reduced to the product of a Gaussian function of spatial locations and an additional Gaussian function of image intensity difference. This additional regularization favors the motion vectors within the same tissue type, but penalized the motions vectors from different tissues. Five lung patient cases from a benchmark data set, each with 300 landmark pairs delineated by a physician, were adopted for the validation of our algorithm. Our approach was also compared against a state‐of‐the‐art implementation of dual‐force Demons algorithm. We also included one difficult lung case with large sliding motion and one head‐neck cases with large motion in air cavity into our validatioResults:For five lung cases, the mean and standard deviation of the landmark displacements were 1.3±0.8(mm) using our approach, but this number went up to 2.3±2.9(mm) using Demons algorithm. Particularly for a case with largest initial displacement of 15± 9(mm), our approach reduced the mean displacement to 1.3±1.1(mm), but Demons can only achieve 3.6±5.9(mm). We also found that, for the two difficult cases in which the original intensity‐based registration failed the registration accuracy was drastically improved using the proposed algorithm.Conclusion:We have demonstrated the effectiveness of an algorithm using adaptive regularization in addressing large distinct motion across different types of tissue.This work is partially supported by the CPRIT (Cancer Prevention Research Institute of Texas) grant RP110732
MO‐C‐17A‐10: Comparison of Dose Deformable Accumulation by Using Parallel and Serial Approaches
Authors: Gao Z , Li M , Wong J 
Published: July 2014

Purpose:The uncertainty of dose accumulation over multiple CT datasets with deformable fusion may have significant impact on clinical decisions. In this study, we investigate the difference of two dose summation approaches involving deformable fusion.Methods:Five patients, four external beam and one brachytherapy(BT), were chosen for the study. The BT patient was treated with CT‐based HDR. The CT image sets acquired in the imageguidance process (8‐11 CTs/patient) were used to determine the dose delivered to the four external beam patients. (prostate, pelvis, lung and head&neck). For the HDR patient (cervix), five CT image sets and the corresponding BT plans were used. In total 44 CT datasets and RT dose/plans were imported into the image fusion software MiM (6.0.4) for analysis.For each of the five clinical cases, the dose from each fraction was accumulated into the primary CT dataset by using both Parallel and Serial approaches. The dose‐volume histogram (DVH) for CTV and selected organs‐at‐risks (OAR) were generated. The D95(CTV), OAR(mean) and OAR(max) for the four external beam cases the D90(CTV), and the max dose to bladder and rectum for the BT case were compared.Results:For the four external beam patients, the difference in D95(CTV) were <1.2% PD between the parallel and the serial approaches. The differences of the OAR(mean) and the OAR(max) range from 0 to 3.7% and <1% PD respectively. For the HDR patient, the dose difference for D90 is 11% PD while that of the max dose to bladder and rectum were 11.5% and 23.3% respectively.Conclusion:For external beam treatments, the parallel and serial approaches have <5% difference probably because tumor volume and OAR have less changes from fraction to fraction. For the brachytherapy case, >10% dose difference between the two approaches was observed as significant volume changes of tumor and OAR were observed among treatment fractions.