bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐FG‐207B‐07: Feasibility of Low Dose Lung Cancer Screening with a Whole‐Body Photon Counting CT: First Human Results
Authors: Symons R , Cork T , Folio L , Bluemke D , Pourmorteza A 
Published: July 2016

Purpose:To evaluate the feasibility of using a whole‐body photon counting detector (PCD) CT scanner for low dose lung cancer screening compared to a conventional energy integrating detector (EID) system.Methods:Radiation dose‐matched EID and PCD scans of the COPDGene 2 phantom and 2 human volunteers were acquired. Phantom images were acquired at different radiation dose levels (CTDIvol: 3.0, 1.5, and 0.75 mGy) and different tube voltages (120, 100, and 80 kVp), while human images were acquired at vendor recommended low‐dose lung cancer screening settings. EID and PCD images were compared for quantitative Hounsfield unit accuracy, noise levels, and contrast‐to‐noise ratios (CNR) for detection of ground‐glass nodules (GGNs) and emphysema.Results:The PCD Hounsfield unit accuracy was better for water at all scan parameters, and for lung, GGN and emphysema equivalent regions of interest (ROIs) at 1.5 and 0.75 mGy. PCD attenuation accuracy was more consistent for all scan parameters (all P<0.01), while Hounsfield units for lung, GGN and emphysema ROIs changed significantly for EID with decreasing dose (all P<0.001). PCD showed lower noise levels at the lowest dose setting at 120, 100 and 80 kVp (15.2±0.3 vs 15.8±0.2, P=0.03; 16.1±0.3 vs 18.0±0.4, P=0.003; and 16.1±0.3 vs 17.9±0.3, P=0.001, respectively), resulting in superior CNR for the detection of GGNs and emphysema at 100 and 80 kVp. Significantly lower PCD noise levels were confirmed in volunteer images.Conclusion:PCD provided better Hounsfield unit accuracy for lung, ground‐glass, and emphysema‐equivalent foams at 1.5 and 0.75 mGy with less variability than EID. Additionally, PCD showed less noise, and higher CNR at 0.75 mGy for both 100 and 80 kVp. PCD technology may help reduce radiation exposure in lung cancer screening while maintaining diagnostic quality.
WE‐FG‐207B‐10: Dual‐Energy CT Monochromatic Image Consistency Across Vendors and Platforms
Authors: Jacobsen M , Wood C , Cody D 
Published: July 2016

Purpose:Although dual‐energy CT provides improved sensitivity of HU for certain tissue types at lower simulated energy levels, if these values vary by scanner type they may impact clinical patient management decisions. Each manufacturer has selected a specific dual‐energy CT approach (or in one case, three different approaches); understanding HU variability among low monochromatic images may be required when more than one dual‐energy CT scanner type is available for use.Methods:A large elliptical dualenergy quality control phantom (Gammex Inc.; Middleton, WI) containing several standard tissue type materials was scanned at least three times on each of the following systems: GE HD750, prototype GE Revolution CT with GSI, Siemens Flash, Siemens Edge, Siemens AS 128, and Philips IQon. Images were generated at 50, 70, and 140 keV. Soft tissue and Iodine HU were measured on a single central 5mm‐thick image; NIST constants were used to calculate the ideal HU for each material. Scan acquisitions were approximately dose‐matched (∼25mGy CTDIvol) and image parameters were held as consistent as possible (thickness, kernel, no noise reduction).Results:Measured soft tissue (29 HU at 120 kVp) varied from 28 HU to 44 HU at 50 keV (excluding one outlier), from 21 HU to 31 HU at 70 keV, and from 19 HU to 32 HU at 140 keV. Measured iodine (5mg/ml, 106 HU at 120 kVp) varied from 246 HU to 280 HU at 50 keV, from 123 HU to 129 HU at 70 keV, and from 22 HU to 32 HU at 140 keV.Conclusion:Measured HU in standard rods across 3 dual‐energy CT manufacturers and 6 scanner models varied directly with monochromatic level, with the most variability was observed at 50 keV and least variability at 70keV. Future work will include additional scanner platforms and how measurement variability impacts radiologists.This research has been supported by funds from Dr. William Murphy, Jr., the John S. Dunn, Sr. Distinguished Chair in Diagnostic Imaging at MD Anderson Cancer Center.
WE‐FG‐207B‐11: Objective Image Characterization of Spectral CT with a Dual‐Layer Detector
Authors: Ozguner O , Halliburton S , Dhanantwari A , Wen G , Utrup S , Jordan D 
Published: July 2016

Purpose:To obtain objective reference data for the spectral performance on a dual‐layer detector CT platform (IQon, Philips) and compare virtual monoenergetic to conventional CT images.Methods:Scanning was performed using the hospital's clinical adult body protocol: helical acquisition at 120kVp, with CTDIvol=15mGy. Multiple modules (591, 515, 528) of a CATPHAN 600 phantom and a 20 cm diameter cylindrical water phantom were scanned. No modifications to the standard protocol were necessary to enable spectral imaging. Both conventional and virtual monoenergetic images were generated from acquired data. Noise characteristics were assessed through Noise Power Spectra (NPS) and pixel standard deviation from water phantom images. Spatial resolution was evaluated using Modulation Transfer Functions (MTF) of a tungsten wire as well as resolution bars. Low‐contrast detectability was studied using contrast‐to‐noise ratio (CNR) of a low contrast object.Results:MTF curves of monoenergetic and conventional images were almost identical. MTF 50%, 10%, and 5% levels for monoenergetic images agreed with conventional images within 0.05lp/cm. These observations were verified by the resolution bars, which were clearly resolved at 7lp/cm but started blurring at 8lp/cm for this protocol in both conventional and 70 keV images. NPS curves indicated that, compared to conventional images, the noise power distribution of 70 keV monoenergetic images is similar (i.e. noise texture is similar) but exhibit a low frequency peak at keVs higher and lower than 70 keV. Standard deviation measurements show monoenergetic images have lower noise except at 40 keV where it is slightly higher. CNR of monoenergetic images is mostly flat across keV values and is superior to that of conventional images.Conclusion:Values for standard image quality metrics are the same or better for monoenergetic images compared to conventional images. Results indicate virtual monoenergetic images can be used without any loss in image quality or noise penalties relative to conventional images.This study was performed as part of a research agreement among Philips Healthcare, University Hospitals of Cleveland, and Case Western Reserve University.
WE‐FG‐207B‐12: Quantitative Evaluation of a Spectral CT Scanner in a Phantom Study: Results of Spectral Reconstructions
Authors: Duan X , Arbique G , Guild J , Yagil Y , Anderson J 
Published: July 2016

Purpose:To evaluate the quantitative image quality of spectral reconstructions of phantom data from a spectral CT scanner.Methods:The spectral CT scanner (IQon Spectral CT, Philips Healthcare) is equipped with a dual‐layer detector and generates conventional 80ߝ140 kVp images and variety of spectral reconstructions, e.g., virtual monochromatic (VM) images, virtual non‐contrast (VNC) images, iodine maps, and effective atomic number (Z) images. A cylindrical solid water phantom (Gammex 472, 33 cm diameter and 5 cm thick) with iodine (2.0ߝ20.0 mg I/ml) and calcium (50ߝ600 mg/ml) rod inserts was scanned at 120 kVp and 27 mGy CTDIvol. Spectral reconstructions were evaluated by comparing image measurements with theoretical values calculated from nominal rod compositions provided by the phantom manufacturer. The theoretical VNC was calculated using water and iodine basis material decomposition, and the theoretical Z was calculated using two common methods, the chemical formula method (Z1) and the dual‐energy ratio method (Z2).Results:Beam‐hardening‐like artifacts between high‐attenuation calcium rods (≥300 mg/ml, >800 HU) influenced quantitative measurements, so the quantitative analysis was only performed on iodine rods using the images from the scan with all the calcium rods removed. The CT numbers of the iodine rods in the VM images (50∼150 keV) were close to theoretical values with average difference of 2.4±6.9 HU. Compared with theoretical values, the average difference for iodine concentration, VNC CT number and effective Z of iodine rods were −0.10±0.38 mg/ml, −0.1±8.2 HU, 0.25±0.06 (Z1) and −0.23±0.07 (Z2).Conclusion:The results indicate that the spectral CT scanner generates quantitatively accurate spectral reconstructions at clinically relevant iodine concentrations. Beam‐hardening‐like artifacts still exist when high‐attenuation objects are present and their impact on patient images needs further investigation.YY is an employee of Philips Healthcare.
WE‐G‐204‐01: Building a Career in Academic Medical Physics: The Hardest and Best Job You Will Ever Have!
Authors: Hazle J. 
Published: July 2016

While many indicators for academic medical physics are distressing – jobs are tight, demands on clinical time are high (and getting worse) and national funding has been flat for several years (meaning less money in reality) the present is perhaps one of the most exciting times in cancer research history, and medical physicists have an opportunity to make a difference.Many of us predict the impact of medical physics on cancer research over the next decade to be more significant than ever. Why is that? First, medical imaging is used for every cancer patient in developed countries. Every improvement in the acquisition, processing or analysis of radiological images has the potential to impact patients. The use of radiation therapy is at an all‐time high – and virtually cannot be performed without medical physics. Many of the advances in both biomedical imaging and radiation oncology are the result of the hard work of academic medical physicists who are thinking of the next generation of technologies that will be used against cancer or an even broader spectrum of diseases.A career in academic medical physics is demanding, particularly for those with clinical responsibilities. As the demands for justification of their clinical effort become increasingly metricized, the ability to do “unfunded research” will become even more difficult. This means that many will have to generate external salary support to justify their efforts in research and development. This comes at a time when funding for research is compressed and harder to obtain. Generally speaking, if you are not contributing 50% or more of your effort to research, you are competing at a disadvantage and it is very unlikely you will get an NIH/NCI/NIBIB grant.Furthermore, in the ongoing effort to improve patient care and safety, we have developed credentialing pathways that now require at least two‐years of residency training. This full‐time clinical training creates a gap in the research trajectory of graduate students who aspire to academic positions with an expectation for extramural funding. To address this, several residency programs have created hybrid programs where the two‐years of clinical training is combined with one or two years of research effort to allow candidates to further establish an academic identity and to ensure adequate academic productivity to compete for a beginning faculty position.In conclusion, while the path to a successful career in academic medical physics is steep and sometimes hard to follow, reaching the apex is worth the journey. Different paths to a career in medical physics are available, you just have to decide which one is right for you. If improving cancer care is your goal as a physicist, then academic medical physics is the job for you!
WE‐G‐BRC‐01: Risk Assessment for Radiosurgery
Authors: Kim G. 
Published: July 2016

Failure Mode and Effects Analysis (FMEA) originated as an industrial engineering technique used for risk management and safety improvement of complex processes. In the context of radiotherapy, the AAPM Task Group 100 advocates FMEA as the framework of choice for establishing clinical quality management protocols. However, there is concern that widespread adoption of FMEA in radiation oncology will be hampered by the perception that implementation of the tool will have a steep learning curve, be extremely time consuming and labor intensive, and require additional resources. To overcome these preconceptions and facilitate the introduction of the tool into clinical practice, the medical physics community must be educated in the use of this tool and the ease in which it can be implemented. Organizations with experience in FMEA should share their knowledge with others in order to increase the implementation, effectiveness and productivity of the tool.This session will include a brief, general introduction to FMEA followed by a focus on practical aspects of implementing FMEA for specific clinical procedures including HDR brachytherapy, physics plan review and radiosurgery. A description of common equipment and devices used in these procedures and how to characterize new devices for safe use in patient treatments will be presented. This will be followed by a discussion of how to customize FMEA techniques and templates to one's own clinic. Finally, cases of common failure modes for specific procedures (described previously) will be shown and recommended intervention methodologies and outcomes reviewed.

Learning Objectives:
Understand the general concept of failure mode and effect analysis
Learn how to characterize new equipment for safety
Be able to identify potential failure modes for specific procedures and learn mitigation techniques
Be able to customize FMEA examples and templates for use in any clinic
WE‐G‐209‐01: Digital Radiography
Authors: Schueler B. 
Published: July 2016

Digital radiography, CT, PET, and MR are complicated imaging modalities which are composed of many hardware and software components. These components work together in a highly coordinated chain of events with the intent to produce high quality images. Acquisition, processing and reconstruction of data must occur in a precise way for optimum image quality to be achieved. Any error or unexpected event in the entire process can produce unwanted pixel intensities in the final images which may contribute to visible image artifacts. The diagnostic imaging physicist is uniquely qualified to investigate and contribute to resolution of image artifacts.This course will teach the participant to identify common artifacts found clinically in digital radiography, CT, PET, and MR, to determine the causes of artifacts, and to make recommendations for how to resolve artifacts.

Learning Objectives:
Identify common artifacts found clinically in digital radiography, CT, PET and MR.
Determine causes of various clinical artifacts from digital radiography, CT, PET and MR.
Describe how to resolve various clinical artifacts from digital radiography, CT, PET and MR.
WE‐H‐BRC‐06: A Unified Machine‐Learning Based Probabilistic Model for Automated Anomaly Detection in the Treatment Plan Data
Authors: Chang X , Kalet A , Liu S , Yang D 
Published: July 2016

Purpose:The purpose of this work was to investigate the ability of a machine‐learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information.MethodsIn total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error‐free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network.Results:We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ∼0.08 seconds.Conclusion:The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and automated plan checks.The senior author received research grants from ViewRay Inc. and Varian Medical System.
WE‐H‐BRA‐08: A Monte Carlo Cell Nucleus Model for Assessing Cell Survival Probability Based On Particle Track Structure Analysis
Authors: Lee B , Wang C 
Published: July 2016

Purpose:To correlate the damage produced by particles of different types and qualities to cell survival on the basis of nanodosimetric analysis and advanced DNA structures in the cell nucleus.Methods:A Monte Carlo code was developed to simulate subnuclear DNA chromatin fibers (CFs) of 30nm utilizing a mean‐free‐path approach common to radiation transport. The cell nucleus was modeled as a spherical region containing 6000 chromatin‐dense domains (CDs) of 400nm diameter, with additional CFs modeled in a sparser interchromatin region. The Geant4‐DNA code was utilized to produce a particle track database representing various particles at different energies and dose quantities. These tracks were used to stochastically position the DNA structures based on their mean free path to interaction with CFs. Excitation and ionization events intersecting CFs were analyzed using the DBSCAN clustering algorithm for assessment of the likelihood of producing DSBs. Simulated DSBs were then assessed based on their proximity to one another for a probability of inducing cell death.Results:Variations in energy deposition to chromatin fibers match expectations based on differences in particle track structure. The quality of damage to CFs based on different particle types indicate more severe damage by high‐LET radiation than low‐LET radiation of identical particles. In addition, the model indicates more severe damage by protons than of alpha particles of same LET, which is consistent with differences in their track structure. Cell survival curves have been produced showing the L‐Q behavior of sparsely ionizing radiation.Conclusion:Initial results indicate the feasibility of producing cell survival curves based on the Monte Carlo cell nucleus method. Accurate correlation between simulated DNA damage to cell survival on the basis of nanodosimetric analysis can provide insight into the biological responses to various radiation types. Current efforts are directed at producing cell survival curves for high‐LET radiation.
WE‐H‐202‐01: Memorial Introduction
Authors: Kirby N. 
Published: July 2016

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre‐ and post‐surgery, and auto‐segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed to tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers.

Learning Objectives:
To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future.
To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms.Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement ‐ RaySearch Laboratories. Research Funding ‐ Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.
WE‐H‐202‐03: Accounting for Large Geometric Changes During Radiotherapy
Authors: Hugo G. 
Published: July 2016

Deformable image registration has now been commercially available for several years, with solid performance in a number of sites and for several applications including contour and dose mapping. However, more complex applications have arisen, such as assessing response to radiation therapy over time, registering images pre‐ and post‐surgery, and auto‐segmentation from atlases. These applications require innovative registration algorithms to achieve accurate alignment. The goal of this session is to highlight emerging registration technology and these new applications. The state of the art in image registration will be presented from an engineering perspective. Translational clinical applications will also be discussed to tie these new registration approaches together with imaging and radiation therapy applications in specific diseases such as cervical and lung cancers.

Learning Objectives:
To understand developing techniques and algorithms in deformable image registration that are likely to translate into clinical tools in the near future.
To understand emerging imaging and radiation therapy clinical applications that require such new registration algorithms.Research supported in part by the National Institutes of Health under award numbers P01CA059827, R01CA166119, and R01CA166703. Disclosures: Phillips Medical systems (Hugo), Roger Koch (Christensen) support, Varian Medical Systems (Brock), licensing agreements from Raysearch (Brock) and Varian (Hugo).; K. Brock, Licensing Agreement ‐ RaySearch Laboratories. Research Funding ‐ Varian Medical Systems; G. Hugo, Research grant from National Institutes of Health, award number R01CA166119.; G. Christensen, Research support from NIH grants CA166119 and CA166703 and a gift from Roger Koch. There are no conflicts of interest.