bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐C‐AUD A‐02: Middleware for the Clinic — Taking Small Steps Towards Implementing New Technologies Into Routine Practice
Authors: Dong L 
Published: July 2008

New products from vendors may not work immediately in many clinics due to a variety of reasons. One of the main reasons, however, is due to the fact that most clinics are in a multi‐vendor environment. The workflow demonstrated by the vendor may not completely work in a multi‐vendor / hospital‐specific environment. Similarly, new technologies are not perfect; they often require supplementary workflow solutions to accomplish the main goal. Often in time, vendor's support is limited. Simply waiting for the next product release is not always working. As the technology leader in a radiation oncology team, medical physicists should be actively involved in the technology implementation process, which can be hospital‐specific. However, there are common steps in achieving the goal of technology integration. The first step is often involved in identifying why things are not working. This trouble‐shooting process may require the assistant from vendors or friends in other hospitals. Once the main problem is identified, value‐adding new ideas should be sought as alternative solutions. After an alternative solution is judged feasible, resources need to be identified to implement it in the clinic. This resource‐seeking process may require you to “sell” your idea to your chief physicist, the vendor, or other members of the radiation oncology team. It is important to get buy‐ins in the clinic before you invest time significantly. Successful clinical implementation often relies on the key clinical issues identified. Technologies used for such implementation do not need to be complicated, definitely not a complete overhaul of the existing workflow. While inventing a technical solution may not be easy, the biggest challenge is to take it live in your clinic. In addition, continuous support, improvement and adapting to changes are required. It is worth mentioning that many of these middleware solutions can be short‐lived. The goal is not to insist “in‐house” solutions. Rather, the eventual goal is the clinical efficacy for your clinic. Examples of in‐house solutions will be discussed to illustrate these processes and experience will be shared among other presenters.Educational Objectives:1. To suggest common strategies for in‐house implementation of new technologies.2. To share experience on how to work effectively with your vendors.3. To identify challenges in in‐house solutions.
TU‐C‐AUD A‐04: Designing and Implementing Clinical Studies Involving New Technology
Authors: Fraass B 
Published: July 2008

Many of the most important advances in modern radiotherapy have been developed, implemented, tested, and/or perfected through in‐house clinical research work. Study of the possible benefits of new technology and new techniques for treatment planning and delivery is crucial if we are to learn when and how to effectively use these capabilities. This presentation will use our experience designing clinical studies of normal tissue toxicity, tumor dose escalation, use of functional imaging, and other on‐going clinical efforts to illustrate both the challenges involved in clinical studies as well as some of the methods that have proven to be helpful to our research efforts. Performing clinical studies which have appropriate goals, are designed to include adequate clinical support, and which eventually result in clinically documented improvements for our patients can be a very valuable and satisfying contribution to improved patient care.Educational Objectives:1. To illustrate clinical study design techniques.2. To discuss some of the methods which help make clinical studies viable in a busy clinic.3. To highlight some of the challenges associated with clinical research efforts.
TU‐C‐AUD B‐08: Identification of a 3D Dosimeter Best‐Suited for Use by the RPC
Authors: Heard M , Followill D , White R , Jackson E , Salehpour M , Ibbott G 
Published: July 2008

Purpose: To characterize several 3D dosimeters and identify one that is best‐suited for use by the RPC in its anthropomorphic phantoms. This study will also provide useful data to other researchers when selecting a 3D dosimeter. Method and Materials: Three different dosimeters were investigated in this study: PRESAGE™ (a polyurethane and leuko dye dosimeter), and BANG® and PAGAT polymer gels. Each dosimeter was characterized to determine the dose response, linearity, reproducibility, spatial stability, and dose resolution. Both MRI and OCT were used to identify the best‐suited imaging modality for each polymer gel formulation. Goals were established for each parameter. These were used to evaluate the dosimeters' characteristics and identify the dosimeter best‐suited for use by the RPC. Results: Each 3D dosimeter demonstrated a useful dose range up to 10 Gy. Only the BANG® dosimeter provided a linear dose response over this range. The uncertainty in the determination of the response of the dosimeter was ∼2% when imaged with OCT; however, MRI yielded uncertainties < 1%. The low uncertainties achievable with MRI resulted in improved dose resolution. OCT required shorter imaging times than MRI; OCT produced planar images in 30 seconds. The BANG and PAGAT dosimeters displayed overshoots in response adjacent to regions of high dose gradient. No response overshoots were observed in PRESAGE™ dosimeters irradiated with steep gradients. The intra and inter‐batch reproducibilities of PRESAGE™ exceed the goals established for these parameters. The spatial stability and reproducibility of the polymer gel formulations is in progress. Conclusion: This study provides a framework for evaluating 3D dosimeters and identifying one best‐suited for a particular use. This study also provides the first comparison of polymer gels characterized with both MRI and OCT.The investigation was supported by PHS grant CA 10953 awarded by the NCI, DHHS.
TU‐C‐351‐10: Inference of Hysteretic Respiratory Tumour Motion From External Surrogates: A State Augmentation Approach
Authors: Ruan D , Fessler J , Balter J , Berbeco R , Nishioka S , Shirato H 
Published: July 2008

Purpose: To infer internal respiration‐induced tumor motion from external surrogate. To systematically resolve mapping ambiguity caused by breathing hysteresis. Method and Materials: We propose a state‐augmentation approach to capture system dynamics. Concatenating real‐time surrogate observations with their time‐delayed records describes the state information in a higher‐dimensional state space. In such space, inhale and exhale “stages” are naturally separated due to the incorporated velocity contents. Any existing inference model migrates effortlessly into this framework. We illustrate the idea with simple polynomial inference models, and derive a closed‐form solution for optimal choice of model parameters. Choice of lag length is demonstrated empirically to be robust and may be chosen offline. This approach is tested on synchronized recordings of internal tumor trajectories and external fiducial marker readouts from eight lung patients (multiple fractions and readings) with a Mitsubishi real‐time radiotherapy (RTRT) system. Internal recording is obtained by fluoroscopic tracking of implanted 1.5mm‐diameter gold ball bearings around the tumor and external surrogates measure relative abdominal skin positions. Results: Examination of trajectories in the augmented state‐space suggests the existence of a consistent and unambiguous inference map. Empirical tests with clinical data show that using state augmentation decreases the 3D RMSE from 2.01mm to 1.74mm with the linear model and 1.93mm to 1.63 with the quadratic model. Paired student‐t tests with P‐values on the order of 10e‐13 indicate statistical significance of the improvement. Conclusion: We proposed a simple state‐augmentation approach to implicitly incorporate the hysteretic internal‐external response pattern into the estimation framework with any existing inference model. For the general class of correspondence models that are linear in their parameters, closed‐form solutions for the optimal parameter values and the error evaluations are derived. Tests with clinical data demonstrate statistically significant improvement over direct models.This work is sponsored by NIH P01‐CA59827 and Barbour Scholarship.
TU‐C‐352‐01: Adapting Traditional Clinical Medical Physics to Digital Radiography
Authors: Willis C , Jones A , Shepard S , Peck D 
Published: July 2008

Development of standards for performance verification of digital radiographic systems has been outpaced by commercialization of the technology. Practitioners, operators, vendors, and medical physicists are uncertain as to how to configure, calibrate, and verify systems for the best diagnostic quality images at the lowest practical radiation exposure to the patient. Several manufacturers have fielded commercial systems for performance verification, but long term data indicating trends and action limits is rare. Sources of definitive information on practical clinical methods are also scarce in the scientific literature. This has led to the current situation where clinical imaging operations are routinely conducted using systems that are not optimized.This workshop will discuss four important aspects of traditional medical physics practice that are modified when dealing with DR systems. These processes are of primary importance to medical physicists, because they affect both the quality of DR images and the ionizing radiation dose to patients. First, we consider how to perform an inspection on a radiographic unit using CR or DR. Second, we discuss how to set up the AEC for CR or DR exams. Third, we address how to verify the exposure indicator for CR and DR. Last, we discuss available phantoms and their characteristics and limitations when used with DR.Educational Objectives:1. Understand how some conventional tests should be modified for a digital radiographic system that is integrated into an electronic image management system.2. Identify key references and standards that can be useful in testing of DR.3. Appreciate opportunities for improvement of testing of DR systems.
TU‐D‐AUD C‐08: Experimental Proof and Feasibility of a 3D Real‐Time Detector System for Therapeutic Proton Beams
Authors: Archambault L , Beddar S , Sahoo N , Poenisch F , Gillin M , Mohan R 
Published: July 2008

Purpose: Fast and accurate quality assurance of complex proton treatments is challenging, but could be achieved with a real‐time three‐dimensional detector system. The purpose of this work is to study the feasibility and to provide experimental proof of principle that a liquid scintillator (LS) dosimetry system can measure three‐dimensional dose distributions of proton beams in real‐time. Method and Materials: A plastic tank of 7×7×16 cm3 filled with BC‐531 liquid scintillator was irradiated with a proton pristine Bragg peak. Scintillation light produced during the irradiation was measured with a cooled CCD camera. Acquisition rates of 20 or 10 frames per seconds (fps) were used to image consecutive frame sequences. These measurements were then compared to ion chamber measurements and Monte Carlo simulations. Results: We have shown that real‐time image acquisition was possible with our current dosimetry system design. A field of view of 12×9 cm2 was measured with the CCD camera. Dose measured from the images acquired at rates of 20 fps and 10 fps have a standard deviation of 1.1% and 0.7% respectively in the plateau region of the Bragg peak. Differences were seen between the raw LS signal and the ion chamber due to quenching effects of the LS. We have shown that this effect can be accounted for and corrected by Monte Carlo simulations. Once corrected, dose distributions measured with the LS were in good agreement with ion chamber measurements. Conclusion: We have shown the feasibility of using real‐time image acquisition to measure proton dose distributions in LS. Like many other detectors, LS response depends on the energy of the proton beam, but we have shown that Monte Carlo can be used to correct LS measurements. Such system would be extremely useful for fast and accurate quality assurance of proton treatments.Supported by the NCI (1R01CA120198‐01A2).
TU‐E‐350‐02: Medical Imaging Physics: Career Opportunities and Considerations
Authors: Barnes G 
Published: July 2008

Medical imaging is big business — a multibillion dollar business, both for the manufacturers of medical imaging equipment and for health care providors. It plays an important role in diagnosis and in therapy. For these and other reasons medical imaging physics offers a number of highly satisfying career paths — clinical support in small to moderate sized medical centers, consulting clinical support, academic radiology positions, and positions in industry. For the past 30+ years, the career path an individual takes depends primarily on individual interest and capabilities and to a lesser extent on job availability. This session focuses on the responsibilities of the medical imaging physicist in these different career paths. Presented are examples of different career paths. Discussed are the responsibilities that medical physicists typically have, minimum educational and experience requirements, and desirable skills and credentials.This Meet the Expert Session will be moderated by Gary T. Barnes, Ph.D. Dr. Barnes has worked for more than thirty‐five years in medical imaging. His experience encompasses routine clinical support, radiology resident teaching, mentoring of young medical physicists, research, and prototype medical imaging equipment development. He is a Fellow of the AAPM, ACR and AIMBE, and is currently Professor Emeritus, Department of Radiology, UAB Medical Center, President of RAD Physics, Inc., a medical physics consulting company he founded in 1978, and President of X‐Ray Imaging Innovations, a technology development company he founded in 1998. For the fifteen years prior to his retirement from UAB and becoming Professor Emeritus, he was the Director of the Physics and Engineering Division of the Department of Radiology. The Division included medical physics faculty, medical imaging equipment service engineers, QC technologists and technicians, computer programmers, and other informatics specialists. At the time of his retirement the Division had twenty‐five members. Dr. Barnes is the author or coauthor of 100+ peer reviewed papers and has several issued U.S. patents.
WE‐E‐351‐04: Image Registration Solves Everything?
Authors: Kessler M 
Published: July 2008

Ongoing developments in multimodality and 4D imaging technologies are providing opportunities to improve tumor localization and patient modeling for treatment planning and to reduce setup variations and margins for treatment delivery. Proper use of these technologies can provide greater sparing of dose to normal tissues and permit safer escalation of tumor doses. Imaging techniques are also being developed to help predict response to treatment during therapy when interventions and adaptations are still possible. The explosive growth in imaging technology has been followed by growth of technologies to manage the plethora of data now being generated.One key technology is image registration, which can be thought of as producing the “glue” to allow quantitative integration of anatomic, functional and dosimetric information from different imaging studies. Indeed, most modern imaging workstations, treatment planning systems and even treatment delivery systems now provide tools for image registration and data integration. These tools range in sophistication from simple manual registration involving just a few degrees of freedom (DOF) to fully automated registration techniques which produce deformation fields at the resolution of the image data.Unfortunately, widespread use of these techniques for all but simple situations has been impeded by lack of more realistic models which can accommodate processes other than just deformations such as tissue loss, gain, and sliding. The situation is also complicated by the realization that the optimal number of DOF and other registration parameters vary from tissue to tissue; using too few or too many DOF or inappropriate parameters can cause inaccurate or unphysical results. Fortunately, developments aimed at overcoming these obstacles continue to emerge and will pave the way to broader clinical use of image registration. These developments as well some of the remaining hurdles will be discussed.
TH‐SAMS‐AUD B‐05: Image Guided Radiation Therapy
Authors: Galvin J 
Published: July 2008

Paralleling the introduction of Intensity Modulated Radiation Therapy (IMRT) in the early to mid 1990s, Image Guided Radiation Therapy (IGRT) is an important but problematic new tool for the Radiation Oncology community. The challenges are similar to IMRT in that IGRT is a general classification that covers many different approaches with numerous variations. Where IMRT ranges from tomotherapy to conventional MLC‐based dose delivery with many different treatment planning approaches, IGRT covers a broad spectrum of techniques ranging from stereoscopic x‐ray imaging with rigid‐body registration to cone‐beam volume imaging with deformable fusion. Thus, like IMRT, guaranteeing the safe and effective use of this new modality in a clinical trial setting will require procedures that are as comprehensive as the ones currently in place for IMRT. As quality assurance techniques are developed for routine clinical use of advanced technologies like IGRT, they must also be appropriately standardized for use in clinical trials.Image Guidance is defined for RTOG protocols as the steps of obtaining daily in‐room images of the patient in the treatment position, matching or fusing these images to the ones obtained during the treatment planning process, and the procedure of manually or automatically changing the patient's position based on the fusion information. Notice that this description does not consider the patient as a deformable object. Using this simple definition, a number of Radiation Therapy Oncology Group (RTOG) protocols have employed or are including IGRT for targeting the lesion. These protocols require institutions to verify their IGRT methodology through a credentialing process. This process is aimed at guaranteeing that each institution understands the protocol requirements relative to the use of their fusion software or manual shift capabilities.This presentation will discuss the process used by the RTOG to credential institutions for the use of IGRT in their protocols, and it will present the problems and considerations that will have to be addressed as the above definition of IGRT expands to include moving targets and deformable fusion.
TH‐C‐352‐01: Clinical Introduction and Use of LDR — the Anatomy of a Prostate Case
Authors: Miller R 
Published: July 2008

The development of Brachytherapy began with the purification of radium by Pierre and Marie Curie. Due to early clinical practice using Radium, several systems were developed to standardize the practice as well as the emergence of different techniques: intracavitary, interstitial, intraluminal and surface molds. Brachytherapy implants can also be temporary or permanent. A list of resources will be provided covering the basics and history of Brachytherapy but the focus of this aspect of the workshop will be on the most common current utilization: permanent prostate seed implants. Topics to be covered include: isotope characteristics and selection, pre‐planning and ordering, receipt and activity verification, implantation, and post‐planning.Educational Objectives:1. Review the isotopes in vogue, prescription ranges, and common clinical characteristics.2. Review isotope manufacturing and calibration issues.3. Review of current implant techniques (Mick ® versus stranded).4. Review radiation safety and release criteria.5. Review post plans and lessons learned the hard way.
Fire history of pinyon–juniper woodlands at upper ecotones with ponderosa pine forests in Arizona and New Mexico
Authors: Huffman David W. , Fulé Peter Z. , Pearson Kristen M. , Crouse Joseph E. 
Published: July 2008

We used maps of fire evidence, fire scar dendrochronology, forest age-structure analysis, and landscape analysis to investigate fire history at pinyon pine ( Pinus edulis Engelm.) – juniper ( Juniperus osteosperma (Torr.) Little, Juniperus scopulorum Sarg.) woodland – ponderosa pine ( Pinus ponderosa P. & C. Lawson) forest ecotones in Arizona (Tusayan) and in New Mexico (Canjilon). Results showed that charred trees were not evenly distributed across vegetative communities but were significantly (p < 0.001) more abundant than expected in ponderosa pine communities. Composite fire scar analysis indicated that surface fires occurred in ponderosa pine stands at both sites and burned at intervals of 7.2–11.1 years (WMPI; Weibull median probability interval). At Tusayan, landscape structure was fine grained, and maximum pinyon age was >200 years across 80% of the site. At Canjilon, landscape pattern was relatively coarse, and most pinyon patches were 200–300 years old. Cumulative standing age distributions suggested pinyon–juniper fire rotations of 340 and 290 years at Tusayan and Canjilon, respectively. We concluded the following: (i) surface fires in ponderosa pine stands did not spread through pinyon–juniper communities at either site, (ii) fire evidence was prevalent across both sites, but old pinyon trees indicated that no widespread lethal fires had occurred in the last 300–400 years, and (iii) structurally heterogeneous landscapes suggested that historical pinyon–juniper fires were of limited extent but lethal in patches.
Seventy-seven years of natural disturbances in a mountain forest area — the influence of storm, snow, and insect damage analysed with a long-term time series
Authors: Hanewinkel Marc , Breidenbach Johannes , Neeff Till , Kublin Edgar 
Published: July 2008

We investigated the effects of site properties, forest structure, and time on snow breakage, insect outbreaks, windthrow, and total damage for predominantly planted forests. A time series of forest damage in southwestern Germany spanning 77 years, from 1925 to 2001, was available along with a database on site properties and forest structure. The statistical modeling procedure successively addressed (i) probability of damage occurrence, (ii) timber loss in damaging events, and (iii) interaction among damage agents over time. Logistic and linear regressions were combined with multivariate autoregressive techniques. Natural disturbances were responsible for a total timber loss of 3.0 m3· ha–1· year–1. The distribution of the timber loss values over the years and over sites and stands with different properties was modeled with a standard error of 6.7 m3· ha–1· year–1. Disturbances are more likely to occur in previously damaged stands. Storm events typically provoke subsequent insect outbreaks between 2 and 6 years later. Large windthrow and snow breakage events tend to occur periodically, once every 10th, 11th, or 15th year. Analysis of disturbances as a time series significantly enhances understanding of forest risk processes.