bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐FF‐T‐419: Time‐Delay Measurement of Phase Gated Treatment with a Varian Real‐Time Position Management System Using Computed Radiography
Authors: Hanna G , Wang Z , Yin F 
Published: July 2007

Purpose: Respiratory gating systems have been developed to reduce treatment uncertainty caused by organ motions related to respiration. The time‐delay between when the gating signal is triggered and when the beam is turned on may affect the actual radiation dose delivered. In this study, we investigated the time delay for a Varian real‐time position management(RPM) respiratory gating treatment system. Method and Materials: A motion phantom with infrared markers was used to simulate a respiration signal. An infrared camera was used to observe the motion of the phantom and to plot out a true position‐versus‐time curve. Computed Radiography(CR) images were taken with a Varian Clinac 21EX at 10% intervals of the simulated breathing cycle with a 2% window. On the CR images, the center of a metal disk attached to the phantom was used to determine the measured position of the phantom relative to a stationary disk. The RPM curve and measured curve were both normalized. The RPM curve was then shifted relative to the measured curve in steps of 0.1milliseconds, and correlation between the two curves was measured at each step using MATLAB. The time shift corresponding to the greatest correlation was defined as the time delay in the gating system. Results: Three measurements were taken and averaged (arithmetic mean) to determine the time delay. The time‐delay between the actual beam‐on time and the RPM beam‐on triggering signal was found to be 0.513 ± 0.077 seconds. Conclusion: This work shows that there is a significant time delay between the gating signal and the time when the radiation beam is turned on for the Varian RPM system. The effect of time‐delay on patient dosimetry needs to be further investigated.Partially supported by Varian research grant.
SU‐FF‐T‐450: Verification of the Source Tech Medical Model STM1251 I‐125 Air‐Kerma Strength in Japan
Authors: Sumida I , Takahashi Y , Ito A , Nose T , Oguchi M , Yamashita T , Yoshioka Y , Inoue T 
Published: July 2007

Purpose: We investigated the difference between measured and manufacturer's stated source strength in a single model STM1251 seeds. Materials and methods: A well‐type ionization chamber with a single seed holder was used to measure source strength of 2412 seeds before implant for 34 patients. The air kerma strength was only used 0.279 U for all cases. The mean source strength used for each patient was compared with the manufacture stated value. The deviation from the measured value was compared with the tolerance level of the recommendation of AAPM TG‐40. Results: Measured source strength was higher than the manufacture stated value with median 1 % (range: −2 to 5 %). Sixteen seeds of all 2412 seeds (0.7 %) were over 5 % different from the manufacture stated value. Standard deviation from the mean value was median 2.2 % (range: 1.1 to 2.5 %) in each patient. Conclusion: A single seed assay was performed for the model STM1251 seed. The manufacture stated strength of the seeds agreed well with the measured value. Our study was helpful as guidance for the permanent prostate implant using the model STM1251 seed.(Submission Withdrawn).
SU‐GG‐AUD‐02: Dose Convolution Filter: Incorporating Spatial Dose Information Into Tissue Response Modeling
Authors: Huang Y , Joiner M , Liao Y , Burmeister J 
Published: July 2007

Purpose: Radiotherapy treatment planning commonly involves analysis of static dose distributions and corresponding Dose Volume Histograms (DVHs). However, such analysis does not account for biological effects of spatial variations in the physical dose distribution. We introduce the Dose Convolution Filter (DCF) model capable of incorporating spatial dose information in plan analysis and optimization, and integrating biological factors such as cell migration and bystander effects into physical dose distributions. The DCF model should allow more accurate prediction of tissue response from complex radiotherapy dose distributions, and can facilitate modeling of the effects of patient motion. Method and Materials: We use a Gaussian convolution filter with standard deviation, σ, determining the degree of dose washout. To test this model, filtered dose distributions are applied to a NTCP model to calculate tissue response. As an illustration, we determine σ from existing rat spinal cord data, and compare model‐predicted NTCP with published data. We also simulate the GRID technique, in which an open field is collimated into many pencil beams. Results: After applying DCF, an NTCP model can predict dependence of tissue response on variations in spatial dose distribution. The model successfully fits the rat spinal cord data with a predicted value of σ=2.6±0.5mm, consistent with 2mm migration distances of remyelinating cells. Moreover, it enables the correct prediction of a high relative seriality for spinal cord. Finally, this model also predicts the sparing of normal tissues by the GRID technique when the size of each pencil beam becomes comparable to σ. Conclusion: The DCF model incorporates spatial dose information and offers an improved way to estimate tissue response from complex radiotherapy dose distributions. It does not alter the prediction of tissue response in large homogenous fields, but successfully predicts increased tissue tolerance in small or highly non‐uniform fields.Partially supported by Varian Medical Systems.
MO‐A‐M100J‐01: Performance Evaluation for New‐Technology CT Systems: Dual‐Source CT
Authors: McCollough C 
Published: July 2007

Computed tomography technology continues to evolve at a rapid pace. Recent technical innovations include dual‐source CT, 256‐channel CT, and cone‐beam angio CT. In this three‐part presentation, the impact of each of these new technologies on physics performance evaluations will be considered.In dual‐source CT, two x‐ray tubes and two data acquisition systems are mounted orthogonally on the same rotating gantry. The system can be used to perform traditional CT scanning similar to the 64‐slice system from the same manufacturer; 85 msec temporal resolution imaging; dual source 160 kW imaging; or dual‐energy imaging. The impact of two x‐ray sources and two data acquisition systems on performance testing is relatively straightforward, as the doses from each source are simply additive. However, to assess image quality in a clinically‐relevant manner, a proper understanding of typical scan parameters is essential. Several suggested scan modes for image quality evaluation will be presented.Educational Objectives:1. Understand the design of dual‐source CT.2. Recognize typical dose values from each source and from clinical protocols.3. Gain familiarity with the image quality associated with typical clinical scan protocols.
MO‐A‐BRB‐01: Review of the AAPM TG‐105
Authors: Chetty IJ , Rogers DWO 
Published: July 2007

Despite its proven accuracy and the potential for improved dose distributions to influence treatment outcomes, the long calculation times previously associated with MC simulation rendered this method impractical for routine clinical treatment planning. However, the development of faster codes optimized for radiotherapy calculations and improvements in computer processor technology have substantially reduced calculation times to, in some instances, within minutes on a single processor. Several commercial vendors have released or are currently in the process of releasing MC algorithms for photon and/or electron beam treatment planning. Consequently, the accessibility and use of MC treatment planning algorithms may well become widespread in the radiotherapy community. With MC simulation, dose is computed stochastically using first principles; this method is therefore quite different from conventional dose algorithms. Issues such as statistical uncertainties, the use of variance reduction techniques, the ability to account for geometric details in the accelerator treatment head simulation, and other features, are all unique components of a MC treatment planning algorithm. Successful implementation by the clinical physicist of such a system will require an understanding of the basic principles of MC techniques.Educational Objectives:1. To provide an educational review of the physics of the MC including discussion of methods used for coupled photon and electron transport.2. To review the methods used to improve the simulation efficiency.3. To briefly review the vendor transport codes currently used for clinical treatment planning.4. To describe the development of treatment head models for clinical treatment planning.5. To discuss the factors associated with MC dose calculation within the patient‐specific geometry, such as statistical uncertainties, CT‐to‐material, and reporting of dose‐to‐medium versus dose‐to‐water.6. To discuss the issues associated with experimental verification of MC algorithms.7. To briefly review the potential clinical implications of MC calculated dose distributions.8. To provide example timing comparisons of the major vendor MC codes in the clinical setting.
MO‐B‐BRB‐01: Quality Assurance in Stereotactic Radiosurgery and Fractionated Stereotactic Radiotherapy
Authors: Solberg T , Shepard D 
Published: July 2007

Stereotactic radiosurgery involves the high‐dose irradiation of cranial neoplasms delivered in a single fraction. In the 50‐plus years since it was first introduced, stereotactic radiosurgery has become a standard of care in the treatment of brain tumors, vascular malformations, functional disorders, and pain. Modern radiosurgery can be performed non‐invasively and on an outpatient basis while maintaining an extremely high degree of accuracy. Within the past ten years, the field of radiosurgery has seen numerous technological enhancements including: (1) the development of dedicated devices for stereotactic delivery; (2) the use of relocatable frames to facilitate fractionated delivery; (3) the development of “frameless” approaches; and (4) the application to extracranial tumor sites. Each of these developments has been accompanied by its own challenges in assuring targeting and dosimetric accuracy. In this presentation we review the technologies for stereotactic localization and treatment of cranial targets with particular emphasis on the quality assurance aspects associated with establishing and maintaining a clinical radiosurgery program. Specifically, the presentation will:1. Differentiate the technologies used in the delivery of stereotactic radiosurgery including linac‐based techniques (both conventional and robotic) and the Gamma Knife.2. Define the treatment planning parameters for linac‐based and Gamma Knife stereotactic radiosurgery.3. Discuss measures for assuring accuracy in stereotactic localization and dose.4. delivery for linac‐based and Gamma Knife stereotactic radiosurgery.
MO‐B‐M100F‐01: Proton Therapy
Authors: Smith A , Paganetti H 
Published: July 2007

The clinical advantages of proton beams have become widely recognized and there has recently been a significant increase in interest for building additional proton therapy facilities. There are currently over 25 institutions worldwide treating patients with proton beams and over 50,000 patients have been treated. There are at least 25 new facilities in various stages of planning and building. However, the fraction of patients treated with protons each year remains extremely small compared to the total number of cancer patients treated with external beam photons and electrons. The advantage of proton beams lies primarily in their excellent dose localization as compared to that which can be achieved using photon beams. Due to the Bragg peak characteristic in the depth dose of proton beams, the integral dose from proton therapy is significantly less that that for photon treatments. This allows higher doses to be delivered to target volumes, resulting in increased rates of local control, and lower doses delivered to critical normal tissues, resulting in decreased rates of treatment‐related morbidity. There are many challenges associated with increasing the accessibility of proton therapy not the least of which is the very limited number of clinical staff with knowledge of and training in proton therapy. The aim of the present Continuing Education Course is to provide a basic understanding of the rationale for proton therapy, physics of proton beams, technology of proton beam acceleration and transport, delivery of proton treatments, proton treatment planning and clinical results of proton therapy.Educational Objectives:Understanding of1. the physical characteristics of proton beams and interactions in tissues.2. the beam production and treatment delivery technology for proton beams.3. the clinical commissioning of proton therapy beams.4. the basic principles of proton treatment planning.5. the clinical results for proton therapy.
MO‐C‐AUD‐00 — Introduction: Imaging as a Biomarker for Therapy Response: Challenges, Opportunities and Initiatives
Authors: Martel M 
Published: July 2007

Imaging as a biomarker for drug and radiation therapy response is emerging as an important area of research. The NIH has formed a Biomarker Consortium with several agencies of the federal government to leverage NIH and Industry funds to develop standardized methods for biomarkers that includes imaging. (http://www.fnih.org/Biomarkers%20Consortium/Biomarkers_home.shtml). The role of imaging as a biomarker poses unique problems for image data collection and analysis across different imaging platforms, as the latter factors increase the uncertainty of measurement, (NIST workshop agenda and report: http://usms.nist.gov/workshops/bioimaging.htm). The AAPM is well positioned to address standards for quality assurance for all imaging modalities, system performance, and development of clinical decision software tools for measurement of therapy response. For this symposium, speakers will discuss:The role of imaging as a biomarker,• Potential funding sources for this work at NIH,• Leveraging therapy physics experience in the NCI's Radiation Research Program that can be applied to measurement of therapy response,• How imaging societies can become engaged to address this research area and promote more standardized methods for biomedical imaging.
MO‐C‐AUD‐01: The Emerging Role of Imaging as a Biomarker for Response
Authors: Schwartz L 
Published: July 2007

Radiologic images have been used for decades to gauge the effectiveness of various therapeutic interventions. In the areas of oncology, cardiovascular disease, neurology, arthritis and osteoporosis, imaging plays a vital role in decision‐making regarding effectiveness of therapy for an individual patient. Increasingly, imaging also is being utilized during all phases of therapeutic drug development, resulting in speedier drug discovery and, in some instances, ensuring a drug's safety. There has been an “explosion” in use of biomarkers in drug discovery.Many imaging biomarkers are already commonly used, such as tumor size measured at CT to reflect tumor burden;T2 hyperintensity detected at brain MR imaging to reflect the burden of Multiple Sclerosis; and bone density determined on DEXA studies to assess for osteoporosis. One major benefit of imaging biomarkers is that the findings may be evaluated and quantified for changes over time. Newer imaging biomarkers are also playing a role in drug discovery, such as dynamic contrast‐enhanced MR imaging to assess for vascular flow to tumors; molecular imaging in the functional evaluation of cardiac, neurologic and oncologic processes; and CT in the evaluation of cystic fibrosis. The complexity in imaging biomarkers is increasing not only with the number of available imaging and post‐processing techniques, but perhaps even more importantly, with the number and diversity of relevant therapeutic options. Increasingly, it is becoming clear that a single imaging biomarker alone can be misleading, but is more robust when combined with other imaging or non‐imaging biomarkers.Educational Objectives:1. Understand the role of imaging in monitoring response to therapy.2. Understand the issues and clinical complications of imaging as a biomarker.Research sponsored by Astrazeneca Corporation.
MO‐C‐AUD‐02: NIH Roadmap, Public‐Private Partnerships and the Biomarker Consortium
Authors: Mittleman B 
Published: July 2007

The NIH mission to improve the public health through biomedical research is accomplished by conducting research directly, by funding research through grants and contracts, and increasingly by partnering with any of a wide variety of partners. The synergy possible through the leveraging of NIH resources with the strengths of industry, professional societies, patient advocacy organizations, and academic partners allows work that cannot be done by any of the partners alone to be realized. Moreover, partnership can also facilitate increasing efficiency, economy and speed for translation of discovery to the public. The NIH Roadmap Public‐Private Partnership(PPP) Program is the center of such activities and policies, programs and examples of PPPs will be described, as well as ways that AAPM might take advantage of partnership opportunities.Educational Objectives:1. Understand the NIH position on PPPs.2. Understand PPP Policies and programs.3. Learn how new PPPs can be initiated.
MO‐D‐L100J‐03: Generating Patient Specific Motion Models Using a Navigator Channel and a Liver Population FEM Motion Model
Authors: Nguyen T‐N , Moseley JL , Dawson LA , Jaffray DA , Brock KK 
Published: July 2007

Purpose: To incorporate motion information from 2D images using a navigator technique and finite element modelling (FEM) to generate patient‐specific 3D motion models. Methods and Materials: A liver population motion model was created using a FEM deformable image registration platform, MORFEUS, to simulate liver deformation during a respiratory cycle. Twenty patient's exhale livers were constructed from the population FEM, and then deformed into their inhale livers. Deformation maps were generated and used to check the accuracy of the navigator‐updated patient models. NavigatorView, an in‐house algorithm, places a rectangular navigator channel to define a region of interest on 2D image slices. An operator chosen navigator is placed on the exhale image while another navigator is automatically placed at the corresponding location on the inhale image. Motion was calculated as the shift required to align the intensity profiles within the channels at the superior dome and the inferior tip of the liver on coronal CT slices and simulated radiograph images. The navigator shifts were used in a weighting equation to generate patient‐specific motion models from the population motion model. Results: The average accuracy ± standard deviation of the navigator channel at the superior and inferior edges is 0.12 ± 0.12cm and 0.25 ± 0.25cm respectively. The navigator‐updated patient‐specific models was 100% and 80% successful of the 20 patient cases for the coronal CT and simulated radiograph slices respectively, where a successful case achieved an accuracy error less than the image voxel size (0.25cm). Conclusions: The navigator technique allows for updating patient‐specific 3D motion models from a liver population model using more easily acquired 2D images. This can be a useful tool for image‐guided therapeutics, such as intra‐fraction image guided tracking during radiotherapy, where 2D data may be more rapidly acquired.Research sponsored by the National Cancer Institute of Canada — Terry Fox Foundation.
MO‐D‐L100F‐01: Progress in In‐Line Phase‐Sensitive X‐Ray Imaging
Authors: Wu X , Liu H 
Published: July 2007

Since Roentgen discovered x‐ray and performed the first x‐ray imaging over 100 years ago, x‐ray medical imaging has always been based on the biological tissue's differences in x‐ray attenuation. However, x‐ray‐tissue interaction causes x‐ray phase shifts as well, and the phase‐shift differences between different tissues are about one thousand times larger than their attenuation differences. The phase‐sensitive x‐ray imaging hence allows not only visualizing the tissues with very low attenuation‐contrast, but also quantifying tissue's projected electron densities by means of the phase retrieval.In this presentation we first introduce the concept of spatial coherence of x‐ray wavefield and elucidate the mechanism of x‐ray phase‐contrast formation. We then review recent progress in the in‐line phase—sensitive x‐ray imaging.Educational Objective:1. Elucidate the mechanism of x‐ray phase—contrast formation.2. Review recent progress in the in‐line phase—sensitive x‐ray imaging.
MO‐D‐L100F‐03: New Developments in Digital Breast Tomosynthesis
Authors: Lo JY , Singh S , Dobbins JT , Samei E 
Published: July 2007

Digital tomosynthesis is one of the most exciting recent developments in breast imaging. By modifying existing full field digital mammography systems, one can achieve this type of limited‐angle cone‐beam CT imaging which produces 3D slice images of the breast. Overlapping dense tissue in mammography is one of the most common causes for unnecessary callbacks as well as missed cancers. Since the 3D images remove such overlapping tissue, breast tomosynthesis can improve radiologists' specificity by obviating unnecessary callbacks. It can also improve sensitivity by allowing easier detection and characterization of breast cancers which might otherwise be obscured. Most remarkably, tomosynthesis can achieve all this with a scan that is comparable to the speed, resolution, cost, and dose of conventional mammography. For these reasons, tomosynthesis stands poised as the only imaging technique with the potential to completely replace the current role of mammography as the primary tool in breast cancer screening and diagnosis.This presentation will cover both the hype and hope surrounding breast tomosynthesis. From a medical physics perspective, the latest results will be reviewed from recent studies to optimize radiographic techniques, acquisition modes, and reconstruction algorithms. In addition, emerging results will be surveyed from advanced applications including display/visualization, computer aided detection, and contrast enhanced tomosynthesis. Finally, the clinical promise and risks of this new technology will be discussed using initial clinical trial results.This research was supported in part by research grants from Siemens Medical Solutions, US Army Breast Cancer Research Program, and NIH/NCI.Educational Objectives:1. Understand the difference between breast tomosynthesis and dedicated breast CT.2. Appreciate the many medical physics issues involved in the development and optimization of breast tomosynthesis.3. Understand the clinical promise and concerns of using breast tomosynthesis.