bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐C‐AUD A‐03: Integrating Research Into the Clinic: Experiences From Implementing Monte Carlo, IMRT, and …
Authors: Siebers J 
Published: July 2008

Clinical implementation of in‐house developed research‐based treatment planning software poses challenges for both the code developer and clinical user. This presentation will examine several important aspects of the implementation and integration processes, using in‐house IMRT and Monte Carlo dose calculation programs as examples. For the developer, challenges include the creation of fail‐safe strategies to ensure patient safety; procedures for upgrades, including updates to address clinically urgent issues; and separation of clinically implemented code from research‐based code which is under continual development. Jointly, the developer and clinical user are challenged to create commissioning and quality assurance procedures which not only meet AAPM TG guidelines, but which also test pre‐identified failure modes that are specific to the software implementation. They are further challenged to develop per‐patient quality assurance methods to ensure patient plan quality. A useful strategy in this regard is to utilize a vendor‐supplied FDA approved product for cross‐comparison of the deliverable treatment plan. Finally, the implementation and integration processes should include procedures for dealing with non‐compliant cases, including identifying error sources and implementing remedies. Examples of how these challenges were addressed in our clinic will be presented.Educational Objectives:1. To appreciate the challenges inherent in safe clinical implementation of in‐house developed software.2. To understand strategies for overcoming the challenges of clinical implementation of in‐house developed software.3. To understand the role of QA in safe clinical implementation of research.The research discussed is supported in part by NCI Grant P01 CA116602, by Philips Medical Systems, and by Varian Medical Systems.
TU‐FF‐A4‐02: Evaluation of the Performance of the Fast Scanning Platform of An OCT System
Authors: Heard M , Ibbott G 
Published: July 2008

Purpose: To ensure that the performance of a fast optical computed tomography (OCT) scanning system is comparable with previous models. Method and Materials: MGS Research Inc. developed an OCT system based on the translate‐rotate method used by early generation x‐ray CT scanners. The performance of the system has been investigated and the system has been used in several published studies. Recently, a new OCT system was developed by MGS Research Inc. that reduces scan times by a factor of 10 or more. Several 3D dosimeters were irradiated using 6 MV photons and imaged on both version of the scanner. The image noise, reproducibility, and spatial accuracy were determined and used to evaluate the performance of the system. Results: The new version of the scanner reduced the scan time per plane from 7 minutes to 30 seconds. Preliminary results showed that noise levels in the images from both models were comparable. The uncertainty in the determination of the optical density values from images acquired with both models was ∼2%. The new model uses Fresnel lenses that may need adjustment prior to an imaging session which can affect the reproducibility of the system. Conclusion: The new version of the OCT scanner shows promise as a replacement for the previous version. Continued improvement in the software and hardware are needed to make the system as robust as the previous version.The investigation was supported by PHS grant CA 10953 awarded by the NCI, DHHS.
WE‐E‐332‐01: A Unified Dose Indicator for Tomographic Acquisition Modalities
Authors: Beldjoudi G , Yilmaz E , Bochud F , Pachoud M , Schmidt S , Verdun FR 
Published: July 2008

Introduction : Tomographic techniques in diagnostic and interventional radiology or radiation therapy are in rapid development. The CTDI formalism that is currently used to assess dose has been challenged by several groups. The goal of this contribution is to evaluate the required scan length to reach equilibrium on various MDCT units and estimate the average dose delivered within a slice when using systems having a cone beam geometry.Method and Materials: Measurements were performed on three MDCT systems (GEMS 8, 16 and 64‐row), a flat panel fluoroscopy (Allura — Philips), an IGRT (Synergy, Elekta ltd) and on a tomotherapy (TomoTherapy Inc.) using two standard CTDI phantoms and a home made phantoms (PMMA cylinder of 30 cm filled with water) with a conventional small volume ion chamber (0.6 cc Farmer type) and a standard pencil ion chamber. Dose profiles were also recorded at various positions within the slice to study the impact of scatter as a function of the distance between the centre of the phantom and its periphery. Results: For CT units a theoretical length of 400 mm is required to reach the equilibrium at the center of the phantom. The measurements show that a dose plateau was reached after 420 mm. The average dose within a slice measured in our home made phantom for standard abdominal CT (120 kV, 210 effective mAs) was 15.3 mGy; 26.9 mGy for the fluoro‐CT (122kV, 274mAs, 20.7s); 39.0mGy for IGRT system (120kV, 219mAs, 27×27cm2 field size, one rotation) and 13.1 mGy for the Tomotherapy system (3.5 MV, pitch=0.8). Conclusion: The CTDI concept should be replaced by a more generic methodology, such as the one presented here, that could be used in all cone beam geometries. Measurements should be done using water equivalent phantoms.
TH‐SAMS‐AUD B‐06: TG113: Improving Treatment Consistency and Data Quality for Clinical Trials
Authors: Moran J 
Published: July 2008

The goal of AAPM Task Group 113 is to provide guidance to physicists, QA centers, and others involved in clinical trials on methods to improve the consistency and quality of data generated for trials involving external beam radiotherapy.To date, there are no universally agreed upon standards for the physics practices related to clinical trials. In addition, as treatment techniques become more sophisticated, it is even more challenging to comprehensively maintain consistency across multiple institutions. Since hundreds of institutions may be involved in clinical trials, it is critical to review the entire treatment planning and delivery process and to identify areas where improvements can be made to ensure that high quality and consistent data are acquired from all institutions treating patients on clinical trials.This presentation will focus on factors that impact data quality for the treatment planning and delivery process. In addition, methods to help individual physicists improve the consistency of clinical trials will be discussed. The scope of the task group includes image acquisition for volume definition, treatment planning systems, patient localization, treatment guidance and delivery, and credentialing for clinical trials.Educational Objectives:1. To describe the goals of TG♯113 Physics Practice Standards for Clinical Trials.2. To highlight factors that directly impact clinical trials that involve IMRT and IGRT.
TH‐A‐351‐01: Digital Mammography: A Physician's Perspective
Authors: Parikh J 
Published: July 2008

Full field digital mammography (FFDM) represents an exciting new frontier in the evaluation of breast cancer. FFDM separates out the components of image acquisition, image processing and image displays compared to traditional film screen mammography, allowing a variety of potential technical advantages. In the ACRIN study FFDM had higher sensitivity for detecting breast cancer compared to SFM in premenopausal women, perimenopausal women, and women with dense breasts. Other reported advantages of digital mammography include higher contrast resolution, reduced noise, reduced need for repeat exposures and thus lower radiation dose to the patient, rapid soft‐copy display image interpretation, linkage to PACS systems and advanced applications such as CAD, telemammography and new‐modality imaging such as contrast‐enhanced digital mammography, dual‐energy subtraction mammography, stereo‐mammography, and tomosynthesis.Digital mammography has not yet received widespread clinical implementation because of several barriers. Significant costs associated with hardware purchase, additional equipment, professional retraining and development and technical support are incurred by facilities that choose digital mammography. Ongoing operational issues include comparing digital versus film screen images in the radiologist and technologist work stations. Lastly, while storage may be rapid, reliable and convenient with digital mammography, significant sizable amounts of computer memory will ultimately be required to store the amount of data required to maintain high‐quality images.Educational Objectives:1. To review the clinically relevant functional components of digital mammography.2. To introduce some of the advanced applications of digital mammography.3. To discuss some barriers to widespread clinical use of digital mammography.4. To review data from clinical trials rerarding digital mammography.Outline:(1) Why digital?(2) Functional components(3) Advanced applications(4) Barriers(5) Clinical trials
TH‐SAMS‐332‐04: Dose Descriptors and Dose Assessment for Cone Beam CT Scanners
Authors: Geleijns J 
Published: July 2008

Computed tomography (CT) dosimetry should be adapted to the fast developments in CT in order to keep pace with new CT technology. Recently, a cone beam CT scanner that challenges the existing Computed Tomography Dose Index (CTDI) dosimetry paradigm was introduced. The appropriateness of existing CT dose metrics for a cone beam CT scanner will be assessed and to new approaches for CT dosimetry will be suggested.Dose measurements with a small Farmer type ionization chamber, and 100mm and 300mm long pencil ionization chambers can be performed free‐in‐ air to characterize the cone beam. According to the most common dose metric in CT, measurements can also performed in 150mm and 350mm long CT head and CT body dose phantoms with 100mm and 300mm long ionization chambers. Effects that cannot be measured with ionization chambers can be explored with Carlo calculations of the dose distribution in 150mm, 350mm and 700mm long CT head and CT body phantoms. To overcome inconsistencies in the definition of CTDI, doses are also expressed as CT_Dose100 (CT_Dose100=160/100 × CTDI100). CTDI300 is chosen as the standard of reference against which results were assessed.Measurements free‐in‐air reveal good‐to‐excellent correspondence between CTDI300air and CT_Dose100air; CTDI100air substantially underestimates CTDI300air. Measurements in phantoms and Monte Carlo calculations reveal good correspondence between CTDI300w, CT_Dose100w and CTDI600w; CTDI100w substantially underestimates CTDI300w. Measurements at different positions within CT dose phantoms with an ionization chamber that is smaller than the cone beam are fundamentally different compared to readings of pencil chambers that are longer than the cone beam.CT_Dose100air and CT_Dose100w are pragmatic metrics for characterizing the dose of the cone beam CT scanner; these quantities can be measured with the widely available 100mm pencil ionization chambers and 150mm long CT dose phantoms. CTDI300air and CTDI300w in a 350mm long CT dose phantom can serve as appropriate standards of reference for characterizing the dose of the cone beam CT scanner. Simple geometrical considerations, supported by Monte Carlo calculations, explain the fundamental differences of different CT dose metrics when measurements are performed at different position within CT dose phantoms.
TH‐A‐352‐01: Management of Interventional Equipment and Suite: Cradle to Grave
Authors: Strauss K 
Published: July 2008

The life of any fluoroscopic imaging device begins with someone's perceived need for it and ends when it is removed from the clinical environment. In between these “cradle and grave” milestones, the machine must meet clinical objectives, provide good image quality, and reduce radiation dose to both patients and personnel. An organized program that achieves cost effective quality improvement is required to achieve these objectives. The first step identifies the clinical requirements of the imager. This identification assists obtaining adequate project funding. One acquires the imager by identifying the vendor of choice, negotiating the purchase, and issuing a purchase contract. One concurrently must plan an effective and efficient supporting facility followed by monitoring of the progress of renovation/construction. After the unit is installed, the owner acceptance tests the imager followed by staff training. A comprehensive program of routine testing, maintenance, repair, and record keeping to assure equipment performance begins after commissioning of the unit and continues throughout its life.Educational Objectives:1. Understand how to help physicians identify equipment attributes that address clinical needs.2. Understand important steps in selecting, negotiating, and contracting the purchase of imaging equipment.3. Understand basic elements of facility design necessary to support selected imaging equipment.4. Understand the objectives of acceptance testing of state‐of‐the‐art imaging equipment.5. Understand the basic elements of a program to assure equipment performance after its commissioning.
TH‐B‐350‐01: Scintillation Dosimetry: Review, New Innovations and Applications
Authors: Beddar S , Beaulieu L 
Published: July 2008

Interest in the development of plastic scintillation detector systems for dosimetry has been evolving for more than a decade. Plastic scintillation materials have many properties that make them ideal for dosimetry including water equivalence and energy independence for MV photons, linearity with dose, dose rate independence, and high spatial resolution. Therefore, these detectors do not require the usual conversion and/or correction factors used for other commonly used detectors to convert the dosimeter reading to absorbed dose. The only disadvantage of plastic scintillation detectors is the spurious effect arising from Cerenkov radiation produced in the optical fiber that guides the scintillation light, which has been solved. This evolution started with point detectors and is leading to matrix arrays to respond to the ever‐increasing complexity of radiotherapy treatment fields such as IMRT. Small fields, high dose gradients and other challenging conditions could soon require the development of commercial scintillation detectors. Moreover by using a photodetector with a large sensitive area (e.g. a CCD camera), arrays of several hundreds of scintillation detectors could be made to simplify long and arduous quality assurance tests.This lecture will provide an overview of the dosimetric characteristics and properties of plastic scintillation detectors when exposed to high‐energy photon, electron as well as proton beams. We will discuss both forms of plastic‐based scintillation detectors: plastic scintillators and plastic scintillating fibers. Finally, we will present few applications for plastic scintillation detectors in clinical radiotherapy: stereotactic radiosurgery, quality assurance and in vivo dosimetry applications.Educational Objectives:1. Review the underlying physics of scintillation materials.2. Review the properties of plastic scintillation materials used in radiation dosimetry.3. Understand the principles of this method and recent innovations in scintillation dosimetry.4. Identify applications that could benefit from scintillation detectors.
TH‐C‐351‐05: Quantitative Methods for Contouring On PET‐CT Images
Authors: Day E , Betler J , Mohammadi S , Reitz B , Parda D , Miften M 
Published: July 2008

Purpose: FDG‐PET imaging is routinely used to diagnose and stage cancer patients. It is also gaining wide acceptance as a tool to assist in tumor delineation in radiotherapy (RT) treatment planning. However, target volume definition is subject to inter‐observer variability. The objective of this study was to evaluate several existing auto‐contouring methods and develop a technique that would reduce inter‐observer variability. Method and Materials: Eighteen rectal and anal cancer patients who had undergone PET‐CT imaging and received RT were retrospectively reviewed. For each patient, a FDG‐PET avid (AVID) region was contoured by an experienced clinician without the use of the CT scan. The AVID volume was compared to volumes derived by the automated methods. Three automated methods were used: a fixed SUV cutoff of 2.5, previously suggested in the literature, a percentage of the maximum SUV (%SUVmax), and an in‐house derived mathematical technique. A 43% threshold was found for %SUVmax using a phantom study with cylinders of known volumes filled with varying concentrations of FDG. The mathematical approach generated 3D volumes using a Confidence Connected Region Growing (CCRG) technique that calculated the mean and standard deviation from pixel intensities contained in a 3D volume grown from a seed pixel. Results: The class solution of using a single value of SUV or a %SUVmax proved limited. These two methods depend on the correct threshold being applied and need to be different for each patient. The resulting volume differences ranged from 1%–129%. The CCRG based volumes were within 8% of the AVID volumes with a range of 1%–23%. Conclusion: Assuming that the same seed pixel is chosen, the CCRG method reduces inter‐observer contouring variability on FDG‐PET images and provides a viable clinical solution by always growing the same volume.Research partially supported by Siemens Medical Solutions.
TH‐C‐351‐08: Clustering Analysis of Dynamic 11C‐Methionine PET in GBM for RT Target Definition
Authors: Wang P , Piert M , Lee I , Gomez‐Hassan D , Junck L , Rogers L , Hayman J , Haken R Ten , Lawrence T , Tsien C , Cao Y 
Published: July 2008

Purpose: To assess tumor differentiation in patients with glioblastoma multiforme (GBM) using dynamic 11C‐methionin (D‐MET) PET and fuzzy c‐means (FCM) clustering analysis; and to evaluate the added value of D‐MET PET in radiation therapy (RT) target definition. Method and Materials: D‐MET PET images were obtained prior to RT in 25 patients with GBM. Each scan was composed of 15 phases acquired at 0–50 minutes following injection. Conventional MRI was also acquired before RT for target volume definition and after RT for evaluation of treatment outcome. D‐MET PET data were normalized to the mean uptake of each individual's cerebellum. Volume of interest (VOI) for the analysis was defined based on pre‐RT FLAIR‐MRI and extended to incorporate regions of high uptake of MET. Time‐activity curves of MET uptake in the VOI were classified using a FCM clustering algorithm with spatial constraints. The optimal number of clusters was determined for each dataset by calculating several clustering validity indices. The results of classification were reviewed by experts; and were also correlated to the patterns of local failure after RT. Results: Using the FCM clustering algorithm, time‐activity curves of MET uptake in the VOI were successfully partitioned into tumor, normal brain tissue, inflammation response, surgical cavity and edema. Heterogeneous MET uptake in the tumor was also differentiated. In 15 of the 25 patients who had tumor progression, the pre‐RT PET in the clusters correspondent to the locations of recurrence had a median uptake value of 1.47 (last dynamic phase), which involves clusters beyond the hottest ones. Conclusion: This study demonstrated that dynamic MET‐PET is capable of differentiating active tumors in patients with GBM. It is also promising in providing extra information for RT target definition.Supported by NIHP01CA59827.
TH‐C‐332‐04: Comparison of Residual Motion Artifacts in Phase‐ and Amplitude‐Binned Onboard 4D‐CBCT Imaging
Authors: Lu J , Zheng D , Keall P , Weiss E , Bartee C , Williamson J 
Published: July 2008

Purpose: Linac‐mounted 4D‐Cone Beam CT (CBCT) imaging is an important tool for extending 3D IGRT techniques to respiration‐motion synchronized treatments. Our purpose is to evaluate the effects of two different projection binning strategies, phase and amplitude binning, on the image quality of our recently implemented 4D‐CBCT method using the Varian On Board Imager and RPM breathing motion surrogate. Method and Materials: We have implemented a 4D‐CBCT imaging procedure that adjusts the acquisition parameters to the breathing period. The Varian Real‐time Position Management system (RPM) was used to establish temporal coincidence between the respiratory motion and the CBCT projections. Projections were sorted into 10 bins. For the projections sorted into each bin, the variances about the RPM amplitude and phase means was calculated and compared to the image quality achieved in CBCT images. 4D‐CBCTs were studied with a phantom with irregular motion cycle. The phantom consists of two acrylic spheres and one cylinder on a motion platform to mimic the superior‐inferior tumor motion mostly seen in patients. Results: With breathing cycle period variation but not amplitude variation, we found that amplitude binning produced fewer blurring artifacts which was consistent with RPM analysis which showed significantly smaller amplitude variances for amplitude binning. For the patient subjects, the RPM amplitude but not phase variances were smaller for amplitude binning. However, image quality was significantly better when phase binning was used. We hypothesize that the under‐sampling artifacts play a major role over the motion artifacts which is the case for amplitude binning, while the phase binning has a good control of aliasing artifacts. Residual motion artifacts persist in both phase‐ and amplitude‐ binning 4D‐CBCT images. Conclusion: Optimizing the binning strategies requires understanding complex interplay between residual motion in each bin and other factors such as uniformity and adequacy of angular sampling.Supported by PPG‐NIH‐P01116602.
TH‐C‐342‐04: An Update On Computer‐Aided Diagnosis (CAD): What's Next?
Authors: Jiang Y 
Published: July 2008

Computer‐aided diagnosis (CAD) has been developed over the last few decades as a tool to improve diagnostic performance. Computer‐aided detection (CADe) promises to help radiologists detect some of the subtle cancers that radiologists tend to miss. After CADe systems are introduced into clinical practice, many clinical studies have been conducted to assess the clinical effect of CADe. Like laboratory observer studies, many of these clinical studies show that CADe helps radiologists detect more cancers. However, all clinical studies do not agree. Controversies have recently arisen that question whether CADe helps radiologists detect more cancers or does more harm by increasing the number of false positives. While laboratory observer studies are very good at controlling biases, potential biases in clinical studies are far less understood and could fundamentally influence the clinical evaluation of CADe. In this presentation, we will review the concept of CAD and summarize laboratory and clinical studies of CADe. We will discuss the pros and cons of various types of clinical studies and discuss their limitations. We will identify some pitfalls that must be overcome to determine the true clinical effect of CAD.Educational Objectives:1. Understand the basic concept of CAD.2. Understand the common types of laboratory and clinical studies of CADe.3. Understand the limitations of current clinical studies.Conflict of Interest Statement: Y Jiang receives research funding from Hologic (Bedford, MA).