bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐EF‐204‐09: A Preliminary Method of Risk‐Informed Optimization of Tube Current Modulation for Dose Reduction in CT
Authors: Gao Y , Liu T , Li X , Liu B , Kalra M , Caracappa P , Xu X 
Published: June 2015

Purpose:X‐rays from CT scans can increase cancer risk to patients. Lifetime Attributable Risk of Cancer Incidence for adult patients has been investigated and shown to decrease as patient age. However, a new risk model shows an increasing risk trend for several radiosensitive organs for middle age patients. This study investigates the feasibility of a general method for optimizing tube current modulation (TCM) functions to minimize risk by reducing radiation dose to radiosensitive organs of patients.Methods:Organ‐based TCM has been investigated in literature for eye lens dose and breast dose. Adopting the concept in organ‐based TCM, this study seeks to find an optimized tube current for minimal total risk to breasts and lungs by reducing dose to these organs. The contributions of each CT view to organ dose are determined through simulations of CT scan view‐by‐view using a GPU‐based fast Monte Carlo code, ARCHER. A Linear Programming problem is established for tube current optimization, with Monte Carlo results as weighting factors at each view. A pre‐determined dose is used as upper dose boundary, and tube current of each view is optimized to minimize the total risk.Results:An optimized tube current is found to minimize the total risk of lungs and breasts: compared to fixed current, the risk is reduced by 13%, with breast dose reduced by 38% and lung dose reduced by 7%. The average tube current is maintained during optimization to maintain image quality. In addition, dose to other organs in chest region is slightly affected, with relative change in dose smaller than 10%.Conclusion:Optimized tube current plans can be generated to minimize cancer risk to lungs and breasts while maintaining image quality. In the future, various risk models and greater number of projections per rotation will be simulated on phantoms of different gender and age.National Institutes of Health R01EB015478
TU‐EF‐210‐03: Real‐Time Ablation Monitoring and Lesion Quantification Using Harmonic Motion Imaging
Authors: Konofagou E. 
Published: June 2015

The use of therapeutic ultrasound to provide targeted therapy is an active research area that has a broad application scope. The invited talks in this session will address currently implemented strategies and protocols for both hyperthermia and ablation applications using therapeutic ultrasound. The role of both ultrasound and MRI in the monitoring and assessment of these therapies will be explored in both pre‐clinical and clinical applications.Katherine Ferrara: High Intensity Focused Ultrasound, Drug Delivery, and ImmunotherapyRajiv Chopra: Translating Localized Doxorubicin Delivery to Pediatric Oncology using MRI‐guided HIFUElisa Konofagou: Real‐time Ablation Monitoring and Lesion Quantification using Harmonic Motion ImagingKeyvan Farahani: AAPM Task Groups in Interventional Ultrasound Imaging and TherapyLearning Objectives:

Understand the role of ultrasound in localized drug delivery and the effects of immunotherapy when used in conjunction with ultrasound therapy.
Understand potential targeted drug delivery clinical applications including pediatric oncology.
Understand the technical requirements for performing targeted drug delivery.
Understand how radiation‐force approaches can be used to both monitor and assess high intensity focused ultrasound ablation therapy.
Understand the role of AAPM task groups in ultrasound imaging and therapies.
Chopra: Funding from Cancer Prevention and Research Initiative of Texas (CPRIT), Award R1308 Evelyn and M.R. Hudson Foundation; Research Support from Research Contract with Philips Healthcare; COI are Co‐founder of FUS Instruments IncFerrara: Supported by NIH, UCDavis and California (CIRM and BHCE)Farahani: In‐kind research support from Philips Healthcare
TU‐G‐304‐01: Things You Wonâ€™t Be Able to Say Someone Should Have Told You
Authors: Sherouse G. 
Published: June 2015

As a new member of AAPM, it's easy to feel overwhelmed by the size and complexity of the association and to be unaware of the benefits and opportunities available to members. At this year's AAPM Annual Meeting in Anaheim, we will host a New Member Symposium where you can learn more about the organization, member resources, opportunities to get involved, and about topics of particular interest to new professionals. We encourage you to take advantage of this great opportunity to learn valuable information and to grow your professional network.Following an introduction from Dr. John Boone, AAPM President; Justin Keener, Chair of the New Professional Subcommittee, will speak on, “Physics of the AAPM.” Next will be Dr. George Sherouse sharing invaluable information in his talk, “Things You Won't Be Able to Say Somebody Should've Told You.”Registered attendees will receive a raffle ticket. Enter to win a complimentary registration for the 2016 Annual Meeting in Washington, DC!In addition, all new members who register for the Symposium will receive a drink ticket, good for one complimentary soda or beer served after the New Member Symposium during the social with Committee Chairs from 4 AAPM Councils: Science, Education, Professional and Administrative.Get your picture taken!!! There will be a photographer there to take pictures for your profile in the AAPM directory.The New Member Symposium is a (free) ticket option when you register online for the Annual Meeting. We hope that you will plan to attend.
TU‐G‐BRD‐02: Automated Systematic Quality Assurance Program for Radiation Oncology Information System Upgrades
Authors: Zhang B , Yi B , Eley J , Mutaf Y , Rahman S , D'souza W 
Published: June 2015

Purpose:To: (1) describe an independent, automated, systematic software‐based protocol for verifying clinical data accuracy/integrity for mitigation of data corruption/loss risks following radiation oncology information system (ROIS) upgrades; and (2) report on application of this approach in an academic/community practice environment.Methods:We propose a robust approach to perform quality assurance on the ROIS after an upgrade, targeting four data sources: (1) ROIS relational database; (2) ROIS DICOM interface; (3) ROIS treatment machine data configuration; and (4) ROIS‐generated clinical reports. We investigated the database schema for differences between pre‐/post‐upgrade states. Paired DICOM data streams for the same object (such as RT‐Plan/Treatment Record) were compared between pre‐/post‐upgrade states for data corruption. We examined machine configuration and related commissioning data files for changes and corruption. ROIS‐generated treatment appointment and treatment parameter reports were compared to ensure patient encounter and treatment plan accuracy. This protocol was supplemented by an end‐to‐end clinical workflow test to verify essential ROI functionality and integrity of components interfaced during patient care chain of activities. We describe the implementation of this protocol during a Varian ARIA system upgrade at our clinic.Results:We verified 1,638 data tables with 2.4 billion data records. For 222 under‐treatment patients, 605 DICOM RT plans and 13,480 DICOM treatment records retrieved from the ROIS DICOM interface were compared, with no differences in fractions, doses delivered, or treatment parameters. We identified 82 new data tables and 78 amended/deleted tables consistent with the upgrade. Reports for 5,073 patient encounters over a 2‐week horizon were compared and were identical to those before the upgrade. Content in 12,237 xml machine files was compared, with no differences identified.Conclusion:An independent QA/validation approach for ROIS upgrades was developed and implemented at our clinic. The success of this approach ensures a robust QA of ROIS upgrades without manual paper/electronic checks and associated intensive labor.
TU‐G‐BRA‐03: Predicting Radiation Therapy Induced Ventilation Changes Using 4DCT Jacobian Calculations
Authors: Patton T , Du K , Christensen G , Reinhardt J , Bayouth J 
Published: June 2015

Purpose:Longitudinal changes in lung ventilation following radiation therapy can be mapped using four‐dimensional computed tomography(4DCT) and image registration. This study aimed to predict ventilation changes caused by radiation therapy(RT) as a function of pre‐RT ventilation and delivered dose.Methods:4DCT images were acquired before and 3 months after radiation therapy for 13 subjects. Jacobian ventilation maps were calculated from the 4DCT images, warped to a common coordinate system, and a Jacobian ratio map was computed voxel‐by‐voxel as the ratio of post‐RT to pre‐RT Jacobian calculations. A leave‐one‐out method was used to build a response model for each subject: post‐RT to pre‐RT Jacobian ratio data and dose distributions of 12 subjects were applied to the subject's pre‐RT Jacobian map to predict the post‐RT Jacobian. The predicted Jacobian map was compared to the actual post‐RT Jacobian map to evaluate efficacy. Within this cohort, 8 subjects had repeat pre‐RT scans that were compared as a reference for no ventilation change. Maps were compared using gamma pass rate criteria of 2mm distance‐to‐agreement and 6% ventilation difference. Gamma pass rates were compared using paired t‐tests to determine significant differences. Further analysis masked non‐radiation induced changes by excluding voxels below specified dose thresholds.Results:Visual inspection demonstrates the predicted post‐RT ventilation map is similar to the actual map in magnitude and distribution. Quantitatively, the percentage of voxels in agreement when excluding voxels receiving below specified doses are: 74%/20Gy, 73%/10Gy, 73%/5Gy, and 71%/0Gy. By comparison, repeat scans produced 73% of voxels within the 6%/2mm criteria. The agreement of the actual post‐RT maps with the predicted maps was significantly better than agreement with pre‐RT maps (p<0.02).Conclusion:This work validates that significant changes to ventilation post‐RT can be predicted. The differences between the predicted and actual outcome are similar to differences between repeat scans with equivalent ventilation.This work was supported by NIH grant CA166703 and a Pilot Grant from University of Iowa Carver College of Medicine
TU‐G‐303‐00: Radiomics: Advances in the Use of Quantitative Imaging Used for Predictive Modeling
Authors: Deasy Joseph 
Published: June 2015

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan‐omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi‐modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics‐derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low‐grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image‐based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi‐modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma.Learning Objectives:

Understanding the basic image features that are often used in radiomic models.
Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability.
Understanding the methodology behind radiomic‐genomic (‘radiogenomics’) correlations.
Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)
TU‐G‐204‐01: BEST IN PHYSICS (IMAGING): Dynamic CT Myocardial Perfusion Measurement and Its Comparison to Fractional Flow Reserve
Authors: Ziemer B , Hubbard L , Groves E , Sadeghi B , Javan H , Lipinski J , Molloi S 
Published: June 2015

Purpose:To evaluate a first pass analysis (FPA) technique for CT perfusion measurement in a swine animal and its validation using fractional flow reserve (FFR) as a reference standard.Methods:Swine were placed under anesthesia and relevant physiologic parameters were continuously recorded. Intra‐coronary adenosine was administered to induce maximum hyperemia. A pressure wire was advanced distal to the first diagonal branch of the left anterior descending (LAD) artery for FFR measurements and a balloon dilation catheter was inserted over the pressure wire into the proximal LAD to create varying levels of stenosis. Images were acquired with a 320‐row wide volume CT scanner. Three main coronary perfusion beds were delineated in the myocardium using arteries extracted from CT angiography images using a minimum energy hypothesis. The integrated density in the perfusion bed was used to calculate perfusion using the FPA technique. The perfusion in the LAD bed over a range of stenosis severity was measured. The measured fractional perfusion was compared to FFR and linear regression was performed.Results:The measured fractional perfusion using the FPA technique (P_FPA) and FFR were related as P_FPA = 1.06FFR – 0.06 (r2 = 0.86). The perfusion measurements were calculated with only three to five total CT volume scans, which drastically reduces the radiation dose as compared with the existing techniques requiring 15–20 volume scans.Conclusion:The measured perfusion using the first pass analysis technique showed good correlation with FFR measurements as a reference standard. The technique for perfusion measurement can potentially make a substantial reduction in radiation dose as compared with the existing techniques.
TU‐G‐204‐02: Automatic Sclerotic Bone Metastases Detection in the Pelvic Region From Dual Energy CT
Authors: Fehr D , Schmidtlein C , Hwang S , Deasy J , Veeraraghavan H 
Published: June 2015

Purpose:To automatically detect sclerotic bone metastases in the pelvic region using dual energy computed tomography (DECT).Methods:We developed a two stage algorithm to automatically detect sclerotic bone metastases in the pelvis from DECT for patients with multiple bone metastatic lesions and with hip implants. The first stage consists of extracting the bone and marrow regions by using a support vector machine (SVM) classifier. We employed a novel representation of the DECT images using multi‐material decomposition, which represents each voxel as a mixture of different physical materials (e.g. bone+water+fat). Following the extraction of bone and marrow, in the second stage, a bi ‐histogram equalization method was employed to enhance the contrast to reveal the bone metastases. Next, meanshift segmentation was performed to separate the voxels by their intensity levels. Finally, shape‐based filtering was performed to extract the possible locations of the metastatic lesions using multiple shape criteria. We used the following shape parameters: area, eccentricity, major and minor axis, perimeter and skeleton.Results:A radiologist with several years of experience with DECT manually labeled 64 regions consisting of metastatic lesions from 10 different patients. However, the patients had many more metastasic lesions throughout the pelvis. Our method correctly identified 46 of the marked 64 regions (72%). In addition, our method also identified several other lesions, which can then be validated by the radiologist. The missed lesions were typically very large elongated regions consisting of several islands of very small (<4mm) lesions.Conclusion:We developed an algorithm to automatically detect sclerotic lesions in the pelvic region from DECT. Preliminary assessment shows that our algorithm generated lesions agreeing with the radiologist generated candidate regions. Furthermore, our method reveals additional lesions that can be inspected by the radiologist, thereby, reducing radiologist effort in identifying all the lesions with poor contrast from the DECT images.
TU‐G‐204‐07: A Research Pipeline to Simulate a Wide Range of CT Image Acquisition and Reconstruction Parameters and Assess the Performance of Quantitative Imaging and CAD Systems
Authors: Young S , Lo P , Kim G , Hoffman J , Brown M , McNitt‐Gray M 
Published: June 2015

Purpose:Quantitative Imaging and CAD tasks performed with CT (e.g. lung nodule detection, assessment of tumor size, etc.) may be sensitive to image acquisition and reconstruction parameters such as dose level, image thickness and reconstruction method (FBP, iterative, etc.). The purpose of this work was to develop a research pipeline for generating CT image series that represent a wide variety of acquisition and reconstruction conditions under which CAD and Quantitative Imaging performance would be evaluated.Methods:With IRB approval, we have collected the raw CT data from hundreds of patients. These raw sinogram files serve as the input to the research pipeline. To simulate a wide range of dose levels, we developed software which adds noise to the sinogram. Multiple reduced‐dose sinograms can be generated for a single patient, and those reduced‐dose sinograms are then fed either to the scanner's reconstruction engine or to our in‐house reconstruction engine; each has conventional filtered back projection (FBP) and iterative reconstruction methods. After generating image series across a range of dose levels and reconstruction methods, we can evaluate the performance of various quantitative imaging or CAD tools in tasks such as automated and semi‐automated lesion segmentation, assessment of lesion size, and measurement of density or texture.Results:We have successfully applied this pipeline across a range of clinical CT applications, including: (1) chest oncology, where the pipeline was used to quantify the effects of dose and reconstruction method on nodule volumetry, and (2) lung cancer screening, where the pipeline is being used to measure the robustness of an automated CAD algorithm with respect to dose.Conclusion:The acquisition/reconstruction pipeline shows promise for investigating and quantifying the effects of dose and reconstruction method on various clinical CT applications.NCI grant U01 CA181156 (Quantitative Imaging Network); Tobacco Related Disease Research Project grant 22RT‐0131.
TU‐G‐204‐08: Investigation of Volume Adjustment Techniques in Low Dose CT Lung Densitometry
Authors: Chen‐Mayer H , Fain S , Judy P 
Published: June 2015

Purpose:Low dose CT for lung densitometry has shown utility in evaluating severity and progression of pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). Effort is underway at QIBA to standardize measurement protocols based on published longitudinal studies on non‐diseased subjects. The purpose of this report is to assess an important component of the densitometry CT data interpretation: the volume adjustment necessary to reduce the effect of respiration on density.Methods:The 15 percentile density scores (Perc 15), defined as a threshold HU containing 15% lowest density in the attenuation histogram of the whole lung, were compiled for 30 subjects obtained from the National Lung Cancer Trial database with baseline and repeat scans one year apart. A model assuming an adapted sponge model, where the relationship between volume (V) and density (rho) is given by m=rhoV^s (Staring et al, Med. Phys. 41, 021905 (2014)). s can be obtained for each subject, and for the entire sample, which allowed adjustment of the Perc15 value at the repeat scan both at the sample level and for each subject.Results:The average bias (change between scan 2 and 1) and standard deviation (SD) of the bias for a) unadjusted, b) sample‐level adjusted, and c) subject‐level adjusted Perc 15 (HU) values are 2.0 (13.7), 1.5 (3.6), and 2.4 (9.8), respectively, suggesting that the method of volume adjustment affects the assessment of the accuracy and precision of the measurement. Method b) improves both more than c). The distribution of the value s indicates that less than 1/5 of the subjects followed the sponge‐like behavior (unity s).Conclusion:Volume adjustment has a major impact on the assessment of accuracy and precision of quantitative lung density CT, and therefore the interpretation directly influence the decision making in a clinical setting.Sean Fain receives grant funding from GE Healthcare.
TU‐G‐207‐03: High Spatial Resolution and High Sensitivity X‐Ray Fluorescence Imaging
Authors: Xing L. 
Published: June 2015

Last few years has witnessed the development of novel of X‐ray imaging modalities, such as spectral CT, phase contrast CT, and X‐ray acoustic/fluorescence/luminescence imaging. This symposium will present the recent advances of these emerging X‐ray imaging modalities and update the attendees with knowledge in various related topics, including X‐ray photon‐counting detectors, X‐ray physics underlying the emerging applications beyond the traditional X‐ray imaging, image reconstruction for the novel modalities, characterization and evaluation of the systems, and their practical implications. In addition, the concept and practical aspects of X‐ray activatable targeted nanoparticles for molecular X‐ray imaging will be discussed in the context of X‐ray fluorescence and luminescence CT.Learning Objectives:

Present background knowledge of various emerging X‐ray imaging techniques, such as spectral CT, phase contrast CT and X‐ray fluorescence/luminescence CT.
Discuss the practical need, technical aspects and current status of the emerging X‐ray imaging modalities.
Describe utility and future impact of the new generation of X‐ray imaging applications.