bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐G‐303‐04: Radiomics and the Coming Pan‐Omics Revolution
Authors: El Naqa I. 
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‐204‐03: Dynamic CT Myocardial Perfusion Measurement Using First Pass Analysis and Maximum Slope Models
Authors: Hubbard L , Ziemer B , Sadeghi B , Javan H , Lipinski J , Molloi S 
Published: June 2015

Purpose:To evaluate the accuracy of dynamic CT myocardial perfusion measurement using first pass analysis (FPA) and maximum slope models.Methods:A swine animal model was prepared by percutaneous advancement of an angioplasty balloon into the proximal left anterior descending (LAD) coronary artery to induce varying degrees of stenosis. Maximal hyperaemia was achieved in the LAD with an intracoronary adenosine drip (240 µg/min). Serial microsphere and contrast (370 mg/mL iodine, 30 mL, 5mL/s) injections were made over a range of induced stenoses, and dynamic imaging was performed using a 320‐row CT scanner at 100 kVp and 200 mA. The FPA CT perfusion technique was used to make vessel‐specific myocardial perfusion measurements. CT perfusion measurements using the FPA and maximum slope models were validated using colored microspheres as the reference gold standard.Results:Perfusion measurements using the FPA technique (P_FPA) showed good correlation with minimal offset when compared to perfusion measurements using microspheres (P_ Micro) as the reference standard (P _FPA = 0.96 P_Micro + 0.05, R2 = 0.97, RMSE = 0.19 mL/min/g). In contrast, the maximum slope model technique (P_MS) was shown to underestimate perfusion when compared to microsphere perfusion measurements (P_MS = 0.42 P _Micro −0.48, R2 = 0.94, RMSE = 3.3 mL/min/g).Conclusion:The results indicate the potential for significant improvements in accuracy of dynamic CT myocardial perfusion measurement using the first pass analysis technique as compared with the standard maximum slope model.
TU‐G‐204‐04: A Unified Strategy for Bi‐Factorial Optimization of Radiation Dose and Contrast Dose in CT Imaging
Authors: Sahbaee P , Zhang Y , Solomon J , Becchetti M , Segars P , Samei E 
Published: June 2015

Purpose:To substantiate the interdependency of contrast dose, radiation dose, and image quality in CT towards the patient‐ specific optimization of the imaging protocolsMethods:The study deployed two phantom platforms. A variable sized (12, 18, 23, 30, 37 cm) phantom (Mercury‐3.0) containing an iodinated insert (8.5 mgI/ml) was imaged on a representative CT scanner at multiple CTDI values (0.7–22.6 mGy). The contrast and noise were measured from the reconstructed images for each phantom diameter. Linearly related to iodine‐concentration, contrast‐to‐noise ratio (CNR), were calculated for 16 iodine‐concentration levels (0–8.5 mgI/ml). The analysis was extended to a recently developed suit of 58 virtual human models (5D XCAT) with added contrast dynamics. Emulating a contrast‐enhanced abdominal image procedure and targeting a peak‐enhancement in aorta, each XCAT phantom was “imaged” using a simulation platform (CatSim, GE). 3D surfaces for each patient/size established the relationship between iodine‐concentration, dose, and CNR. The ratios of change in iodine‐concentration versus dose (IDR) to yield a constant change in CNR were calculated for each patient size.Results:Mercury phantom results show the image‐quality size‐ dependence on CTDI and IC levels. For desired image‐quality values, the iso‐contour‐lines reflect the trade off between contrast‐material and radiation doses. For a fixed iodine‐concentration (4 mgI/mL), the IDR values for low (1.4 mGy) and high (11.5 mGy) dose levels were 1.02, 1.07, 1.19, 1.65, 1.54, and 3.14, 3.12, 3.52, 3.76, 4.06, respectively across five sizes. The simulation data from XCAT models confirmed the empirical results from Mercury phantom.Conclusion:The iodine‐concentration, image quality, and radiation dose are interdependent. The understanding of the relationships between iodine‐concentration, image quality, and radiation dose will allow for a more comprehensive optimization of CT imaging devices and techniques, providing the methodology to balance iodine‐concentration and dose based on patient's attributes.
TU‐G‐204‐05: The Effects of CT Acquisition and Reconstruction Conditions On Computed Texture Feature Values of Lung Lesions
Authors: Lo P , Young S , Kim G , Hoffman J , Brown M , McNitt‐Gray M 
Published: June 2015

Purpose:Texture features have been investigated as a biomarker of response and malignancy. Because these features reflect local differences in density, they may be influenced by acquisition and reconstruction parameters. The purpose of this study was to investigate the effects of radiation dose level and reconstruction method on features derived from lung lesions.Methods:With IRB approval, 33 lung tumor cases were identified from clinically indicated thoracic CT scans in which the raw projection (sinogram) data were available. Based on a previously‐published technique, noise was added to the raw data to simulate reduced‐dose versions of each case at 25%, 10% and 3% of the original dose. Original and simulated reduced dose projection data were reconstructed with conventional and two iterative‐reconstruction settings, yielding 12 combinations of dose/recon conditions. One lesion from each case was contoured. At the reference condition (full dose, conventional recon), 17 lesions were randomly selected for repeat contouring (repeatability). For each lesion at each dose/recon condition, 151 texture measures were calculated. A paired differences approach was employed to compare feature variation from repeat contours at the reference condition to the variation observed in other dose/recon conditions (reproducibility). The ratio of standard deviation of the reproducibility to repeatability was used as the variation measure for each feature.Results:The mean variation (standard deviation) across dose levels and kernel was significantly different with a ratio of 2.24 (±5.85) across texture features (p=0.01). The mean variation (standard deviation) across dose levels with conventional recon was also significantly different with 2.30 (7.11) (p=0.025). The mean variation across reconstruction settings of original dose has a trend in showing difference with 1.35 (2.60) among all features (p=0.09).Conclusion:Texture features varied considerably with variations in dose and reconstruction condition. Care should be taken to standardize these conditions when using texture as a quantitative feature.This effort supported in part by a grant from the National Cancer Institute's Quantitative Imaging Network (QIN): U01 CA181156; The UCLA Department of Radiology has a Master Research Agreement with Siemens Healthcare; Dr. McNitt‐Gray has previously received research support from Siemens Healthcare.
TU‐G‐204‐06: Correlation Between Texture Analysis‐Based Model Observer and Human Observer in Diagnosis of Ischemic Infarct in Non‐Contrast Head CT of Adults
Authors: Li B , Fujita A , Buch K , Sakai O 
Published: June 2015

Purpose:To investigate the correlation between texture analysis‐based model observer and human observer in the task of diagnosis of ischemic infarct in non‐contrast head CT of adults.Methods:Non‐contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro‐reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro ‐radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9‐point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro‐radiologist placed multiple ROIs over the infarcts. These ROIs were entered into a texture analysis software package. Forty‐two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2‐D features, were computed and averaged over the images per dataset. The Fisher‐coefficient (ratio of between‐class variance to in‐class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher ‐coefficient were entered into linear multivariate regression for iterative modeling.Results:Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p‐value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R‐sq=0.77). The root‐mean‐square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased.Conclusion:For diagnosis of ischemic infarct in non‐contrast head CT in adults, the predicted image quality scores from texture analysis‐based model observer was highly correlated with that of human observers for various noise levels. Texture‐based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.
TU‐G‐207‐02: Clinical Applications of X‐Ray Phase Contrast Imaging: Where Do We Stand?
Authors: Li K. 
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.
TU‐G‐201‐02: An MRI Simulator From Proposal to Operation
Authors: Cao Y. 
Published: June 2015

This session will update therapeutic physicists on technological advancements and radiation oncology features of commercial CT, MRI, and PET/CT imaging systems. Also described are physicists’ roles in every stage of equipment selection, purchasing, and operation, including defining specifications, evaluating vendors, making recommendations, and optimal and safe use of imaging equipment in radiation oncology environment.The first presentation defines important terminology of CT and PET/CT followed by a review of latest innovations, such as metal artifact reduction, statistical iterative reconstruction, radiation dose management, tissue classification by dual energy CT and spectral CT, improvement in spatial resolution and sensitivity in PET, and potentials of PET/MR. We will also discuss important technical specifications and items in CT and PET/CT purchasing quotes and their impacts.The second presentation will focus on key components in the request for proposal for a MRI simulator and how to evaluate vendor proposals. MRI safety issues in radiation Oncology, including MRI scanner Zones (4‐zone design), will be discussed. Basic MR terminologies, important functionalities, and advanced features, which are relevant to radiation therapy, will be discussed.In the third presentation, justification of imaging systems for radiation oncology, considerations in room design and construction in a RO department, shared use with diagnostic radiology, staffing needs and training, clinical/research use cases and implementation, will be discussed. The emphasis will be on understanding and bridging the differences between diagnostic and radiation oncology installations, building consensus amongst stakeholders for purchase and use, and integrating imaging technologies into the radiation oncology environment.Learning Objectives:

Learn the latest innovations of major imaging systems relevant to radiation therapy
Be able to describe important technical specifications of CT, MRI, and PET/CT
Understand the process of budget request, equipment justification, comparisons of technical specifications, site visits, vendor selection, and contract development
TU‐G‐210‐02: TRANS‐FUSIMO ‐ An Integrative Approach to Model‐Based Treatment Planning of Liver FUS
Authors: Preusser T. 
Published: June 2015

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney.Douglas Christensen – Modeling for Breast and Brain HIFU Treatment PlanningTobias Preusser – TRANS‐FUSIMO – An Integrative Approach to Model‐Based Treatment Planning of Liver FUSTobias Preusser – TRANS‐FUSIMO – An Integrative Approach to Model‐Based Treatment Planning of Liver FUSLearning Objectives:

Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments.
Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies.
Determine how to obtain appropriate acoustic modeling parameters from clinical images.
Understand the separate role of absorption and scattering in energy delivery to tissues.
See how organ motion can be compensated for in ultrasound therapies.
Compare simulated data with clinical temperature measurements in transcranial applications.
Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007‐2013) under 270186 (FUSIMO) and 611889 (TRANS‐FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV).
TU‐F‐CAMPUS‐T‐01: Dose and Energy Spectra From Neutron Induced Radioactivity in Medical Linear Accelerators Following High Energy Total Body Irradiation
Authors: Keehan S , Taylor M , Smith R , Dunn L , Kron T , Franich R 
Published: June 2015

Purpose:To assess the risk posed by neutron induced activation of components in medical linear accelerators (linacs) following the delivery of high monitor unit 18 MV photon beams such as used in TBI.Methods:Gamma spectroscopy was used to identify radioisotopes produced in components of a Varian 21EX and an Elekta Synergy following delivery of photon beams. Dose and risk estimates for TBI were assessed using dose deliveries from an actual patient treatment. A 1 litre spherical ion chamber (PTW, Germany) has been used to measure the dose at the beam exit window and at the total body irradiation (TBI) treatment couch following large and small field beams with long beam‐on times. Measurements were also made outside of the closed jaws to quantify the benefit of the attenuation provided by the jaws.Results:The radioisotopes produced in the linac head have been identified as 18⁷W, ⁵⁶Mn, 2⁴Na and 2⁸Al, which have half‐lives from between 2.3 min to 24 hours. The dose at the beam exit window following an 18 MV 2197 MU TBI beam delivery was 12.6 µSv in ten minutes. The dose rate at the TBI treatment couch 4.8 m away is a factor of ten lower. For a typical TBI delivered in six fractions each consisting of four beams and an annual patient load of 24, the annual dose estimate for a staff member at the treatment couch for ten minutes is 750 µSv. This can be further reduced by a factor of about twelve if the jaws are closed before entering the room, resulting in a dose estimate of 65 µSv.Conclusion:The dose resulting from the activation products for a representative TBI workload at our clinic of 24 patients per year is 750 µSv, which can be further reduced to 65 µSv by closing the jaws.
TU‐F‐CAMPUS‐T‐04: An Evaluation of Out‐Of‐Field Doses for Electron Beams From Modern Varian and Elekta Linear Accelerators
Authors: Cardenas C , Nitsch P , Kudchadker R , Howell R , Kry S 
Published: June 2015

Purpose:Accurately determining out‐of‐field doses when using electron beam radiotherapy is of importance when treating pregnant patients or patients with implanted electronic devices. Scattered doses outside of the applicator field in electron beams have not been broadly investigated, especially since manufacturers have taken different approaches in applicator designs.Methods:In this study, doses outside of the applicator field were measured for electron beams produced by a 10×10 applicator on two Varian 21iXs operating at 6, 9, 12, 16, and 20 MeV, a Varian TrueBeam operating at 6, 9, 12, 16, and 20 MeV, and an Elekta Versa HD operating at 6, 9, 12 and 15 MeV. Peripheral dose profiles and percent depth doses were measured in a Wellhofer water phantom at 100 cm SSD with a Farmer ion chamber. Doses were compared to peripheral photon doses from AAPM's Task Group #36 report.Results:Doses were highest for the highest electron energies. Doses typically decreased with increasing distance from the field edge but showed substantial increases over some distance ranges. Substantial dose differences were observed between different accelerators; the Elekta accelerator had much higher doses than any Varian unit examined. Surprisingly, doses were often similar to, and could be much higher than, doses from photon therapy. Doses decreased sharply with depth before becoming nearly constant; the dose was found to decrease to a depth of approximately E(MeV)/4 in cm.Conclusion:The results of this study indicate that proper shielding may be very important when utilizing electron beams, particularly on a Versa HD, while treating pregnant patients or those with implanted electronic devices. Applying a water equivalent bolus of Emax(MeV)/4 thickness (cm) on the patient would reduce fetal dose drastically for all clinical energies and is a practical solution to manage the potentially high peripheral doses seen from modern electron beams.Funding from NIH Grant number: #CA180803
TU‐F‐CAMPUS‐J‐03: Prediction of Respiratory Motion Using State Space Models
Authors: Kotoku J , Kumagai S , Haga A , Nakabayashi S , Arai N , Kobayashi T 
Published: June 2015

Purpose:The purpose of this study is to predict respiratory motion for a few seconds ahead by use of dynamic linear models. The models describe trends and periodic components of time series of respiratory curves obtained on patient's body.Methods:To measure spatial coordinates of multiple points on patient's body during respiratory motion, we used a consumer depth camera (Microsoft Kinect) and obtained depth data via triangulation from infrared random dots patterns.To describe a dynamics of respiratory motion, we selected a symplectic form of a harmonic oscillator. As a filter, we selected a particle filter. Particle filter is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations. The key idea is to represent the required posterior density function by a set of random samples and to compute estimates based on these samples.Results:Filtered values were calculated as a mean of a filtered distribution. The prediction values were well correlated with the values to be observed.To validate accuracy of our model, predicted depth values were compared with measured ones. The accuracy of our model was roughly 20 % for 4 seconds ahead in average, while the measurement depth accuracy is within 1 mm.Conclusion:Time‐series modeling using Bayesian inference technique is useful for prediction of respiratory motion.Although prediction accuracy became worse along with the length of forecasting time, we can conclude this method is a promising tool for prediction of patient's motion.This work was partly supported by the JSPS Core‐to‐Core Program (No. 23003).