Purpose: To investigate the usefulness of metal artifact reduction in CT images of patients with bilateral hip prostheses (BHP) for contouring the prostate and determine if the inclusion of MR images provides additional benefits. Methods: Five patients with BHP were CT scanned using our clinical protocol (140kV, 300mAs, 3mm slices, 1.5mm increment, Philips Medical Systems, OH). Images were reconstructed with the orthopaedic metal artifact reduction (O-MAR) algorithm. MRI scanning was then performed (1.5T, GE Healthcare, WI) with a flat table-top (T{sub 2}-weighted, inherent body coil, FRFSE, 3mm slices with 0mm gap). All images were transferred to Pinnacle (Version 9.2, Philips Medical Systems). For each patient, two data sets were produced: one containing the O-MAR-corrected CT images and another containing fused MRI and O-MAR-corrected CT images. Four genito-urinary radiation oncologists contoured the prostate of each patient on the O-MAR-corrected CT data. Two weeks later, they contoured the prostate on the fused data set, blinded to all other contours. During each contouring session, the oncologists reported their confidence in the contours (1=very confident, 3=not confident) and the contouring difficulty that they experienced (1=really easy, 4=very challenging). Prostate volumes were computed from the contours and the conformity index was used to evaluate inter-observer variability. Results: Larger prostate volumes were found on the O-MAR-corrected CT set than on the fused set (p< 0.05, median=36.9cm{sup 3} vs. 26.63 cm{sup 3}). No significant differences were noted in the inter-observer variability between the two data sets (p=0.3). Contouring difficulty decreased with the addition of MRI (p<0.05) while the radiation oncologists reported more confidence in their contours when MRI was fused with the O-MAR-corrected CT data (p<0.05). Conclusion: This preliminary work demonstrated that, while O-MAR correction to CT images improves visualization of anatomy, the addition of MRI enhanced the oncologists’ confidence in contouring the prostate in patients with BHP.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-142: An Analytical Model to Correct the Aperture Scattered Dose in Clinical Proton Beams

Purpose: Apertures or collimators are used to laterally shape proton beams in double scattering (DS) delivery and to sharpen the penumbra in pencil beam (PB) delivery. However, aperture-scattered dose is not included in the current dose calculations of treatment planning system (TPS). The purpose of this study is to provide a method to correct the aperture-scattered dose based on an analytical model. Methods: A DS beam with a non-divergent aperture was delivered using a single-room proton machine. Dose profiles were measured with an ion-chamber scanning in water and a 2-D ion chamber matrix with solid-water buildup at various depths. The measured doses were considered as the sum of the non-contaminated dose and the aperture-scattered dose. The non-contaminated dose was calculated by TPS and subtracted from the measured dose. Aperture scattered-dose was modeled as a 1D Gaussian distribution. For 2-D fields, to calculate the scatter-dose from all the edges of aperture, a sum of weighted distance was used in the model based on the distance from calculation point to aperture edge. The gamma index was calculated between the measured and calculated dose with and without scatter correction. Results: For a beam with range of 23 cm and aperture size of 20 cm, the contribution of the scatter horn was ∼8% of the total dose at 4 cm depth and diminished to 0 at 15 cm depth. The amplitude of scatter-dose decreased linearly with the depth increase. The 1D gamma index (2%/2 mm) between the calculated and measured profiles increased from 63% to 98% for 4 cm depth and from 83% to 98% at 13 cm depth. The 2D gamma index (2%/2 mm) at 4 cm depth has improved from 78% to 94%. Conclusion: Using the simple analytical method the discrepancy between the measured and calculated dose has significantly improved.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-275: A Correlation Study On 3D Fluence-Based QA and 2D Dose Measurement-Based QA

Purpose: The aim of this paper was to demonstrate the feasibility and creditability of computing and verifying 3D fluencies to assure IMRT and VMAT treatment deliveries, by correlating the passing rates of the 3D fluence-based QA (P(ά)) to the passing rates of 2D dose measurementbased QA (P(Dm)). Methods: 3D volumetric primary fluencies are calculated by forward-projecting the beam apertures and modulated by beam MU values at all gantry angles. We first introduce simulated machine parameter errors (MU, MLC positions, jaw, gantry and collimator) to the plan. Using passing rates of voxel intensity differences (P(Ir)) and 3D gamma analysis (P(γ)), calculated 3D fluencies, calculated 3D delivered dose, and measured 2D planar dose in phantom from the original plan are then compared with those from corresponding plans with errors, respectively. The correlations of these three groups of resultant passing rates, i.e. 3D fluence-based QA (P(ά,Ir) and P(ά,γ)), calculated 3D dose (P(Dc,Ir) and P(Dc,γ)), and 2D dose measurement-based QA (P(Dm,Ir) and P(Dm,γ)), will be investigated. Results: 20 treatment plans with 5 different types of errors were tested. Spearman’s correlations were found between P(ά,Ir) and P(Dc,Ir), and also between P(ά,γ) and P(Dc,γ), with averaged p-value 0.037, 0.065, and averaged correlation coefficient ρ-value 0.942, 0.871 respectively. Using Matrixx QA for IMRT plans, Spearman’s correlations were also obtained between P(ά,Ir) and P(Dm,Ir) and also between P(ά,γ) and P(Dm,γ), with p-value being 0.048, 0.071 and ρ-value being 0.897, 0.779 respectively. Conclusion: The demonstrated correlations improve the creditability of using 3D fluence-based QA for assuring treatment deliveries for IMRT/VMAT plans. Together with advantages of high detection sensitivity and better visualization of machine parameter errors, this study further demonstrates the accuracy and feasibility of 3D fluence based-QA in pre-treatment QA and daily QA. Research reported in this study is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-FG-201-03: Automatic Pre-Delivery Verification Using Statistical Analysis of Consistencies in Treatment Plan Parameters by the Treatment Site and Modality

Purpose: A novel computer software system, namely APDV (Automatic Pre-Delivery Verification), has been developed for verifying patient treatment plan parameters right prior to treatment deliveries in order to automatically detect and prevent catastrophic errors. Methods: APDV is designed to continuously monitor new DICOM plan files on the TMS computer at the treatment console. When new plans to be delivered are detected, APDV checks the consistencies of plan parameters and high-level plan statistics using underlying rules and statistical properties based on given treatment site, technique and modality. These rules were quantitatively derived by retrospectively analyzing all the EBRT treatment plans of the past 8 years at authors’ institution. Therapists and physicists will be notified with a warning message displayed on the TMS computer if any critical errors are detected, and check results, confirmation, together with dismissal actions will be saved into database for further review. Results: APDV was implemented as a stand-alone program using C# to ensure required real time performance. Mean values and standard deviations were quantitatively derived for various plan parameters including MLC usage, MU/cGy radio, beam SSD, beam weighting, and the beam gantry angles (only for lateral targets) per treatment site, technique and modality. 2D-based rules of combined MU/cGy ratio and averaged SSD values were also derived using joint probabilities of confidence error ellipses. The statistics of these major treatment plan parameters quantitatively evaluate the consistency of any treatment plans which facilitates automatic APDV checking procedures. Conclusion: APDV could be useful in detecting and preventing catastrophic errors immediately before treatment deliveries. Future plan including automatic patient identify and patient setup checks after patient daily images are acquired by the machine and become available on the TMS computer. This project is supported by the Agency for Healthcare Research and Quality (AHRQ) under award 1R01HS0222888. The senior author received research grants from ViewRay Inc. and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-H-BRC-06: A Unified Machine-Learning Based Probabilistic Model for Automated Anomaly Detection in the Treatment Plan Data

Purpose: The purpose of this work was to investigate the ability of a machine-learning based probabilistic approach to detect radiotherapy treatment plan anomalies given initial disease classes information. Methods In total we obtained 1112 unique treatment plans with five plan parameters and disease information from a Mosaiq treatment management system database for use in the study. The plan parameters include prescription dose, fractions, fields, modality and techniques. The disease information includes disease site, and T, M and N disease stages. A Bayesian network method was employed to model the probabilistic relationships between tumor disease information, plan parameters and an anomaly flag. A Bayesian learning method with Dirichlet prior was useed to learn the joint probabilities between dependent variables in error-free plan data and data with artificially induced anomalies. In the study, we randomly sampled data with anomaly in a specified anomaly space.We tested the approach with three groups of plan anomalies – improper concurrence of values of all five plan parameters and values of any two out of five parameters, and all single plan parameter value anomalies. Totally, 16 types of plan anomalies were covered by the study. For each type, we trained an individual Bayesian network. Results: We found that the true positive rate (recall) and positive predictive value (precision) to detect concurrence anomalies of five plan parameters in new patient cases were 94.45±0.26% and 93.76±0.39% respectively. To detect other 15 types of plan anomalies, the average recall and precision were 93.61±2.57% and 93.78±3.54% respectively. The computation time to detect the plan anomaly of each type in a new plan is ∼0.08 seconds. Conclusion: The proposed method for treatment plan anomaly detection was found effective in the initial tests. The results suggest that this type of models could be applied to develop plan anomaly detection tools to assist manual and automated plan checks. The senior author received research grants from ViewRay Inc. and Varian Medical System.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Structural Basis of Transcription Inhibition by CBR Hydroxamidines and CBR Pyrazoles

Abstract not provided

Feng, Yu↗

DOE OSTI2015

SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the treatment plan complexity. This work was supported by a research grant from Viewray Inc.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2015

Visual exploration of high-dimensional data through subspace analysis and dynamic projections

Here, we introduce a novel interactive framework for visualizing and exploring high-dimensional datasets based on subspace analysis and dynamic projections. We assume the high-dimensional dataset can be represented by a mixture of low-dimensional linear subspaces with mixed dimensions, and provide a method to reliably estimate the intrinsic dimension and linear basis of each subspace extracted from the subspace clustering. Subsequently, we use these bases to define unique 2D linear projections as viewpoints from which to visualize the data. To understand the relationships among the different projections and to discover hidden patterns, we connect these projections through dynamic projections that create smooth animated transitions between pairs of projections. We introduce the view transition graph, which provides flexible navigation among these projections to facilitate an intuitive exploration. Finally, we provide detailed comparisons with related systems, and use real-world examples to demonstrate the novelty and usability of our proposed framework.

97 MATHEMATICS AND COMPUTING↗