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DOE OSTI2016

MO-DE-207B-05: Predicting Gene Mutations in Renal Cell Carcinoma Based On CT Imaging Features: Validation Using TCGA-TCIA Datasets

Purpose: The goal of this work is to investigate the use of contrast enhanced computed tomographic (CT) features for the prediction of mutations of BAP1, PBRM1, and VHL genes in renal cell carcinoma (RCC). Methods: For this study, we used two patient databases with renal cell carcinoma (RCC). The first one consisted of 33 patients from our institution (UT Southwestern Medical Center, UTSW). The second one consisted of 24 patients from the Cancer Imaging Archive (TCIA), where each patient is connected by a unique identi?er to the tissue samples from the Cancer Genome Atlas (TCGA). From the contrast enhanced CT image of each patient, tumor contour was first delineated by a physician. Geometry, intensity, and texture features were extracted from the delineated tumor. Based on UTSW dataset, we completed feature selection and trained a support vector machine (SVM) classifier to predict mutations of BAP1, PBRM1 and VHL genes. We then used TCIA-TCGA dataset to validate the predictive model build upon UTSW dataset. Results: The prediction accuracy of gene expression of TCIA-TCGA patients was 0.83 (20 of 24), 0.83 (20 of 24), and 0.75 (18 of 24) for BAP1, PBRM1, and VHL respectively. For BAP1 gene, texture feature was the most prominent feature type. For PBRM1 gene, intensity feature was the most prominent. For VHL gene, geometry, intensity, and texture features were all important. Conclusion: Using our feature selection strategy and models, we achieved predictive accuracy over 0.75 for all three genes under the condition of using patient data from one institution for training and data from other institutions for testing. These results suggest that radiogenomics can be used to aid in prognosis and used as convenient surrogates for expensive and time consuming gene assay procedures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-DE-207B-08: Radiomic CT Features Complement Semantic Annotations to Predict EGFR Mutations in Lung Adenocarcinomas

Purpose: To compare the complementary value of quantitative radiomic features to that of radiologist-annotated semantic features in predicting EGFR mutations in lung adenocarcinomas. Methods: Pre-operative CT images of 258 lung adenocarcinoma patients were available. Tumors were segmented using the sing-click ensemble segmentation algorithm. A set of radiomic features was extracted using 3D-Slicer. Test-retest reproducibility and unsupervised dimensionality reduction were applied to select a subset of reproducible and independent radiomic features. Twenty semantic annotations were scored by an expert radiologist, describing the tumor, surrounding tissue and associated findings. Minimum-redundancy-maximum-relevance (MRMR) was used to identify the most informative radiomic and semantic features in 172 patients (training-set, temporal split). Radiomic, semantic and combined radiomic-semantic logistic regression models to predict EGFR mutations were evaluated in and independent validation dataset of 86 patients using the area under the receiver operating curve (AUC). Results: EGFR mutations were found in 77/172 (45%) and 39/86 (45%) of the training and validation sets, respectively. Univariate AUCs showed a similar range for both feature types: radiomics median AUC = 0.57 (range: 0.50 – 0.62); semantic median AUC = 0.53 (range: 0.50 – 0.64, Wilcoxon p = 0.55). After MRMR feature selection, the best-performing radiomic, semantic, and radiomic-semantic logistic regression models, for EGFR mutations, showed a validation AUC of 0.56 (p = 0.29), 0.63 (p = 0.063) and 0.67 (p = 0.004), respectively. Conclusion: Quantitative volumetric and textural Radiomic features complement the qualitative and semi-quantitative radiologist annotations. The prognostic value of informative qualitative semantic features such as cavitation and lobulation is increased with the addition of quantitative textural features from the tumor region.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-BRA-02: A Feasibility Study of Integrating Breathing Audio Signal with Surface Surrogates for Respiratory Motion Management

Purpose: Tracking the surrogate placed on patient skin surface sometimes leads to problematic signals for certain patients, such as shallow breathers. This in turn impairs the 4D CT image quality and dosimetric accuracy. In this pilot study, we explored the feasibility of monitoring human breathing motion by integrating breathing sound signal with surface surrogates. Methods: The breathing sound signals were acquired though a microphone attached adjacently to volunteer’s nostrils, and breathing curve were analyzed using a low pass filter. Simultaneously, the Real-time Position Management™ (RPM) system from Varian were employed on a volunteer to monitor respiratory motion including both shallow and deep breath modes. The similar experiment was performed by using Calypso system, and three beacons taped on volunteer abdominal region to capture breath motion. The period of each breathing curves were calculated with autocorrelation functions. The coherence and consistency between breathing signals using different acquisition methods were examined. Results: Clear breathing patterns were revealed by the sound signal which was coherent with the signal obtained from both the RPM system and Calypso system. For shallow breathing, the periods of breathing cycle were 3.00±0.19 sec (sound) and 3.00±0.21 sec (RPM); For deep breathing, the periods were 3.49± 0.11 sec (sound) and 3.49±0.12 sec (RPM). Compared with 4.54±0.66 sec period recorded by the calypso system, the sound measured 4.64±0.54 sec. The additional signal from sound could be supplement to the surface monitoring, and provide new parameters to model the hysteresis lung motion. Conclusion: Our preliminary study shows that the breathing sound signal can provide a comparable way as the RPM system to evaluate the respiratory motion. It’s instantaneous and robust characteristics facilitate it possibly to be a either independently or as auxiliary methods to manage respiratory motion in radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-202-01: A Fast Yet Sensitive EPID-Based Real-Time Treatment Verification System

Purpose: To create a real-time EPID-based treatment verification system which robustly detects treatment delivery and patient attenuation variations. Methods: Treatment plan DICOM files sent to the record-and-verify system are captured and utilized to predict EPID images for each planned control point using a modified GPU-based digitally reconstructed radiograph algorithm which accounts for the patient attenuation, source energy fluence, source size effects, and MLC attenuation. The DICOM and predicted images are utilized by our C++ treatment verification software which compares EPID acquired 1024×768 resolution frames acquired at ∼8.5hz from Varian Truebeam™ system. To maximize detection sensitivity, image comparisons determine (1) if radiation exists outside of the desired treatment field; (2) if radiation is lacking inside the treatment field; (3) if translations, rotations, and magnifications of the image are within tolerance. Acquisition was tested with known test fields and prior patient fields. Error detection was tested in real-time and utilizing images acquired during treatment with another system. Results: The computational time of the prediction algorithms, for a patient plan with 350 control points and 60×60×42cm^3 CT volume, is 2–3minutes on CPU and <27 seconds on GPU for 1024×768 images. The verification software requires a maximum of ∼9ms and ∼19ms for 512×384 and 1024×768 resolution images, respectively, to perform image analysis and dosimetric validations. Typical variations in geometric parameters between reference and the measured images are 0.32°for gantry rotation, 1.006 for scaling factor, and 0.67mm for translation. For excess out-of-field/missing in-field fluence, with masks extending 1mm (at isocenter) from the detected aperture edge, the average total in-field area missing EPID fluence was 1.5mm2 the out-of-field excess EPID fluence was 8mm^2, both below error tolerances. Conclusion: A real-time verification software, with EPID images prediction algorithm, was developed. The system is capable of performing verifications between frames acquisitions and identifying source(s) of any out-of-tolerance variations. This work was supported in part by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-202-06: Improving the Performance of Gamma Analysis QA with Radiomics- Based Image Analysis

Purpose: The use of gamma analysis for IMRT quality assurance has well-known limitations. Traditionally, a simple thresholding technique is used to evaluated passing criteria. However, like any image the gamma distribution is rich in information which thresholding mostly discards. We therefore propose a novel method of analyzing gamma images that uses quantitative image features borrowed from radiomics, with the goal of improving error detection. Methods: 368 gamma images were generated from 184 clinical IMRT beams. For each beam the dose to a phantom was measured with EPID dosimetry and compared to the TPS dose calculated with and without normally distributed (2mm sigma) errors in MLC positions. The magnitude of 17 intensity histogram and size-zone radiomic features were derived from each image. The features that differed most significantly between image sets were determined with ROC analysis. A linear machine-learning model was trained on these features to classify images as with or without errors on 180 gamma images.The model was then applied to an independent validation set of 188 additional gamma distributions, half with and half without errors. Results: The most significant features for detecting errors were histogram kurtosis (p=0.007) and three size-zone metrics (p<1e-6 for each). The sizezone metrics detected clusters of high gamma-value pixels under mispositioned MLCs. The model applied to the validation set had an AUC of 0.8, compared to 0.56 for traditional gamma analysis with the decision threshold restricted to 98% or less. Conclusion: A radiomics-based image analysis method was developed that is more effective in detecting error than traditional gamma analysis. Though the pilot study here considers only MLC position errors, radiomics-based methods for other error types are being developed, which may provide better error detection and useful information on the source of detected errors. This work was partially supported by a grant from the Agency for Healthcare Research and Quality, grant number R18 HS022244-01.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-CAMPUS-JeP1-01: Prompt Gamma Imaging with a Multi-Knife-Edge Slit Collimator: Evaluation for Use in Proton Beam Range Verification

Purpose: To evaluate and characterize a multi-slit collimated imaging system for use in prompt gamma range verification of proton therapy. Methods: Acrylic (PMMA) targets were irradiated with a 50 MeV proton beam. With the collimator placed 13 cm from the beam axis, photons of energy from 2–7 MeV were measured. Image reconstruction provided 2-dimensional distribution of gamma rays. Estimated Bragg peak location was compared with 1-dimensional profiles of photon images. Shifts in Bragg peak were simulated by physically moving the targets in 1 mm increments. Results: The imaging system measured prompt gamma emissions resulting from a 50 MeV proton beam, at currents up to 2 nA, incident on a PMMA target. Overall system detection efficiency was approximately 2.6×10{sup −5} gamma/proton. With delivery of 1×10{sup 11} protons, shifts of 1 mm in the target location were detected in 2D prompt gamma images and 1D profiles. With delivery of 1×10{sup 8} protons, shifts of approximately 3 mm were detectable. Conclusion: This work has characterized the performance of a prototype multi-slit collimated imaging system. The system can produce 2D images of prompt gamma distributions and detect shifts in Bragg peak location down to 1 mm. These results encourage further development and optimization of the system for clinical proton beam applications. This material is based upon work supported by the Department of Energy National Nuclear Security Administration under Award Number: DENA0000979 through the Nuclear Science and Security Consortium.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

MO-FG-CAMPUS-TeP3-01: A Model of Baseline Shift to Improve Robustness of Proton Therapy Treatments of Moving Tumors

Purpose: The shift in mean position of a moving tumor also known as “baseline shift”, has been modeled, in order to automatically generate uncertainty scenarios for the assessment and robust optimization of proton therapy treatments in lung cancer. Methods: An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at the planning time are first generated from a 4D-CT data. The mean position of the tumor along the breathing cycle is represented by the GTV contour in the MidPCT. Several studies reported both systematic and random variations of the mean tumor position from fraction to fraction. Our model can simulate this baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall in order to allow the tumor to slip within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce its continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results: Based on the first 4D-CT set, the model was able to generate a third 4D-CT that reproduced the 5.8 mm baseline-shift measured in the second 4D-CT. Water equivalent thickness (WET) of the voxels have been computed for the 3 average CTs. The root mean square deviation of the WET in the GTV is 0.34 mm between week 1 and week 2, and 0.08 mm between the simulated data and week 2. Conclusion: Our model can be used to automatically generate uncertainty scenarios for robustness analysis of a proton therapy plan. The generated scenarios can also feed a TPS equipped with a robust optimizer. Kevin Souris, Ana Barragan, and Dario Di Perri are financially supported by Televie Grants from F.R.S.-FNRS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Technical Note: Evaluation of a 160-mm/256-row CT scanner for whole-heart quantitative myocardial perfusion imaging

Purpose: The authors investigated the performance of a recently introduced 160-mm/256-row CT system for low dose quantitative myocardial perfusion (MP) imaging of the whole heart. This platform is equipped with a gantry capable of rotating at 280 ms per full cycle, a second generation of adaptive statistical iterative reconstruction (ASiR-V) to correct for image noise arising from low tube voltage potential/tube current dynamic scanning, and image reconstruction algorithms to tackle beam-hardening, cone-beam, and partial-scan effects. Methods: Phantom studies were performed to investigate the effectiveness of image noise and artifact reduction with a GE Healthcare Revolution CT system for three acquisition protocols used in quantitative CT MP imaging: 100, 120, and 140 kVp/25 mAs. The heart chambers of an anthropomorphic chest phantom were filled with iodinated contrast solution at different concentrations (contrast levels) to simulate the circulation of contrast through the heart in quantitative CT MP imaging. To evaluate beam-hardening correction, the phantom was scanned at each contrast level to measure the changes in CT number (in Hounsfield unit or HU) in the water-filled region surrounding the heart chambers with respect to baseline. To evaluate cone-beam artifact correction, differences in mean water HU between the central and peripheral slices were compared. Partial-scan artifact correction was evaluated from the fluctuation of mean water HU in successive partial scans. To evaluate image noise reduction, a small hollow region adjacent to the heart chambers was filled with diluted contrast, and contrast-to-noise ratio in the region before and after noise correction with ASiR-V was compared. The quality of MP maps acquired with the CT system was also evaluated in porcine CT MP studies. Myocardial infarct was induced in a farm pig from a transient occlusion of the distal left anterior descending (LAD) artery with a catheter-based interventional procedure. MP maps were generated from the dynamic contrast-enhanced (DCE) heart images taken at baseline and three weeks after the ischemic insult. Results: Their results showed that the phantom and animal images acquired with the CT platform were minimally affected by image noise and artifacts. For the beam-hardening phantom study, changes in water HU in the wall surrounding the heart chambers greatly reduced from >±30 to ≤ ± 5 HU at all kVp settings except one region at 100 kVp (7 HU). For the cone-beam phantom study, differences in mean water HU from the central slice were less than 5 HU at two peripheral slices with each 4 cm away from the central slice. These findings were reproducible in the pig DCE images at two peripheral slices that were 6 cm away from the central slice. For the partial-scan phantom study, standard deviations of the mean water HU in 10 successive partial scans were less than 5 HU at the central slice. Similar observations were made in the pig DCE images at two peripheral slices with each 6 cm away from the central slice. For the image noise phantom study, CNRs in the ASiR-V images were statistically higher (p < 0.05) than the non-ASiR-V images at all kVp settings. MP maps generated from the porcine DCE images were in excellent quality, with the ischemia in the LAD territory clearly seen in the three orthogonal views. Conclusions: The study demonstrates that this CT system can provide accurate and reproducible CT numbers during cardiac gated acquisitions across a wide axial field of view. This CT number fidelity will enable this imaging tool to assess contrast enhancement, potentially providing valuable added information beyond anatomic evaluation of coronary stenoses. Furthermore, their results collectively suggested that the 100 kVp/25 mAs protocol run on this CT system provides sufficient image accuracy at a low radiation dose (<3 mSv) for whole-heart quantitative CT MP imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-AB-207A-03: Image Quality, Dose, and Clinical Applications

Practicing medical physicists are often time charged with the tasks of evaluating and troubleshooting complex image quality issues related to CT scanners. This course will equip them with a solid and practical understanding of common CT imaging chain and its major components with emphasis on acquisition physics and hardware, reconstruction, artifacts, image quality, dose, and advanced clinical applications. The core objective is to explain the effects of these major system components on the image quality. This course will not focus on the rapid-changing advanced technologies given the two-hour time limit, but the fundamental principles discussed in this course may facilitate better understanding of those more complicated technologies. The course will begin with an overview of CT acquisition physics and geometry. X-ray tube and CT detector are important acquisition hardware critical to the overall image quality. Each of these two subsystems consists of several major components. An in-depth description of the function and failure modes of these components will be provided. Examples of artifacts related to these failure modes will be presented: off-focal radiation, tube arcing, heel effect, oil bubble, offset drift effect, cross-talk effect, and bad pixels. The fundamentals of CT image reconstruction will first be discussed on an intuitive level. Approaches that do not require rigorous derivation of mathematical formulations will be presented. This is followed by a detailed derivation of the Fourier slice theorem: the foundation of the FBP algorithm. FBP for parallel-beam, fan-beam, and cone-beam geometries will be discussed. To address the issue of radiation dose related to x-ray CT, recent advances in iterative reconstruction, their advantages, and clinical applications will also be described. Because of the nature of fundamental physics and mathematics, limitations in data acquisition, and non-ideal conditions of major system components, image artifact often arise in the reconstructed images. Because of the limited scope of this course, only major imaging artifacts, their appearance, and possible mitigation and corrections will be discussed. Assessment of the performance of a CT scanner is a complicated subject. Procedures to measure common image quality metrics such as high contrast spatial resolution, low contrast detectability, and slice profile will be described. The reason why these metrics used for FBP may not be sufficient for statistical iterative reconstruction will be explained. Optimizing radiation dose requires comprehension of CT dose metrics. This course will briefly describe various dose metrics, and interaction with acquisition parameters and patient habitus. CT is among the most frequently used imaging tools due to its superior image quality, easy to operate, and a broad range of applications. This course will present several interesting CT applications such as a mobile CT unit on an ambulance for stroke patients, low dose lung cancer screening, and single heartbeat cardiac CT. Learning Objectives: Understand the function and impact of major components of X-ray tube on the image quality. Understand the function and impact of major components of CT detector on the image quality. Be familiar with the basic procedure of CT image reconstruction. Understand the effect of image reconstruction on CT image quality and artifacts. Understand the root causes of common CT image artifacts. Be familiar with image quality metrics especially high and low contrast resolution, noise power spectrum, slice sensitivity profile, etc. Understand why basic image quality metrics used for FBP may not be sufficient to characterize the performance of advanced iterative reconstruction. Be familiar with various CT dose metrics and their interaction with acquisition parameters. New development in advanced CT clinical applications. JH: Employee of GE Healthcare. FD: No disclosure.; J. Hsieh, Jiang Hsieh is an employee of GE Healthcare.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-FG-201-07: Development of SRS Conical Collimator Collision Prediction Software for Radiation Treatment Safety

Purpose: Stereotactic radiosurgery (SRS) treatments using conical collimators can potentially result in gantry collision with treatment table due to limited collision-clear spaces. An in-house software was developed to help the SRS treatment planner mitigate potential SRS conical collimator (Varian Medical System, Palo Alto, CA) collisions with the treatment table. This software was designed to remove treatment re-planning secondary to unexpected collisions. Methods: A BrainLAB SRS ICT Frameless Extension used for SRS treatments in our clinic was mathematically modelled using surface points registered to the 3D co-ordinate space of the couch extension. The surface points are transformed based on the treatment isocenter point and potential collisions are determined in 3D space for couch and gantry angle combinations. The distance between the SRS conical collimators and LINAC isocenter is known. The collision detection model was programmed in MATLAB (Mathwork, Natick, MA) to display graphical plots of the calculations, and the plotted data is used to avoid the gantry and couch angle combinations that would likely result in a collision. We have utilized the cone collision tool for 23 SRS cone treatment plans (8 retrospective and 15 prospective for 10 patients). Results: Twenty one plans strongly agreed with the software tool prediction for collision. However, in two plans, a collision was observed with a 0.5 cm margin when the software predicted no collision. Therefore, additional margins were added to the clearance criteria in the program to achieve a lower risk of actual collisions. Conclusion: Our in-house developed collision check software successfully avoided SRS cone re-planning by 91.3% due to a reduction in cone collisions with the treatment table. Future developments to our software will include a CT image data set based collision prediction model as well as a beam angle optimization tool to avoid normal critical tissues as well as previously treated lesions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-FG-BRB-01: Dual Energy CT Proton Stopping Power Ratio Calibration and Validation with Animal Tissues

Purpose: The conversion of Hounsfield Unit (HU) to proton stopping power ratio (SPR) is a main source of uncertainty in proton therapy. In this study, the SPRs of animal tissues were measured and compared with prediction from dual energy CT (DECT) and single energy CT (SECT) calibrations. Methods: A stoichiometric calibration method for DECT was applied to predict the SPR using CT images acquired at 80 kVp and 140 kVp. The dual energy index was derived based on the HUs of the paired spectral images and used to calculate the SPRs of the materials. Tissue surrogates with known chemical compositions were used for calibration, and animal tissues (pig brain, liver, kidney; veal shank, muscle) were used for validation. The materials were irradiated with proton pencil beams, and SPRs were deduced from the residual proton range measured using a multi-layer ion chamber device. In addition, Gafchromic EBT3 films were used to measure the distal dose profiles after irradiation through the tissue samples and compared with those calculated by the treatment planning system using both DECT and SECT predicted SPRs. Results: The differences in SPR between DECT prediction and measurement were −0.31±0.36% for bone, 0.47±0.42% for brain, 0.67±0.15% for liver, 0.51±0.52% for kidney, and −0.96±0.15% for muscle. The corresponding results using SECT were 3.1±0.12%, 1.90±0.45%, −0.66±0.11%, 2.33±0.21%, and −1.70±0.17%. In the film measurements, average distances between film and calculated distal dose profiles were 0.35±0.12 mm for DECT calibration and −1.22±0.12 mm for SECT calibration for a beam with a range of 15.79 cm. Conclusion: Our study indicates that DECT is superior to SECT for proton SPR prediction and has the potential to reduce the range uncertainty to less than 2%. DECT may permit the use of tighter distal and proximal range uncertainty margins for treatment, thereby increasing the precision of proton therapy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

TU-FG-BRB-02: The Impact of Using Dual-Energy CT for Determining Proton Stopping Powers: Comparison Between Theory and Experiments

Purpose: To evaluate the clinical performance of dual-energy CT (DECT) in determining proton stopping power ratios (SPR) and demonstrate advantages over conventional single-energy CT (SECT). Methods: SECT and DECT scans of tissue-equivalent plastics as well as animal meat samples are performed with a Siemens SOMATOM Definition Flash. The methods of Schneider et al. (1996) and Bourque et al. (2014) are used to determine proton SPR on SECT and DECT images, respectively. Waterequivalent path length (WEPL) measurements of plastics and tissue samples are performed with a 195 MeV proton beam. WEPL values are determined experimentally using the depth-dose shift and dose extinction methods. Results: Comparison between CT-based and experimental WEPL is performed for 12 tissue-equivalent plastic as well as 6 meat boxes containing animal liver, kidney, heart, stomach, muscle and bones. For plastic materials, results show a systematic improvement in determining SPR with DECT, with a mean absolute error of 0.4% compared to 1.7% for SECT. For the meat samples, preliminary results show the ability for DECT to determine WEPL with a mean absolute value of 1.1% over all meat boxes. Conclusion: This work demonstrates the potential in using DECT for determining proton SPR with plastic materials in a clinical context. Further work is required to show the benefits of DECT for tissue samples. While experimental uncertainties could be a limiting factor to show the benefits of DECT over SECT for the meat samples, further work is required to adapt the DECT formalism in the context of clinical use, where noise and artifacts play an important role.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

TU-H-BRC-08: Use and Validation of Flexible 3D Printed Tissue Compensators for Post-Mastectomy Radiation Therapy

Purpose: Patient-specific tissue equivalent compensators can be used for post-mastectomy radiation therapy (PMRT) to achieve homogenous dose distributions with single-field treatments. However, current fabrication methods are time consuming and expensive. 3D-printing technology could overcome these limitations. The purposes of this study were to [1] evaluate materials for 3D-printed compensators [2] design and print a compensator to achieve a uniform thickness to a clinical target volume (CTV), and [3] demonstrate that a single-field electron compensator plan is a clinically feasible treatment option for PMRT. Methods: Blocks were printed with three materials; print accuracy, density, Hounsfield units (HU), and percent depth doses (PDD) were evaluated. For a CT scan of an anthropomorphic phantom, we used a ray-tracing method to design a compensator that achieved uniform thickness from compensator surface to CTV. The compensator was printed with flexible tissue equivalent material whose physical and radiological properties were most similar to soft tissue. A single-field electron compensator plan was designed and compared with two standard-of-care techniques. The compensator plan was validated with thermoluminescent dosimeter (TLD) measurements. Results: We identified an appropriate material for 3D-printed compensators that had high print accuracy (99.6%) and was similar to soft tissue; density was 1.04, HU was - 45 ± 43, and PDD curves agreed with clinical curves within 3 mm. We designed and printed a compensator that conformed well to the phantom surface and created a uniform thickness to the CTV. In-house fabrication was simple and inexpensive (<$75). Compared with the two standard plans, the compensator plan resulted in overall more homogeneous dose distributions and performed similarly in terms of lung/heart doses and 90% isodose coverage of the CTV. TLD measurements agreed well with planned doses (within 5 %). Conclusions: We have demonstrated that 3D-printed compensators make single-field electron therapy a clinically feasible treatment option for PMRT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-JeP2-05: Can Automatic Delineation of Cardiac Substructures On Noncontrast CT Be Used for Cardiac Toxicity Analysis?

Purpose: To evaluate the feasibility of using an automatic segmentation tool to delineate cardiac substructures from computed tomography (CT) images for cardiac toxicity analysis for non-small cell lung cancer (NSCLC) patients after radiotherapy. Methods: A multi-atlas segmentation tool developed in-house was used to delineate eleven cardiac substructures including the whole heart, four heart chambers, and six greater vessels automatically from the averaged 4DCT planning images for 49 NSCLC patients. The automatic segmented contours were edited appropriately by two experienced radiation oncologists. The modified contours were compared with the auto-segmented contours using Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate how much modification was needed. In addition, the dose volume histogram (DVH) of the modified contours were compared with that of the auto-segmented contours to evaluate the dosimetric difference between modified and auto-segmented contours. Results: Of the eleven structures, the averaged DSC values ranged from 0.73 ± 0.08 to 0.95 ± 0.04 and the averaged MSD values ranged from 1.3 ± 0.6 mm to 2.9 ± 5.1mm for the 49 patients. Overall, the modification is small. The pulmonary vein (PV) and the inferior vena cava required the most modifications. The V30 (volume receiving 30 Gy or above) for the whole heart and the mean dose to the whole heart and four heart chambers did not show statistically significant difference between modified and auto-segmented contours. The maximum dose to the greater vessels did not show statistically significant difference except for the PV. Conclusion: The automatic segmentation of the cardiac substructures did not require substantial modification. The dosimetric evaluation showed no statistically significant difference between auto-segmented and modified contours except for the PV, which suggests that auto-segmented contours for the cardiac dose response study are feasible in the clinical practice with a minor modification to the PV vessel.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRA-05: PET-Guided Delivery Quality Evaluation of Yttrium-90 Microsphere Radioembolizaton for Hepatocellular Carcinoma Patients: The Optimal Sequence of Radioembolizaton and Chemoembolization Treatments

Purpose: Selective-internal-radiation-therapy (SIRT) and transarterial-chemoembolization (TACE) are commonly used for treatment of liver tumors. The use of TACE, which is macroembolic, prior to SIRT may cause hemodynamic changes in tumor vasculature that impair yttrium-90 (90Y) microsphere delivery to the targeted lesions. This work aims to quantify dosimetric tumor coverage using 90Y positron emission tomography (PET) dosimetry after SIRT alone compared to TACE followed by SIRT. Methods: A total of 40 consecutive hepatocellular carcinoma (HCC) SIRT patients who had a post-SIRT 90Y PET/CT scan were evaluated. The patient-specific-3D-dose was reconstructed from the PET images. Patients were categorized into two groups: patients received TACE prior SIRT procedure (n=18) and patient received SIRT alone (n=22). The lesions and liver were delineated by a senior radiologist. We evaluated both the lesion-specific dose-volume-histogram (DVH) and the selectivity index (SI) defined as the ratio of the average dose inside the total lesion(s) and the average dose of the normal liver. The SI values of patients were compared based on whether TACE was previously used. Results: A wide spectrum was observed in the lesion-specific DVH-evaluation and SI appeared to be suitable of evaluating the quality of each SIRT infusion. The average SI of the entire patient group was 3.0, i.e. targeted lesion receiving three times higher dose than normal liver. The average SI was 1.8 for patients who had prior TACE and 3.9 for patients who did not have prior TACE (p=0.008). 85% of the patients with prior TACE demonstrated poor 90Y-microsphere delivery (SI <2) while none demonstrated excellent delivery (SI >4). On the other hand, the incidence SI >4 among patients with no prior TACE was 37%. Conclusion: 3D dose evaluation using post-SIRT PET suggests that 90Y microsphere delivery to liver tumors is impaired among patients who received prior TACE compared to those who receive SIRT alone.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-202-09: Feasibility and Quantitative Analysis of 4DCT-Based High Precision Lung Elastography

Purpose: The purpose of this project is to derive high precision elastography measurements from 4DCT lung scans to facilitate the implementation of elastography in a radiotherapy context. Methods: 4DCT scans of the lungs were acquired, and breathing stages were subsequently registered to each other using an optical flow DIR algorithm. The displacement of each voxel gleaned from the registration was taken to be the ground-truth deformation. These vectors, along with the 4DCT source datasets, were used to generate a GPU-based biomechanical simulation that acted as a forward model to solve the inverse elasticity problem. The lung surface displacements were applied as boundary constraints for the model-guided lung tissue elastography, while the inner voxels were allowed to deform according to the linear elastic forces within the model. A biomechanically-based anisotropic convergence magnification technique was applied to the inner voxels in order to amplify the subtleties of the interior deformation. Solving the inverse elasticity problem was accomplished by modifying the tissue elasticity and iteratively deforming the biomechanical model. Convergence occurred when each voxel was within 0.5 mm of the ground-truth deformation and 1 kPa of the ground-truth elasticity distribution. To analyze the feasibility of the model-guided approach, we present the results for regions of low ventilation, specifically, the apex. Results: The maximum apical boundary expansion was observed to be between 2 and 6 mm. Simulating this expansion within an apical lung model, it was observed that 100% of voxels converged within 0.5 mm of ground-truth deformation, while 91.8% converged within 1 kPa of the ground-truth elasticity distribution. A mean elasticity error of 0.6 kPa illustrates the high precision of our technique. Conclusion: By utilizing 4DCT lung data coupled with a biomechanical model, high precision lung elastography can be accurately performed, even in low ventilation regions of the lungs. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1144087.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207A-03: A CBCT Head Scanner for Point-Of-Care Imaging of Intracranial Hemorrhage

Purpose: This work reports the design, development, and first technical assessment of a cone-beam CT (CBCT) scanner developed specifically for imaging of acute intracranial hemorrhage (ICH) at the point of care, with target applications in diagnosis and monitoring of traumatic brain injury, stroke, and postsurgical hemorrhage. Methods: System design employed a task-based image quality model to quantify the influence of factors such as additive noise and high-gain (HG) detector readout on ICH detectability. Three bowtie filters with varying bare-beam attenuation strength and curvature were designed to enable HG readout without detector saturation, and a polyenergetic gain correction was developed to minimize artifacts from bowtie flood-field calibration. Image reconstruction used an iterative penalized weighted least squares (PWLS) method with artifact correction including Monte Carlo scatter estimation, Joseph-Spital beam hardening correction, and spatiotemporal deconvolution of detector glare and lag. Radiation dose was characterized for half-scan and full-scan protocols at various kV, and imaging performance was assessed in a head phantom presenting simulated ICH with diameter ranging 2–12 mm. Results: The image quality model guided system design and was validated by measurements on a CBCT imaging bench. Compared to low-gain readout without a bowtie filter, the combination of HG readout and a modest bowtie improved the contrast-to-noise ratio (CNR per unit square-root dose) by 20% in the center of the image but degraded noise performance near the periphery (20% reduction in CNR). Low-frequency bowtie artifacts (∼100 HU magnitude) were corrected by the polyenergetic gain correction. Image reconstructions on the prototype scanner demonstrate clear visibility of the smallest ICH insert (2 mm diameter) in both HG readout (with a bowtie) and dual-gain readout (without bowtie). Conclusion: Technical assessment of the prototype scanner suggests the capability for reliable visualization of small (2 mm), low-contrast (50 HU) ICH at <20 mGy dose and motivates translation to clinical studies, now underway. Xiaohui Wang and David Foos (co-authors) are employees of Carestream Health. Research funding support from Carestream Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207A-04: Random Undersampled Cone Beam CT: Theoretical Analysis and a Novel Reconstruction Method

Purpose: Reducing x-ray exposure and speeding up data acquisition motived studies on projection data undersampling. It is an important question that for a given undersampling ratio, what the optimal undersampling approach is. In this study, we propose a new undersampling scheme: random-ray undersampling. We will mathematically analyze its projection matrix properties and demonstrate its advantages. We will also propose a new reconstruction method that simultaneously performs CT image reconstruction and projection domain data restoration. Methods: By representing projection operator under the basis of singular vectors of full projection operator, matrix representations for an undersampling case can be generated and numerical singular value decomposition can be performed. We compared properties of matrices among three undersampling approaches: regular-view undersampling, regular-ray undersampling, and the proposed random-ray undersampling. To accomplish CT reconstruction for random undersampling, we developed a novel method that iteratively performs CT reconstruction and missing projection data restoration via regularization approaches. Results: For a given undersampling ratio, random-ray undersampling preserved mathematical properties of full projection operator better than the other two approaches. This translates to advantages of reconstructing CT images at lower errors. Different types of image artifacts were observed depending on undersampling strategies, which were ascribed to the unique singular vectors of the sampling operators in the image domain. We tested the proposed reconstruction algorithm on a Forbid phantom with only 30% of the projection data randomly acquired. Reconstructed image error was reduced from 9.4% in a TV method to 7.6% in the proposed method. Conclusion: The proposed random-ray undersampling is mathematically advantageous over other typical undersampling approaches. It may permit better image reconstruction at the same undersampling ratio. The novel algorithm suitable for this random-ray undersampling was able to reconstruct high-quality images.

60 APPLIED LIFE SCIENCES↗