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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

WE-E-17A-03: FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

Purpose: An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research. Methods: We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H and N) cancer patients with stage III–IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV). Results: For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H and N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H and N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites. Conclusion: PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-17A-06: Assessing the Scale of Tumor Heterogeneity by Complete Hierarchical Segmentation On MRI

Purpose: In many cancers, intratumoral heterogeneity exists in vascular and genetic structure. We developed an algorithm which uses clinical imaging to interrogate different scales of heterogeneity. We hypothesize that heterogeneity of perfusion at large distance scales may correlate with propensity for disease recurrence. We applied the algorithm to initial diagnosis MRI of rhabdomyosarcoma patients to predict recurrence. Methods: The Spatial Heterogeneity Analysis by Recursive Partitioning (SHARP) algorithm recursively segments the tumor image. The tumor is repeatedly subdivided, with each dividing line chosen to maximize signal intensity difference between the two subregions. This process continues to the voxel level, producing segments at multiple scales. Heterogeneity is measured by comparing signal intensity histograms between each segmented region and the adjacent region. We measured the scales of contrast enhancement heterogeneity of the primary tumor in 18 rhabdomyosarcoma patients. Using Cox proportional hazards regression, we explored the influence of heterogeneity parameters on relapse-free survival (RFS). To compare with existing methods, fractal and Haralick texture features were also calculated. Results: The complete segmentation produced by SHARP allows extraction of diverse features, including the amount of heterogeneity at various distance scales, the area of the tumor with the most heterogeneity at each scale, and for a given point in the tumor, the heterogeneity at different scales. 10/18 rhabdomyosarcoma patients suffered disease recurrence. On contrast-enhanced MRI, larger scale of maximum signal intensity heterogeneity, relative to tumor diameter, predicted for shorter RFS (p=0.05). Fractal dimension, fractal fit, and three Haralick features did not predict RFS (p=0.09-0.90). Conclusion: SHARP produces an automatic segmentation of tumor regions and reports the amount of heterogeneity at various distance scales. In rhabdomyosarcoma, RFS was shorter when the primary tumor exhibited larger scale of heterogeneity on contrast-enhanced MRI. If validated on a larger dataset, this imaging biomarker could be useful to help personalize treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-G-9A-01: Radiation Oncology Outcomes Informatics

The construction of databases and support software to enable routine and systematic aggregation, analysis and reporting of patient outcomes data is emerging as an important area. “How have results for our patients been affected by the improvements we have made in our practice and in the technologies we use?” To answer this type of fundamental question about the overall pattern of efficacy observed, it is necessary to systematically gather and analyze data on all patients treated within a clinic. Clinical trials answer, in great depth and detail, questions about outcomes for the subsets of patients enrolled in a given trial. However, routine aggregation and analysis of key treatment parameter data and outcomes information for all patients is necessary to recognize emergent patterns that would be of interest from a public health or practice perspective and could better inform design of clinical trials or the evolution of best practice principals. To address these questions, Radiation Oncology outcomes databases need to be constructed to enable combination essential data from a broad group of data types including: diagnosis and staging, dose volume histogram metrics, patient reported outcomes, toxicity metrics, performance status, treatment plan parameters, demographics, DICOM data and demographics. Developing viable solutions to automate aggregation and analysis of this data requires multidisciplinary efforts to define nomenclatures, modify clinical processes and develop software and database tools requires detailed understanding of both clinical and technical issues. This session will cover the developing area of Radiation Oncology Outcomes Informatics. Learning Objectives: Audience will be able to speak to the technical requirements (software, database, web services) which must be considered in designing an outcomes database. Audience will be able to understand the content and the role of patient reported outcomes as compared to traditional toxicity measures. Audience will be understand approaches, clinical process changes, consensus building efforts and standardizations which must be addressed to succeed in a multi-disciplinary effort to aggregate data for all patients. Audience will be able to discuss technical and process issues related to pooling data among institutions in the context of collaborative studies among the presenting institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-A-19A-12: A GPU-Accelerated and Monte Carlo-Based Intensity Modulated Proton Therapy Optimization System

Purpose: To develop a clinically applicable intensity modulated proton therapy (IMPT) optimization system that utilizes more accurate Monte Carlo (MC) dose calculation, rather than analytical dose calculation. Methods: A very fast in-house graphics processing unit (GPU) based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified gradient based optimization method was used to achieve the desired dose volume histograms (DVH). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve the spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that Result from maintaining the intrinsic CT resolution and large number of proton spots. The dose effects were studied particularly in cases with heterogeneous materials in comparison with the commercial treatment planning system (TPS). Results: For a relatively large and complex three-field bi-lateral head and neck case (i.e. >100K spots with a target volume of ∼1000 cc and multiple surrounding critical structures), the optimization together with the initial MC dose influence map calculation can be done in a clinically viable time frame (i.e. less than 15 minutes) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The DVHs of the MC TPS plan compare favorably with those of a commercial treatment planning system. Conclusion: A GPU accelerated and MC-based IMPT optimization system was developed. The dose calculation and plan optimization can be performed in less than 15 minutes on a hardware system costing less than 45,000 dollars. The fast calculation and optimization makes the system easily expandable to robust and multi-criteria optimization. This work was funded in part by a grant from Varian Medical Systems, Inc.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

TH-A-9A-08: Knowledge-Based Quality Control of Clinical Stereotactic Radiosurgery Treatment Plans

Purpose: To develop a quality control tool to reduce stereotactic radiosurgery (SRS) planning variability using models that predict achievable plan quality metrics (QMs) based on individual patient anatomy. Methods: Using a knowledge-based methodology that quantitatively correlates anatomical geometric features to resultant organ-at-risk (OAR) dosimetry, we developed models for predicting achievable OAR dose-volume histograms (DVHs) by training with a cohort of previously treated SRS patients. The DVH-based QMs used in this work are the gradient measure, GM=(3/4pi)^1/3*[V50%^1/3−V100%^1/3], and V10Gy of normal brain. As GM quantifies the total rate of dose fall-off around the planning target volume (PTV), all voxels inside the patient's body contour were treated as OAR for DVH prediction. 35 previously treated SRS plans from our institution were collected; all were planned with non-coplanar volumetric-modulated arc therapy to prescription doses of 12–25 Gy. Of the 35-patient cohort, 15 were used for model training and 20 for model validation. Accuracies of the predictions were quantified by the mean and the standard deviation of the difference between clinical and predicted QMs, δQM=QM-clin−QM-pred. Results: Best agreement between predicted and clinical QMs was obtained when models were built separately for V-PTV<2.5cc and V-PTV>2.5cc. Eight patients trained the V-PTV<2.5cc model and seven patients trained the V-PTV>2.5cc models, respectively. The mean and the standard deviation of δGM were 0.3±0.4mm for the training sets and −0.1±0.6mm for the validation sets, demonstrating highly accurate GM predictions. V10Gy predictions were also highly accurate, with δV10Gy=0.8±0.7cc for the training sets and δV10Gy=0.7±1.4cc for the validation sets. Conclusion: The accuracy of the models in predicting two key SRS quality metrics highlights the potential of this technique for quality control for SRS treatments. Future investigations will seek to determine whether QM variations are due to residual model inaccuracies or true plan quality variations in the clinical sample. Support from Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-BRD-08: Reducing the Effect of Respiratory Motion On the Delivered Dose in Proton Therapy Through Proper Field Angle Selection

Purpose: This work investigated a novel planning strategy of selecting radiotherapy beam angles that minimizes the change in water equivalent thickness (dWET) during respiration in order to reduce the effects of respiratory motion in passively scattered proton therapy (PSPT). Methods: In a clinical trial treating locally-advanced lung cancer with proton therapy, 2–4 co-planar beams were previously selected by dosimetrists in the design of physician-approved PSPT treatment plans. The authors identified a cohort of patients in which respiratory motion affected the planned PSPT dose delivery. For this cohort, this work analyzed dWET during respiration over a 360 degree arc of potential treatment angles around the patient: the dWET was defined as the difference in WET between the full-exhale (T50) and full-inhale (T0) phases of the simulation 4DCT. New PSPT plans were redesigned by selecting new beam angles that demonstrated significant reduction in the value of dWET. Between the T50 and T0 phases, the root-mean-square deviation of dose and the change in dose-volume histogram curves (dAUC) for anatomical structures were calculated to compare the original to dWET reduction plans. Results: To date, three plans were retrospectively redesigned based on dWET analysis. In the dWET reduction plan, the root mean square dose (T50-T0) was reduced by 15–35% and the DVH dAUC values were reduced by more than 60%.The PSPT plans redesigned by selecting appropriate field angles to minimize dWET demonstrated less dosimetric variation due to respiration. Conclusion: We have introduced the use of a new metric to quantify respiratory motion in proton therapy: dWET. The use of dWET allows us to minimize the impact of respiratory motion of the entire anatomy in the beam path. This work is a proof of principle that dWET could suggest field angles in proton therapy that are more robust to the effects of respiratory motion.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-C-BRD-09: Successes and Limitations of Online Range Adaptive Spot Scanning Proton Therapy for NSCLC

Purpose: To determine the ability to adapt discrete spot-scanning proton therapy (SSPT) plans based on geometric changes of anatomy to minimize normal tissue dose and maintain target coverage. Methods: We developed and tested a range-correction algorithm to compensate for anatomy changes in SSPT with correction factors for target lateral size changes and energy scaling. This algorithm adjusts the energy of each spot from the original optimized treatment plan to match the new daily anatomy based on water equivalent path-length. To correct for the lateral target size changes, the peripheral spots were scaled based on phantom studies with variable target size. For energy change corrections, alternative cumulative scaling factor lookup tables were generated based on calculated central-axis and integral depth dose calculations for different energies. These various adaptive algorithms were performed on 7 lung cancer patients that were previously treated with proton therapy and who required at least one adaptive intervention. Single-field optimized SSPT plans were generated for these patients with clinical beam angles. Dose-volume histogram metrics were obtained for these patients for both the non-adaptive and the different adaptive plans applied to the last available weekly CT scan. Results: The doses to normal tissue were largely reduced for the spinal cord (Dmax), total lung (V20Gy), and contralateral lung (V20Gy) for all different methods of adaptive planning. With both corrections applied, the average changes for these metrics were −6.2Gy, −2.7%, and −4.9%, respectively. The same method generated unacceptably high target hot spots with average target V110% increase of 12.3%. Conclusion: Adaptive methods based on direct adjustments to proton range can reduce normal tissue doses under large anatomical changes but are insufficient in achieving clinically acceptable target doses and generate unacceptably sizeable hot spots. Adaptive planning methods for proton therapy will likely necessitate some aspect of reoptimization for acceptable clinical utility.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core-to-Core Program(No. 23003) and Grant-in-aid from the JSPS Fellows.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-C-17A-01: MRI-Based Radiotherapy Treatment Planning In Pelvis

Purpose: To support radiotherapy dose calculation, synthetic CT (MRCT) image volumes need to represent the electron density of tissues with sufficient accuracy. This study compares CT and MRCT for pelvic radiotherapy. Methods: CT and multi-contrast MRI acquired using T1- based Dixon, T2 TSE, and PETRA sequences were acquired on an IRBapproved protocol patient. A previously published method was used to create a MRCT image volume by applying fuzzy classification on T1- weighted and calculated water image volumes (air and fluid voxels were excluded using thresholds applied to PETRA and T2-weighted images). The correlation of pelvic bone intensity between CT and MRCT was investigated. Two treatment plans, based on CT and MRCT, were performed to mimic treatment for: (a) pelvic bone metastasis with a 16MV parallel beam arrangement, and (b) gynecological cancer with 6MV volumetric modulated arc therapy (VMAT) using two full arcs. The CT-calculated fluence maps were used to recalculate doses using the MRCT-derived density grid. The dose-volume histograms and dose distributions were compared. Results: Bone intensities in the MRCT volume correlated linearly with CT intensities up to 800 HU (containing 96% of the bone volume), and then decreased with CT intensity increase (4% volume). There was no significant difference in dose distributions between CT- and MRCTbased plans, except for the rectum and bladder, for which the V45 differed by 15% and 9%, respectively. These differences may be attributed to normal and visualized organ movement and volume variations between CT and MR scans. Conclusion: While MRCT had lower bone intensity in highly-dense bone, this did not cause significant dose deviations from CT due to its small percentage of volume. These results indicate that treatment planning using MRCT could generate comparable dose distributions to that using CT, and further demonstrate the feasibility of using MRI-alone to support Radiation Oncology workflow. NIH R01EB016079.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Independent dosimetric assessment of the model EP917 episcleral brachytherapy plaque

Purpose: To investigate the influence of slot design on dose distributions and dose-volume histograms (DVHs) for the model EP917 plaque for episcleral brachytherapy. Methods: Dimensions and orientations of the slots were measured for three model EP917 plaques and compared to data in the Plaque Simulator (PS) treatment planning software (version 5.7.6). These independently determined coordinates were incorporated into the MCNP Monte Carlo simulation environment to obtain dose from the plaques in a water environment and in a clinical environment with ocular structures. A tumor volume was simulated as 5 mm in apical height and 11 mm in basal diameter. Variations in plaque mass density and composition; slot length, width, and depth; seed positioning; and Ag-marker rod positioning were simulated to examine their influence on plaque central axis (CAX) and planar dose distributions, and DVHs. Results: Seed shifts in a single slot toward the eye and shifts of the{sup 125}I-coated Ag rod within the capsule had the greatest impact on CAX dose distribution. A shift of 0.0994 mm toward the eye increased dose by 14%, 9%, 4.3%, and 2.7% at 1, 2, 5, and 10 mm, respectively, from the inner sclera. When examining the fully-modeled plaque in the ocular geometry, the largest dose variations were caused by shifting the Ag rods toward the sclera and shifting the seeds away from the globe when the slots were made 0.51 mm deeper, causing +34.3% and −69.4% dose changes to the outer sclera, respectively. At points along the CAX, dose from the full plaque geometry using the measured slot design was 2.4% ± 1.1% higher than the manufacturer-provided slot design and 2.2% ± 2.3% higher than the homogeneous calculation of PS treatment planning results. The ratio of D{sub 10} values for the measured slot design to the D{sub 10} values for the manufacturer-provided slot design was higher by 9%, 10%, and 19% for the tumor, inner sclera, and outer sclera, respectively. In comparison to the measured slot design, a theoretical plaque having narrower and deeper slots delivered 30%, 37%, and 62% lower D{sub 10} doses to the tumor, inner sclera, and outer sclera, respectively. Conclusions: While the measured positions of the slots on the model EP917 plaque were in close agreement (<0.7 mm) with the PS values, small differences in the slot shape caused substantial differences in dose distributions and DVH metrics. Increasing slot depth by 0.1 mm decreased outer scleral dose by 20%, yet shifting the Ag rods in the seeds toward the globe by 0.1 mm increased outer scleral dose by 35%. The clinical medical physicist is advised to measure these types of plaques upon acceptance testing before clinical use to inspect slot shape and position for comparison with data used for treatment planning purposes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Comparison of internal dose estimates obtained using organ-level, voxel S value, and Monte Carlo techniques

Purpose: The authors’ objective was to compare internal dose estimates obtained using the Organ Level Dose Assessment with Exponential Modeling (OLINDA/EXM) software, the voxel S value technique, and Monte Carlo simulation. Monte Carlo dose estimates were used as the reference standard to assess the impact of patient-specific anatomy on the final dose estimate. Methods: Six patients injected with{sup 99m}Tc-hydrazinonicotinamide-Tyr{sup 3}-octreotide were included in this study. A hybrid planar/SPECT imaging protocol was used to estimate {sup 99m}Tc time-integrated activity coefficients (TIACs) for kidneys, liver, spleen, and tumors. Additionally, TIACs were predicted for {sup 131}I, {sup 177}Lu, and {sup 90}Y assuming the same biological half-lives as the {sup 99m}Tc labeled tracer. The TIACs were used as input for OLINDA/EXM for organ-level dose calculation and voxel level dosimetry was performed using the voxel S value method and Monte Carlo simulation. Dose estimates for {sup 99m}Tc, {sup 131}I, {sup 177}Lu, and {sup 90}Y distributions were evaluated by comparing (i) organ-level S values corresponding to each method, (ii) total tumor and organ doses, (iii) differences in right and left kidney doses, and (iv) voxelized dose distributions calculated by Monte Carlo and the voxel S value technique. Results: The S values for all investigated radionuclides used by OLINDA/EXM and the corresponding patient-specific S values calculated by Monte Carlo agreed within 2.3% on average for self-irradiation, and differed by as much as 105% for cross-organ irradiation. Total organ doses calculated by OLINDA/EXM and the voxel S value technique agreed with Monte Carlo results within approximately ±7%. Differences between right and left kidney doses determined by Monte Carlo were as high as 73%. Comparison of the Monte Carlo and voxel S value dose distributions showed that each method produced similar dose volume histograms with a minimum dose covering 90% of the volume (D90) agreeing within ±3%, on average. Conclusions: Several aspects of OLINDA/EXM dose calculation were compared with patient-specific dose estimates obtained using Monte Carlo. Differences in patient anatomy led to large differences in cross-organ doses. However, total organ doses were still in good agreement since most of the deposited dose is due to self-irradiation. Comparison of voxelized doses calculated by Monte Carlo and the voxel S value technique showed that the 3D dose distributions produced by the respective methods are nearly identical.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Poster — Thur Eve — 32: Stereotactic Body Radiation Therapy for Peripheral Lung Lesion: Treatment Planning and Quality Assurance

Stereotactic body radiation therapy (SBRT), due to its high precision for target localizing, has become widely used to treat tumours at various locations, including the lungs. Lung SBRT program was started at our institution a year ago. Eighteen patients with peripheral lesions up to 3 cm diameter have been treated with 48 Gy in 4 fractions. Based on four-dimensional computed tomography (4DCT) simulation, internal target volume (ITV) was delineated to encompass the respiratory motion of the lesion. A margin of 5 mm was then added to create the planning target volume (PTV) for setup uncertainties. There was no expansion from gross tumour volume (GTV) to clinical target volume (CTV). Pinnacle 9.6 was used as the primary treatment planning system. Volumetric modulated arc therapy (VMAT) technique, with one or two coplanar arcs, generally worked well. For quality assurance (QA), each plan was exported to Eclipse 10 and dose calculation was repeated. Dose volume histograms (DVHs) of the targets and organs at risk (OARs) were then compared between the two treatment planning systems. Winston-Lutz tests were carried out as routine machine QA. Patient-specific QA included ArcCheck measurement with an insert, where an ionization chamber was placed at the centre to measure dose at the isocenter. For the first several patients, and subsequently for the plans with extremely strong modulation, Gafchromic film dosimetry was also employed. For each patient, a mock setup was scheduled prior to treatments. Daily pre- and post-CBCT were acquired for setup and assessment of intra-fractional motion, respectively.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Poster — Thur Eve — 35: The impact of intensity- and energy-modulated photon radiotherapy (XMRT) optimization on a variety of organ geometries

We previously reported on a novel, modulated in both energy and intensity; photon radiotherapy (XMRT) optimization technique. The purpose of this investigation was to test this XMRT optimization against conventional intensity modulated radiotherapy (IMRT) optimization on four different organ test geometries. All geometries mimicked clinically relevant scenarios. Both IMRT and XMRT were based on a linear programming approach where the objective function was the mean dose to healthy organs and organ-specific linear dose-point constraints were used. For IMRT, the beam energy was fixed to 6 MV while XMRT optimized in terms of both 6 and 18 MV beams. All plans consisted of a seven beam coplanar arrangement. All organ geometries were contoured on a 25cm diameter cylindrical water phantom in open source radiotherapy research software known as CERR. Solutions for both IMRT and XMRT were obtained for each geometry using a numerical solver Gurobi. Analyzing the quality of the solutions was done by comparing dose distributions and dose volume histograms calculated using CERR. For all four geometries, IMRT and XMRT solutions were comparable in terms of target coverage. For two of the geometries, IMRT provided an advantage in terms of reduced dose to the healthy structures. XMRT showed improved dose reduction to healthy organs for one geometry and a comparable dose distribution to IMRT for the remaining geometry. The inability to exploit the benefits of using multiple energies may be attributed to limited water phantom diameter and having the majority of the organs in close proximity to the transverse axis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Sci—Thur AM: YIS - 11: Estimation of Bladder-Wall Cumulative Dose in Multi-Fraction Image-Based Gynaecological Brachytherapy Using Deformable Point Set Registration

Dosimetric parameters based on dose-volume histograms (DVH) of contoured structures are routinely used to evaluate dose delivered to target structures and organs at risk. However, the DVH provides no information on the spatial distribution of the dose in situations of repeated fractions with changes in organ shape or size. The aim of this research was to develop methods to more accurately determine geometrically localized, cumulative dose to the bladder wall in intracavitary brachytherapy for cervical cancer. The CT scans and treatment plans of 20 cervical cancer patients were used. Each patient was treated with five high-dose-rate (HDR) brachytherapy fractions of 600cGy prescribed dose. The bladder inner and outer surfaces were delineated using MIM Maestro software (MIM Software Inc.) and were imported into MATLAB (MathWorks) as 3-dimensional point clouds constituting the “bladder wall”. A point-set registration toolbox for MATLAB, Coherent Point Drift (CPD), was used to non-rigidly transform the bladder-wall points from four of the fractions to the coordinate system of the remaining (reference) fraction, which was chosen to be the emptiest bladder for each patient. The doses were accumulated on the reference fraction and new cumulative dosimetric parameters were calculated. The LENT-SOMA toxicity scores of these patients were studied against the cumulative dose parameters. Based on this study, there was no significant correlation between the toxicity scores and the determined cumulative dose parameters.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Poster — Thur Eve — 15: Improvements in the stability of the tomotherapy imaging beam

Use of helical TomoTherapy based MVCT imaging for adaptive planning requires the image values (HU) to remain stable over the course of treatment. In the past, the image value stability was suboptimal, which required frequent change to the image value to density calibration curve to avoid dose errors on the order of 2–4%. The stability of the image values at our center was recently improved by stabilizing the dose rate of the machine (dose control servo) and performing daily MVCT calibration corrections. In this work, we quantify the stability of the image values over treatment time by comparing patient treatment image density derived using MVCT and KVCT. The analysis includes 1) MVCT - KVCT density difference histogram, 2) MVCT vs KVCT density spectrum, 3) multiple average profile density comparison and 4) density difference in homogeneous locations. Over two months, the imaging beam stability was compromised several times due to a combination of target wobbling, spectral calibration, target change and magnetron issues. The stability of the image values were analyzed over the same period. Results show that the impact on the patient dose calculation is 0.7% +− 0.6%.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Dosimetric feasibility of real-time MRI-guided proton therapy

Purpose: Magnetic resonance imaging (MRI) is a prime candidate for image-guided radiotherapy. This study was designed to assess the feasibility of real-time MRI-guided proton therapy by quantifying the dosimetric effects induced by the magnetic field in patients’ plans and identifying the associated clinical consequences. Methods: Monte Carlo dose calculation was performed for nine patients of various treatment sites (lung, liver, prostate, brain, skull-base, and spine) and tissue homogeneities, in the presence of 0.5 and 1.5 T magnetic fields. Dose volume histogram (DVH) parameters such as D{sub 95}, D{sub 5}, and V{sub 20} as well as equivalent uniform dose were compared for the target and organs at risk, before and after applying the magnetic field. The authors further assessed whether the plans affected by clinically relevant dose distortions could be corrected independent of the planning system. Results: By comparing the resulting dose distributions and analyzing the respective DVHs, it was determined that despite the observed lateral beam deflection, for magnetic fields of up to 0.5 T, neither was the target coverage jeopardized nor was the dose to the nearby organs increased in all cases except for prostate. However, for a 1.5 T magnetic field, the dose distortions were more pronounced and of clinical concern in all cases except for spine. In such circumstances, the target was severely underdosed, as indicated by a decrease in D{sub 95} of up to 41% of the prescribed dose compared to the nominal situation (no magnetic field). Sites such as liver and spine were less affected due to higher tissue homogeneity, typically smaller beam range, and the choice of beam directions. Simulations revealed that small modifications to certain plan parameters such as beam isocenter (up to 19 mm) and gantry angle (up to 10°) are sufficient to compensate for the magnetic field-induced dose disturbances. The authors’ observations indicate that the degree of required corrections strongly depends on the beam range and direction relative to the magnetic field. This method was also applicable to more heterogeneous scenarios such as skull-base tumors. Conclusions: This study confirmed the dosimetric feasibility of real-time MRI-guided proton therapy and delivering a clinically acceptable dose to patients with various tumor locations within magnetic fields of up to 1.5 T. This work could serve as a guide and encouragement for further efforts toward clinical implementation of hybrid MRI–proton gantry systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system

Purpose: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. Methods: An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. Results: For relatively large and complex three-field head and neck cases, i.e., >100 000 spots with a target volume of ∼1000 cm{sup 3} and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. Conclusions: A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45 000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Segmentation-free empirical beam hardening correction for CT

Purpose: The polychromatic nature of the x-ray beams and their effects on the reconstructed image are often disregarded during standard image reconstruction. This leads to cupping and beam hardening artifacts inside the reconstructed volume. To correct for a general cupping, methods like water precorrection exist. They correct the hardening of the spectrum during the penetration of the measured object only for the major tissue class. In contrast, more complex artifacts like streaks between dense objects need other techniques of correction. If using only the information of one single energy scan, there are two types of corrections. The first one is a physical approach. Thereby, artifacts can be reproduced and corrected within the original reconstruction by using assumptions in a polychromatic forward projector. These assumptions could be the used spectrum, the detector response, the physical attenuation and scatter properties of the intersected materials. A second method is an empirical approach, which does not rely on much prior knowledge. This so-called empirical beam hardening correction (EBHC) and the previously mentioned physical-based technique are both relying on a segmentation of the present tissues inside the patient. The difficulty thereby is that beam hardening by itself, scatter, and other effects, which diminish the image quality also disturb the correct tissue classification and thereby reduce the accuracy of the two known classes of correction techniques. The herein proposed method works similar to the empirical beam hardening correction but does not require a tissue segmentation and therefore shows improvements on image data, which are highly degraded by noise and artifacts. Furthermore, the new algorithm is designed in a way that no additional calibration or parameter fitting is needed. Methods: To overcome the segmentation of tissues, the authors propose a histogram deformation of their primary reconstructed CT image. This step is essential for the proposed algorithm to be segmentation-free (sf). This deformation leads to a nonlinear accentuation of higher CT-values. The original volume and the gray value deformed volume are monochromatically forward projected. The two projection sets are then monomially combined and reconstructed to generate sets of basis volumes which are used for correction. This is done by maximization of the image flatness due to adding additionally a weighted sum of these basis images. sfEBHC is evaluated on polychromatic simulations, phantom measurements, and patient data. The raw data sets were acquired by a dual source spiral CT scanner, a digital volume tomograph, and a dual source micro CT. Different phantom and patient data were used to illustrate the performance and wide range of usability of sfEBHC across different scanning scenarios. The artifact correction capabilities are compared to EBHC. Results: All investigated cases show equal or improved image quality compared to the standard EBHC approach. The artifact correction is capable of correcting beam hardening artifacts for different scan parameters and scan scenarios. Conclusions: sfEBHC generates beam hardening-reduced images and is furthermore capable of dealing with images which are affected by high noise and strong artifacts. The algorithm can be used to recover structures which are hardly visible inside the beam hardening-affected regions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗