bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐F‐J‐31: Dual Energy Imaging in the Treatment of Head and Neck Tumors: A Phantom Study
Authors: Roeske J , Patel R , Surucu M 
Published: July 2016

Purpose:Organ motion, due to swallowing, can significantly affect the PTV margins used in head and neck patients. Evaluation of swallowing motion 2016 AAPM Annual Meeting Program Medical Physics, Vol. 43, No. 6, June 2016 3412 3412 may be performed using x‐ray fluoroscopy. However, regions such as the soft palette and epiglottis may be difficult to visualize due to the presence of bone (mandible, teeth). The goal of this phantom study is to determine if dual energy (DE) subtraction would improve the visualization in these regions.Methods:An Alderson head/neck anthropomorphic phantom was positioned on the treatment table of the linac (iX, Varian Medical Systems, Palo Alto, CA). Lateral single energy (SE) kV images of the phantom were obtained with the On‐Board Imager (OBI) using our standard protocol (70 kVp, 3.2 mAs). Subsequently, DE images were obtained sequentially at 60 kVp and 120 kVp. The mAs settings for the individual DE component images were determined by varying the x‐ray exposure time, while maintaining a constant tube current, with the goal of producing a dose at the surface that was comparable to the SE dose. Subtraction images were created offline using in‐house software.Results:Optimal DE images were produced using 120 kVp (0.5 mAs) and 60 kVp (2 mAs). The DE images showed good subtraction of the overlying bony anatomy. In particular, subtraction of the mandible resulted in improved visualization of the airway and soft palette. An analysis of the relative contrast of the soft palette showed an increase of 25–400% using DE vs. SE. The imaging dose at the surface was estimated as 0.22 mGy using SE and 0.23 mGy using DE.Conclusion:DE imaging may provide an effective means for visualizing the soft palette and epiglottis in head and neck cancer patients. Combining such an approach with fluoroscopy may provide data on organ motion due to swallowing that may be incorporated in PTV design.
SU‐F‐J‐38: Dose Rates and Preliminary Evaluation of Contouring Similarity Metrics Using 4D Cone Beam CT
Authors: Santoso A , Song K , Qin Y , Gardner S , Liu C , Cattaneo R , Chetty I , Movsas B , Aljouni M , Wen N 
Published: July 2016

Purpose:4D imaging modalities require detailed characterization for clinical optimization. The On‐Board Imager mounted on the linear accelerator was used to investigate dose rates in a tissue mimicking phantom using 4D‐CBCT and assess variability of contouring similarity metrics between 4D‐CT and 4D‐CBCT retrospective reconstructions.Methods:A 125 kVp thoracic protocol was used. A phantom placed on a motion platform simulated a patient's breathing cycle. An ion chamber was affixed inside the phantom's tissue mimicking cavities (i.e. bone, lung, and soft tissue). A sinusoidal motion waveform was executed with a five second period and superior‐inferior motion. Dose rates were measured at six ion chamber positions. A preliminary workflow for contouring similarity between 4D‐CT and 4D‐CBCT was established using a single lung SBRT patient's historical data. Average intensity projection (Ave‐IP) and maximum intensity projection (MIP) reconstructions generated offline were compared between the 4D modalities. Similarity metrics included Dice similarity coefficient (DSC), Hausdorff distance, and center of mass (COM) deviation. Two isolated lesions were evaluated in the patient's scans: one located in the right lower lobe (ITVRLL) and one located in the left lower lobe (ITVLLL).Results:Dose rates ranged from 2.30 (lung) to 5.18 (bone) E‐3 cGy/mAs. For fixed acquisition parameters, cumulative dose is inversely proportional to gantry speed. For ITVRLL, DSC were 0.70 and 0.68, Hausdorff distances were 6.11 and 5.69 mm, and COM deviations were 1.24 and 4.77 mm, for Ave‐IP and MIP respectively. For ITVLLL, DSC were 0.64 and 0.75, Hausdorff distances were 10.74 and 8.00 mm, and COM deviations were 7.55 and 4.3 mm, for Ave‐IP and MIP respectively.Conclusion:While the dosimetric output of 4D‐CBCT is low, characterization is necessary to assure clinical optimization. A basic workflow for comparison of simulation and treatment 4D image‐based contours was established.This work was partially supported by a Research Scholar Grant (RSG‐15‐137‐01‐CCE) from the American Cancer Society.
SU‐F‐J‐46: Feasibility of Cerenkov Emission for Absorption Spectroscopy
Authors: Oraiqat I , Rehemtulla A , Lam K , Clarke R , Ten Haken R , El Naqa I 
Published: July 2016

Purpose:Cerenkov emission (CE) is a promising tool for online tumor microenvironment interrogation and targeting during radiotherapy. In this work, we utilize CE generated during radiotherapy as a broadband excitation source for real‐time absorption spectroscopy. We demonstrate the feasibility of CE spectroscopy using a controlled experiment of materials with known emission/absorption properties.Methods:A water tank is irradiated with 20 MeV electron beam to induce Cerenkov emission. Food coloring dyes (Yellow #5, Red #40, and Blue #1), which have known emission/absorption properties were added to the water tank with increasing concentration (1 drop (0.05 mL), 2 drops, and 4 drops from a dispenser bottle). The signal is collected using a condensing lens which is coupled into a 20m optical fiber that is fed into a spectrometer that measures the emitted spectra. The resulting spectra from water/food coloring dye solutions were normalized by the reference spectrum, which is the Cerenkov spectrum of pure water, correcting for both the nonlinearity of the broadband Cerenkov emission spectrum as well as the non‐uniform spectral response of the spectrometer. The emitted spectra were then converted into absorbance and their characteristics were analyzed.Results:The food coloring dye had a drastic change on the Cerenkov emission, shifting its wavelength according to its visible color. The collected spectra showed various absorbance peaks which agrees with tabulated peak positions of the dyes added within 0.3% for yellow, 1.7% for red, and 0.16% for blue. The CE peak heights proportionally increased as the dye concentration is increased.Conclusion:This work shows the potential for real‐time functional spectroscopy using Cerenkov emission during radiotherapy. It was demonstrated that molecule identification as well as relative concentration can be extracted from the Cerenkov emission color shift.
SU‐F‐J‐54: Towards Real‐Time Volumetric Imaging Using the Treatment Beam and KV Beam
Authors: Chen M , Rozario T , Liu A , Jiang S , Lu W 
Published: July 2016

Purpose:Existing real‐time imaging uses dual (orthogonal) kV beam fluoroscopies and may result in significant amount of extra radiation to patients, especially for prolonged treatment cases. In addition, kV projections only provide 2D information, which is insufficient for in vivo dose reconstruction. We propose real‐time volumetric imaging using prior knowledge of pre‐treatment 4D images and real‐time 2D transit data of treatment beam and kV beam.Methods:The pre‐treatment multi‐snapshot volumetric images are used to simulate 2D projections of both the treatment beam and kV beam, respectively, for each treatment field defined by the control point. During radiation delivery, the transit signals acquired by the electronic portal image device (EPID) are processed for every projection and compared with pre‐calculation by cross‐correlation for phase matching and thus 3D snapshot identification or real‐time volumetric imaging. The data processing involves taking logarithmic ratios of EPID signals with respect to the air scan to reduce modeling uncertainties in head scatter fluence and EPID response. Simulated 2D projections are also used to pre‐calculate confidence levels in phase matching. Treatment beam projections that have a low confidence level either in pre‐calculation or real‐time acquisition will trigger kV beams so that complementary information can be exploited. In case both the treatment beam and kV beam return low confidence in phase matching, a predicted phase based on linear regression will be generated.Results:Simulation studies indicated treatment beams provide sufficient confidence in phase matching for most cases. At times of low confidence from treatment beams, kV imaging provides sufficient confidence in phase matching due to its complementary configuration.Conclusion:The proposed real‐time volumetric imaging utilizes the treatment beam and triggers kV beams for complementary information when the treatment beam along does not provide sufficient confidence for phase matching. This strategy minimizes the use of extra radiation to patients.This project is partially supported by a Varian MRA grant.
SU‐F‐J‐64: Comparison of Dosimetric Robustness Between Proton Therapy and IMRT Plans Following Tumor Regression for Locally Advanced Non‐Small Cell Lung Cancer (NSCLC)
Authors: Teng C , Ainsley C , Teo B , Burgdorf B , Janssens G , Berman A , Levin W , Xiao Y , Lin L , Simone C , Solberg T 
Published: July 2016

Purpose:In the light of tumor regression and normal tissue changes, dose distributions can deviate undesirably from what was planned. As a consequence, replanning is sometimes necessary during treatment to ensure continued tumor coverage or to avoid overdosing organs at risk (OARs). Proton plans are generally thought to be less robust than photon plans because of the proton beam's higher sensitivity to changes in tissue composition, suggesting also a higher likely replanning rate due to tumor regression. The purpose of this study is to compare dosimetric deviations between forward‐calculated double scattering (DS) proton plans with IMRT plans upon tumor regression, and assesses their impact on clinical replanning decisions.Methods:Ten consecutive locally advanced NSCLC patients whose tumors shrank > 50% in volume and who received four or more CT scans during radiotherapy were analyzed. All the patients received proton radiotherapy (6660 cGy, 180 cGy/fx). Dosimetric robustness during therapy was characterized by changes in the planning objective metrics as well as by point‐by‐point root‐mean‐squared differences for the entire PTV, ITV, and OARs (heart, cord, esophagus, brachial plexus and lungs) DVHs.Results:Sixty‐four pairs of DVHs were reviewed by three clinicians, who requested a replanning rate of 16.7% and 18.6% for DS and IMRT plans, respectively, with a high agreement between providers. Robustness of clinical indicators was found to depend on the beam orientation and dose level on the DVH curve. Proton dose increased most in OARs distal to the PTV along the beam path, but these changes were primarily in the mid to low dose levels. In contrast, the variation in IMRT plans occurred primarily in the high dose region.Conclusion:Robustness of clinical indicators depends where on the DVH curves comparisons are made. Similar replanning rates were observed for DS and IMRT plans upon large tumor regression.
SU‐F‐J‐65: Prediction of Patient Setup Errors and Errors in the Calibration Curve from Prompt Gamma Proton Range Measurements
Authors: Albert J , Labarbe R , Sterpin E , Lee J , Macq B 
Published: July 2016

Purpose:To understand the extent to which the prompt gamma camera measurements can be used to predict the residual proton range due to setup errors and errors in the calibration curve.Methods:We generated ten variations on a default calibration curve (CC) and ten corresponding range maps (RM). Starting with the default RM, we chose a square array of N beamlets, which were then rotated by a random angle θ and shifted by a random vector s. We added a 5% distal Gaussian noise to each beamlet in order to introduce discrepancies that exist between the ranges predicted from the prompt gamma measurements and those simulated with Monte Carlo algorithms. For each RM, s, θ, along with an offset u in the CC, were optimized using a simple Euclidian distance between the default ranges and the ranges produced by the given RM.Results:The application of our method lead to the maximal overrange of 2.0mm and underrange of 0.6mm on average. Compared to the situations where s, θ, and u were ignored, these values were larger: 2.1mm and 4.3mm. In order to quantify the need for setup error corrections, we also performed computations in which u was corrected for, but s and θ were not. This yielded: 3.2mm and 3.2mm. The average computation time for 170 beamlets was 65 seconds.Conclusion:These results emphasize the necessity to correct for setup errors and the errors in the calibration curve. The simplicity and speed of our method makes it a good candidate for being implemented as a tool for in‐room adaptive therapy. This work also demonstrates that the Prompt gamma range measurements can indeed be useful in the effort to reduce range errors. Given these results, and barring further refinements, this approach is a promising step towards an adaptive proton radiotherapy.
SU‐F‐J‐68: Deformable Dose Accumulation for Voxel‐Based Dose Tracking of PTV Cold Spots for Adaptive Radiotherapy of the Head and Neck
Authors: Liu C , Chetty I , Mao W , Kumarasiri A , Zhong H , Brown S , Siddiqui F 
Published: July 2016

Purpose:To utilize deformable dose accumulation (DDA) to determine how cold spots within the PTV change over the course of fractionated head and neck (H&N) radiotherapy.Methods:Voxel‐based dose was tracked using a DDA platform. The DDA process consisted of B‐spline‐based deformable image registration (DIR) and dose accumulation between planning CT's and daily cone‐beam CT's for 10 H&N cancer patients. Cold spots within the PTV (regions receiving less than the prescription, 70 Gy) were contoured on the cumulative dose distribution. These cold spots were mapped to each fraction, starting from the first fraction to determine how they changed. Spatial correlation between cold spot regions over each fraction, relative to the last fraction, was computed using the Jaccard index Jk (Mk,N), where N is the cold spot within the PTV at the end of the treatment, and Mk the same region for fraction k.Results:Figure 1 shows good spatial correlation between cold spots, and highlights expansion of the cold spot region over the course of treatment, as a result of setup uncertainties, and anatomical changes. Figure 2 shows a plot of Jk versus fraction number k averaged over 10 patients. This confirms the good spatial correlation between cold spots over the course of treatment. On average, Jk reaches ∼90% at fraction 22, suggesting that possible intervention (e.g. reoptimization) may mitigate the cold spot region. The cold spot, D99, averaged over 10 patients corresponded to a dose of ∼65 Gy, relative to the prescription dose of 70 Gy.Conclusion:DDA‐based tracking provides spatial dose information, which can be used to monitor dose in different regions of the treatment plan, thereby enabling appropriate mid‐treatment interventions.This work is supported in part by Varian Medical Systems, Palo Alto, CA.
SU‐F‐J‐75: Accuracy and Stability of Electron Density Measurements Across Patient Size Using Dual Energy CT
Authors: Michalak G , Halaweish A , Krauss B , Fletcher J , McCollough C 
Published: July 2016

Purpose:Dual energy (DE) CT can be used to characterize tissue composition. One application of DE CT is to measure electron density (ED, rho) and atomic number (Z) for use in radiation therapy treatment planning. This work evaluated the accuracy and stability of ED estimation as patient size varied for both single‐energy (SE) and DE CT.Methods:An ED phantom (CIRS) and four torso‐shaped water tanks (lateral widths 15, 25, 35 and 45 cm) containing 8 tissue‐simulating cylinders of known ED were scanned on a dual‐source CT system (Siemens Somatom Force) in SE (120 kV) and DE (90/150Sn) modes. Additional scans were performed on the 15 and 25 cm water tanks using DE techniques of 70/150Sn and 80/150Sn, respectively. CTDIvol was matched for all SE and DE scans for a given phantom size. Images were reconstructed using quantitative kernels to preserve CT number accuracy. ED was estimated in each test cylinder and in solid and liquid water using calibration measurements acquired in the CIRS phantom (SE) and a Rho‐Z algorithm (DE).Results:ED estimates showed good agreement with the nominal ED values when using Rho‐Z (slope = 1.0051, R2 = 0.9982). Mean percent error was similar between SE (1.21%) and DE (1.28%). Mean deviation across patient size decreased 34% (1.43% with SE, 0.95% with DE). When compared to 90/150Sn, DE techniques of 70/150Sn and 80/150Sn showed mean differences in ED of 0.43% and 0.15%, respectively.Conclusion:While both DE Rho‐Z and SE CT number calibration methods are both accurate for estimating ED, Rho‐Z offers the advantages of having less variability across patient size, not requiring a phantom calibration, and being able to distinguish between materials of similar attenuation, but different chemical composition. Low kV DE pairs are an option in small patients due to lack of effect on ED accuracy.This research was supported by Siemens Healthcare
SU‐F‐J‐88: Comparison of Two Deformable Image Registration Algorithms for CT‐To‐CT Contour Propagation
Authors: Gopal A , Xu H , Chen S 
Published: July 2016

Purpose:To compare the contour propagation accuracy of two deformable image registration (DIR) algorithms in the Raystation treatment planning system – the “Hybrid” algorithm based on image intensities and anatomical information; and the “Biomechanical” algorithm based on linear anatomical elasticity and finite element modeling.Methods:Both DIR algorithms were used for CT‐to‐CT deformation for 20 lung radiation therapy patients that underwent treatment plan revisions. Deformation accuracy was evaluated using landmark tracking to measure the target registration error (TRE) and inverse consistency error (ICE). The deformed contours were also evaluated against physician drawn contours using Dice similarity coefficients (DSC). Contour propagation was qualitatively assessed using a visual quality score assigned by physicians, and a refinement quality score (0<RQS<1) assigned by dosimetrists based on the extent of manual refinement needed for acceptable quality.Results:Both algorithms showed similar ICE (< 1.5 mm), but the hybrid DIR (TRE = 3.2 mm) performed better than the biomechanical DIR (TRE = 4.3 mm) with landmark tracking. Both algorithms had comparable DSC (DSC > 0.9 for lungs, > 0.85 for heart, > 0.8 for liver) and similar qualitative assessments (VQS < 0.35, RQS > 0.75 for lungs). When anatomical structures were used to control the deformation, the DSC improved more significantly for the biomechanical DIR compared to the hybrid DIR, while the VQS and RQS improved only for the controlling structures. However, while the inclusion of controlling structures improved the TRE for the hybrid DIR, it increased the TRE for the biomechanical DIR.Conclusion:The hybrid DIR was found to perform slightly better than the biomechanical DIR based on lower TRE while the DSC, VQS, and RQS studies yielded comparable results for both. The use of controlling structures showed considerable improvement in the hybrid DIR results and is recommended for clinical use in contour propagation.
SU‐F‐J‐94: Development of a Plug‐in Based Image Analysis Tool for Integration Into Treatment Planning
Authors: Owen D , Anderson C , Mayo C , El Naqa I , Ten Haken R , Cao Y , Balter J , Matuszak M 
Published: July 2016

Purpose:To extend the functionality of a commercial treatment planning system (TPS) to support (i) direct use of quantitative image‐based metrics within treatment plan optimization and (ii) evaluation of dose‐functional volume relationships to assist in functional image adaptive radiotherapy.Methods:A script was written that interfaces with a commercial TPS via an Application Programming Interface (API). The script executes a program that performs dose‐functional volume analyses. Written in C#, the script reads the dose grid and correlates it with image data on a voxel‐by‐voxel basis through API extensions that can access registration transforms. A user interface was designed through WinForms to input parameters and display results. To test the performance of this program, image‐ and dose‐based metrics computed from perfusion SPECT images aligned to the treatment planning CT were generated, validated, and compared.Results:The integration of image analysis information was successfully implemented as a plug‐in to a commercial TPS. Perfusion SPECT images were used to validate the calculation and display of image‐based metrics as well as dose‐intensity metrics and histograms for defined structures on the treatment planning CT. Various biological dose correction models, custom image‐based metrics, dose‐intensity computations, and dose‐intensity histograms were applied to analyze the image‐dose profile.Conclusion:It is possible to add image analysis features to commercial TPSs through custom scripting applications. A tool was developed to enable the evaluation of image‐intensity‐based metrics in the context of functional targeting and avoidance. In addition to providing dose‐intensity metrics and histograms that can be easily extracted from a plan database and correlated with outcomes, the system can also be extended to a plug‐in optimization system, which can directly use the computed metrics for optimization of post‐treatment tumor or normal tissue response models.Supported by NIH ‐ P01 ‐ CA059827
SU‐F‐J‐103: Assessment of Liver Tumor Contrast for Radiation Therapy: Inter‐Patient and Inter‐Sequence Variability
Authors: Moore B , Yin F , Czito B , Palta M , Cai J 
Published: July 2016

Purpose:To determine the variation in tumor contrast between different MRI sequences and between patients for the purpose of MRI‐based treatment planning.Methods:Multiple MRI scans of 11 patients with cancer(s) in the liver were included in this IRB‐approved study. Imaging sequences consisted of T1W MRI, Contrast‐Enhanced T1W MRI, T2W MRI, and T2*/T1W MRI. MRI images were acquired on a 1.5T GE Signa scanner with a four‐channel torso coil. We calculated the tumor‐to‐tissue contrast to noise ratio (CNR) for each MR sequence by contouring the tumor and a region of interest (ROI) in a homogeneous region of the liver using the Eclipse treatment planning software. CNR was calculated (I_Tum‐I_ROI)/SD_ROI, where I_Tum and I_ROI are the mean values of the tumor and the ROI respectively, and SD_ROI is the standard deviation of the ROI. The same tumor and ROI structures were used in all measurements for different MR sequences. Inter‐patient Coefficient of variation (CV), and inter‐sequence CV was determined. In addition, mean and standard deviation of CNR were calculated and compared between different MR sequences.Results:Our preliminary results showed large inter‐patient CV (range: 37.7% to 88%) and inter‐sequence CV (range 5.3% to 104.9%) of liver tumor CNR, indicating great variations in tumor CNR between MR sequences and between patients. Tumor CNR was found to be largest in CE‐T1W (8.5±7.5), followed by T2W (4.2±2.4), T1W (3.4±2.2), and T2*/T1W (1.7±0.6) MR scans. The inter‐patient CV of tumor CNR was also the largest in CE‐T1W (88%), followed by T1W (64.3%), T1W (56.2%), and T2*/T1W (37.7) MR scans.Conclusion:Large inter‐sequence and inter‐patient variations were observed in liver tumor CNR. CE‐T1W MR images on average provided the best tumor CNR. Efforts are needed to optimize tumor contrast and its consistency for MRI‐based treatment planning of cancer in the liver.This project is supported by NIH grant: 1R21CA165384