bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐C‐BRB‐07: Threshold‐Driven Optimization for Reference‐Based Auto‐Planning
Authors: Long T , Chen M , Jiang S , Lu W 
Published: July 2016

Purpose:To study the procedure of reference‐based auto‐planning for treatment plan optimization and develop a threshold‐driven optimization methodology for automatically generating a treatment plan that is motivated by a reference DVH and/or dose distribution. We study threshold‐driven optimization for reference‐based auto‐planning (TORA).Methods:Commonly used voxel‐based quadratic penalties have three components: an overdose weight, and underdose weight, and some target dose threshold. Conventional planning using such a function involves iteratively updating the preference weights while keeping the thresholds constant, an unintuitive and inconsistent method for planning towards some target DVH. In auto‐planning, current cutting‐edge techniques also focus on the preference weights while fixing the threshold values. However, driving the optimization by threshold values can achieve similar plans without the extreme updates usually required of the weight parameters. The proposed methodology directly relates reference information to threshold values, which influence the optimization in a more impactful, intuitive way than the preference weights.Results:This methodology was applied to a prostate cancer case as a proof‐of‐concept. Both IMRT and VMAT treatment planning models were able to closely achieve a reference DVH. Other cases and modalities will be studied.Conclusion:This auto‐planning technique has the potential to be very effective for auto‐planning based on reference plans. As dose prediction and data‐driven planning becomes more prevalent in the clinical setting, incorporating such data into the treatment planning model in a clear, effective way will be crucial for efficient planning. Conventional methods of determining objective function parameters for a voxel‐based quadratic objective function have shortcomings and a threshold‐focused objective tuning should be explored, especially in the realm of efficient auto‐planning.Supported by Cancer Prevention & Research Institute of Texas (CPRIT) ‐ ID RP150485
SU‐C‐BRA‐01: Interactive Auto‐Segmentation for Bowel in Online Adaptive MRI‐Guided Radiation Therapy by Using a Multi‐Region Labeling Algorithm
Authors: Lu Y , Chen I , Kashani R , Wan H , Maughan N , Muccigrosso D , Parikh P 
Published: July 2016

Purpose:In MRI‐guided online adaptive radiation therapy, re‐contouring of bowel is time‐consuming and can impact the overall time of patients on table. The study aims to auto‐segment bowel on volumetric MR images by using an interactive multi‐region labeling algorithm.Methods:5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI‐guided radiation therapy system with a 0.35 Tesla magnet and three Co‐60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two‐dimensional multi‐region labeling technique based on graph cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician's manual contouring by using metrics of Dice coefficient and Hausdorff distance.Results:Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto‐contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively.Conclusion:The preliminary results demonstrated the potential of the proposed algorithm in auto‐segmenting large and small bowel on low field MRI images in MRI‐guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.
SU‐C‐BRA‐05: Delineating High‐Dose Clinical Target Volumes for Head and Neck Tumors Using Machine Learning Algorithms
Authors: Cardenas C , Wong A , Mohamed A , Yang J , Court L , Rao A , Fuller C , Aristophanous M 
Published: July 2016

Purpose:To develop and test population‐based machine learning algorithms for delineating high‐dose clinical target volumes (CTVs) in H&N tumors. Automating and standardizing the contouring of CTVs can reduce both physician contouring time and inter‐physician variability, which is one of the largest sources of uncertainty in H&N radiotherapy.Methods:Twenty‐five node‐negative patients treated with definitive radiotherapy were selected (6 right base of tongue, 11 left and 9 right tonsil). All patients had GTV and CTVs manually contoured by an experienced radiation oncologist prior to treatment. This contouring process, which is driven by anatomical, pathological, and patient specific information, typically results in non‐uniform margin expansions about the GTV. Therefore, we tested two methods to delineate high‐dose CTV given a manually‐contoured GTV: (1) regression‐support vector machines(SVM) and (2) classification‐SVM. These models were trained and tested on each patient group using leave‐one‐out cross‐validation. The volume difference(VD) and Dice similarity coefficient(DSC) between the manual and auto‐contoured CTV were calculated to evaluate the results. Distances from GTV‐to‐CTV were computed about each patient's GTV and these distances, in addition to distances from GTV to surrounding anatomy in the expansion direction, were utilized in the regression‐SVM method. The classification‐SVM method used categorical voxel‐information (GTV, selected anatomical structures, else) from a 3×3×3cm3 ROI centered about the voxel to classify voxels as CTV.Results:Volumes for the auto‐contoured CTVs ranged from 17.1 to 149.1cc and 17.4 to 151.9cc; the average(range) VD between manual and auto‐contoured CTV were 0.93 (0.48–1.59) and 1.16(0.48–1.97); while average(range) DSC values were 0.75(0.59–0.88) and 0.74(0.59–0.81) for the regression‐SVM and classification‐SVM methods, respectively.Conclusion:We developed two novel machine learning methods to delineate high‐dose CTV for H&N patients. Both methods showed promising results that hint to a solution to the standardization of the contouring process of clinical target volumes.Varian Medical Systems grant
SU‐C‐BRA‐06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications
Authors: Liu Y , Stojadinovic S , Jiang S , Timmerman R , Abdulrahman R , Nedzi L , Gu X 
Published: July 2016

Purpose:Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super‐voxel clustering, and level‐set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast‐enhance T1‐weighted (T1c) Magnetic Resonance Images (MRI).Methods:The developed auto‐segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto‐segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics.Results:DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto‐segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0.078, SSIM 0.999±0.001, and HD 5.926±6.141mm.Conclusion:The developed automatic segmentation strategy, which yields accurate brain tumor delineation in evaluation cases, is promising for its application in SRS treatment planning.
SU‐C‐202‐04: Adapting Biologically Optimized Dose Escalation Based On Mid‐Treatment PET/CT for Non‐Small‐Cell Lung Cancer
Authors: Zhang P , Kuo L , Yorke E , Hu Y , Lockney N , Mageras G , Deasy J , Rimner A 
Published: July 2016

Purpose:To develop a biological modeling strategy which incorporates the response observed on the mid‐treatment PET/CT into a dose escalation design for adaptive radiotherapy of non‐small‐cell lung cancer.Method:FDG‐PET/CT was acquired midway through standard fractionated treatment and registered to pre‐treatment planning PET/CT to evaluate radiation response of lung cancer. Each mid‐treatment PET voxel was assigned the median SUV inside a concentric 1cm‐diameter sphere to account for registration and imaging uncertainties. For each voxel, the planned radiation dose, pre‐ and mid‐treatment SUVs were used to parameterize the linear‐quadratic model, which was then utilized to predict the SUV distribution after the full prescribed dose. Voxels with predicted post‐treatment SUV≥2 were identified as the resistant target (response arm). An adaptive simultaneous integrated boost was designed to escalate dose to the resistant target as high as possible, while keeping prescription dose to the original target and lung toxicity intact. In contrast, an adaptive target volume was delineated based only on the intensity of mid‐treatment PET/CT (intensity arm), and a similar adaptive boost plan was optimized. The dose escalation capability of the two approaches was compared.Result:Images of three patients were used in this planning study. For one patient, SUV prediction indicated complete response and no necessary dose escalation. For the other two, resistant targets defined in the response arm were multifocal, and on average accounted for 25% of the pre‐treatment target, compared to 67% in the intensity arm. The smaller response arm targets led to a 6Gy higher mean target dose in the adaptive escalation design.Conclusion:This pilot study suggests that adaptive dose escalation to a biologically resistant target predicted from a pre‐ and mid‐treatment PET/CT may be more effective than escalation based on the mid‐treatment PET/CT alone. More plans and ultimately clinical protocols are needed to validate this approach.MSKCC has a research agreement with Varian Medical System
SU‐C‐202‐06: Design and Development of a Non‐Rigid Phantom Ventilating Air Quantitatively Evaluating CT‐Based Pulmonary Ventilation Imaging
Authors: Miyakawa S , Tachibana H , Moriya S , Kurosawa T , Sato M 
Published: July 2016

Purpose:A validation study using human or sheep for CT pulmonary ventilation (CT‐V) imaging are inefficient and partially unstable. In this study, we designed and developed non‐rigid phantom ventilating air to quantitatively evaluate the CT‐V image.Methods:The phantom consisted of an acryl cylinder filled with polyurethane foam designed to pulmonic alveoli and a polyurethane membrane was attached to the inferior end of the phantom to simulate a lung diaphragm. Also, trachea‐shaped polyurethane tubes were covered by the foam and the tubes passed through the outside in order to exchange a gas. Lung arteries were also modeled with polyurethane and were located adjacent to the trachea model. Two 3D‐CT scans were performed at exhale and inhale using an air contrast agent of xenon gas, respectively. The exhale 3D‐CT was deformed to the inhale 3D‐CT using the in‐house program with the NiftyReg. Sixteen landmarks were assigned around trachea and lung arteries in the inhale image and the deformed exhale image. Target Registration Error (TRE) for each landmark between the two images was calculated for commissioning of our program. CT‐V image was generated using Jacobian determinant metrics (JD). Subsequently, HU value change between the two images was measured. The correlation coefficient was calculated between the JD and the HU change.Results:The deformable and ventilation phantom shows a variety of displacement for each landmark (1 mm − 20 mm). The mean TRE was 4.5 ± 4.7 mm (Maximum: 12.3 mm). The relationship between the JD and the HU change shows high correlation. (R = −0.86). At the several landmarks where TRE values were over 3 mm, the correlation was poor.Conclusion:The phantom may be a useful QA tool to validate CT‐V imaging. We will improve our deformable image registration program for CT‐V imaging with the efficient way using the phantom.
SU‐C‐207A‐01: A Novel Maximum Likelihood Method for High‐Resolution Proton Radiography/proton CT
Authors: Collins‐Fekete C , Schulte R , Beaulieu L , Seco J 
Published: July 2016

Purpose:Multiple Coulomb scattering is the largest contributor to blurring in proton imaging. Here we tested a maximum likelihood least squares estimator (MLLSE) to improve the spatial resolution of proton radiography (pRad) and proton computed tomography (pCT).Methods:The object is discretized into voxels and the average relative stopping power through voxel columns defined from the source to the detector pixels is optimized such that it maximizes the likelihood of the proton energy loss. The length spent by individual protons in each column is calculated through an optimized cubic spline estimate. pRad images were first produced using Geant4 simulations. An anthropomorphic head phantom and the Catphan line‐pair module for 3‐D spatial resolution were studied and resulting images were analyzed. Both parallel and conical beam have been investigated for simulated pRad acquisition. Then, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Specific filters were applied on proton angle and energy loss data to remove proton histories that underwent nuclear interactions. The MTF10% (lp/mm) was used to evaluate and compare spatial resolution.Results:Numerical simulations showed improvement in the pRad spatial resolution for the parallel (2.75 to 6.71 lp/cm) and conical beam (3.08 to 5.83 lp/cm) reconstructed with the MLLSE compared to averaging detector pixel signals. For full tomographic reconstruction, the improved pRad were used as input into a simultaneous algebraic reconstruction algorithm. The Catphan pCT reconstruction based on the MLLSE‐enhanced projection showed spatial resolution improvement for the parallel (2.83 to 5.86 lp/cm) and conical beam (3.03 to 5.15 lp/cm). The anthropomorphic head pCT displayed important contrast gains in high‐gradient regions. Experimental results also demonstrated significant improvement in spatial resolution of the pediatric head radiography.Conclusion:The proposed MLLSE shows promising potential to increase the spatial resolution (up to 244%) in proton imaging.
SU‐C‐207A‐02: Proton Radiography Using Pencil Beam Scanning and a Novel, Low‐Cost Range Telescope
Authors: Dolney D , Mayers G , Newcomer M , Weiss D , Meekins E , Bollinger D , Desai N , Maughan R , Solberg T , Hollebeek R 
Published: July 2016

Purpose:While the energy of therapeutic proton beams can be adjusted to penetrate to any given depth in water, range uncertainties arise in patients due in part to imprecise knowledge of the stopping power of protons in human tissues [1]. Proton radiography is one approach to reduce the beam range uncertainty [2], thereby allowing for a reduction in treatment margins and dose escalation.Methods:The authors have adapted a novel detector technology based on Micromesh Gaseous Structure (“Micromegas”) for proton therapy beams and have demonstrated fine spatial and time resolution of magnetically scanned proton pencil beams, as well as wide dynamic range for dosimetry [3]. The authors have constructed a prototype imaging system comprised of 5 Micromegas layers. Proton radiographs were obtained downstream of solid water assemblies. The position‐sensitive monitor chambers in the IBA proton delivery nozzle provide the beam entrance position.Results:Our technique achieves spatial resolution as low as 300 µm and water‐equivalent thickness (WET) resolution as good as 0.02% (60 µm out of 31 cm total thickness). The dose delivered to the patient is kept below 2 cGy. The spatial resolution as a function of sample rate and number of delivered protons is found to be near the theoretical Cramer‐Rao lower bound. By extrapolating the CR bound, we argue that the imaging dose could be further lowered to 1 mGy, while still achieving submillimeter spatial resolution, by achievable instrumentation and beam delivery modifications.Conclusion:For proton radiography, high spatial and WET resolution can be achieved, with minimal additional dose to patient, by using magnetically scanned proton pencil beams and Micromegas detectors.
SU‐C‐207A‐04: Accuracy of Acoustic‐Based Proton Range Verification in Water
Authors: Jones KC , Vander Stappen F , Sehgal CM , Avery S 
Published: July 2016

Purpose:To determine the accuracy and dose required for acoustic‐based proton range verification (protoacoustics) in water.Methods:Proton pulses with 17 µs FWHM and instantaneous currents of 480 nA (5.6 × 107 protons/pulse, 8.9 cGy/pulse) were generated by a clinical, hospital‐based cyclotron at the University of Pennsylvania. The protoacoustic signal generated in a water phantom by the 190 MeV proton pulses was measured with a hydrophone placed at multiple known positions surrounding the dose deposition. The background random noise was measured. The protoacoustic signal was simulated to compare to the experiments.Results:The maximum protoacoustic signal amplitude at 5 cm distance was 5.2 mPa per 1 × 107 protons (1.6 cGy at the Bragg peak). The background random noise of the measurement was 27 mPa. Comparison between simulation and experiment indicates that the hydrophone introduced a delay of 2.4 µs. For acoustic data collected with a signal‐to‐noise ratio (SNR) of 21, deconvolution of the protoacoustic signal with the proton pulse provided the most precise time‐of‐flight range measurement (standard deviation of 2.0 mm), but a systematic error (−4.5 mm) was observed.Conclusion:Based on water phantom measurements at a clinical hospital‐based cyclotron, protoacoustics is a potential technique for measuring the proton Bragg peak range with 2.0 mm standard deviation. Simultaneous use of multiple detectors is expected to reduce the standard deviation, but calibration is required to remove systematic error. Based on the measured background noise and protoacoustic amplitude, a SNR of 5.3 is projected for a deposited dose of 2 Gy.
SU‐C‐207A‐05: Feature Based Water Equivalent Path Length (WEPL) Determination for Proton Radiography by the Technique of Time Resolved Dose Measurement
Authors: Zhang R , Jee K , Sharp G , Flanz J , Lu H 
Published: July 2016

Purpose:Studies show that WEPL can be determined from modulated dose rate functions (DRF). However, the previous calibration method based on statistics of the DRF is sensitive to energy mixing of protons due to scattering through different materials (termed as range mixing here), causing inaccuracies in the determination of WEPL. This study intends to explore time‐domain features of the DRF to reduce the effect of range mixing in proton radiography (pRG) by this technique.Methods:An amorphous silicon flat panel (PaxScan™ 4030CB, Varian Medical Systems, Inc., Palo Alto, CA) was placed behind phantoms to measure DRFs from a proton beam modulated by a specially designed modulator wheel. The performance of two methods, the previously used method based on the root mean square (RMS) and the new approach based on time‐domain features of the DRF, are compared for retrieving WEPL and RSP from pRG of a Gammex phantom.Results:Calibration by T80 (the time point for 80% of the major peak) was more robust to range mixing and produced WEPL with improved accuracy. The error of RSP was reduced from 8.2% to 1.7% for lung equivalent material, with the mean error for all other materials reduced from 1.2% to 0.7%. The mean error of the full width at half maximum (FWHM) of retrieved inserts was decreased from 25.85% to 5.89% for the RMS and T80 method respectively. Monte Carlo simulations in simplified cases also demonstrated that the T80 method is less sensitive to range mixing than the RMS method.Conclusion:WEPL images have been retrieved based on single flat panel measured DRFs, with inaccuracies reduced by exploiting time‐domain features as the calibration parameter. The T80 method is validated to be less sensitive to range mixing and can thus retrieve the WEPL values in proximity of interfaces with improved numerical and spatial accuracy for proton radiography.
SU‐C‐207A‐07: Cumulative 18F‐FDG Uptake Histogram Relative to Radiation Dose Volume Histogram of Lung After IMRT Or PSPT and Their Association with Radiation Pneumonitis
Authors: Shusharina N , Liao Z , Mohan R , Choi N , Bortfeld T 
Published: July 2016

Purpose:To determine whether the difference in cumulative 18F‐FDG uptake histogram of lung treated with either IMRT or PSPT is associated with radiation pneumonitis (RP) in patients with inoperable stage II and III NSCLC.Methods:We analyzed 24 patients from a prospective randomized trial to compare IMRT (n=12) with vs. PSPT (n=12) for inoperable NSCLC. All patients underwent PET‐CT imaging between 35 and 88 days post‐therapy. Post‐treatment PET‐CT was aligned with planning 4D CT to establish a voxel‐to‐voxel correspondence between post‐treatment PET and planning dose images. 18F‐FDG uptake as a function of radiation dose to normal lung was obtained for each patient. Distribution of the standard uptake value (SUV) was analyzed using a volume histogram method. The image quantitative characteristics and DVH measures were correlated with clinical symptoms of pneumonitis.Results:Patients with RP were present in both groups: 5 in the IMRT and 6 in the PSPT. The analysis of cumulative SUV histograms showed significantly higher relative volumes of the normal lung having higher SUV uptake in the PSPT patients for both symptomatic and asymptomatic cases (VSUV=2: 10% for IMRT vs 16% for proton RT and VSUV=1: 10% for IMRT vs 23% for proton RT). In addition, the SUV histograms for symptomatic cases in PSPT patients exhibited a significantly longer tail at the highest SUV. The absolute volume of the lung receiving the dose >70 Gy was larger in the PSPT patients.Conclusion:18F‐FDG uptake – radiation dose response correlates with RP in both groups of patients by means of the linear regression slope. SUV is higher for the PSPT patients for both symptomatic and asymptomatic cases. Higher uptake after PSPT patients is explained by larger volumes of the lung receiving high radiation dose.