bluuebunny/crossref_metadata_2025_split huggingface dataset:
SU‐C‐209‐02: 3D Fluoroscopic Image Generation From Patient‐Specific 4DCBCT‐Based Motion Models Derived From Clinical Patient Images
Authors: Dhou S , Ionascu D , Cai W , Hurwitz M , Williams C , Lewis J 
Published: July 2016

Purpose:We develop a method to generate time varying volumetric images (3D fluoroscopic images) using patient‐specific motion models derived from four‐dimensional cone‐beam CT (4DCBCT).Methods:Motion models are derived by selecting one 4DCBCT phase as a reference image, and registering the remaining images to it. Principal component analysis (PCA) is performed on the resultant displacement vector fields (DVFs) to create a reduced set of PCA eigenvectors that capture the majority of respiratory motion. 3D fluoroscopic images are generated by optimizing the weights of the PCA eigenvectors iteratively through comparison of measured cone‐beam projections and simulated projections generated from the motion model. This method was applied to images from five lung‐cancer patients. The spatial accuracy of this method is evaluated by comparing landmark positions in the 3D fluoroscopic images to manually defined ground truth positions in the patient cone‐beam projections.Results:4DCBCT motion models were shown to accurately generate 3D fluoroscopic images when the patient cone‐beam projections contained clearly visible structures moving with respiration (e.g., the diaphragm). When no moving anatomical structure was clearly visible in the projections, the 3D fluoroscopic images generated did not capture breathing deformations, and reverted to the reference image. For the subset of 3D fluoroscopic images generated from projections with visibly moving anatomy, the average tumor localization error and the 95th percentile were 1.6 mm and 3.1 mm respectively.Conclusion:This study showed that 4DCBCT‐based 3D fluoroscopic images can accurately capture respiratory deformations in a patient dataset, so long as the cone‐beam projections used contain visible structures that move with respiration. For clinical implementation of 3D fluoroscopic imaging for treatment verification, an imaging field of view (FOV) that contains visible structures moving with respiration should be selected. If no other appropriate structures are visible, the images should include the diaphragm.This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.
SU‐C‐209‐04: Calculation of Two‐Material Tissue Parameterizations for Dual‐Energy Imaging
Authors: Jennings R 
Published: July 2016

Purpose:The characterization of the energy dependences of tissue attenuation coefficients is fundamental in dual‐energy imaging. It has been posited that a two‐parameter model is both necessary and sufficient to describe these dependences for tissues of medical interest in the diagnostic energy range. In one approach the two parameters are the weighting factors for the energy‐dependent linear attenuation coefficients of two distinct materials. This is commonly known as the basis material decomposition method. The purpose of this work is to describe and evaluate a method for determining these weighting factors for user‐selected basis materials and tissues of interest.Methods:The method essentially says that the linear attenuation coefficient of the tissue of interest is equal, to a good approximation, to the weighted sum of the coefficients of two other distinct materials. The weights can be obtained from the resulting equation, evaluated at two different energies. Not all pairs of energies produce weights that yield accurate results. The method for finding a quasi‐optimal pair of weights is described.Results:The method has been applied to a variety of combinations of tissue of interest and materials chosen to represent it. In most instances, the accuracy of the match is of the order of a few tenths of one percent over a large energy range. Even for combinations that might be expected to give poor results, water represented by PMMA and aluminum for example, the maximum deviations are of the order of one percent.Conclusion:The method produces characterizations that are at least as good as, and usually better than, the accuracy of x‐ray measurements obtainable with reasonable patient doses and currently available hardware. The results also show that many different materials are reasonable choices for basis materials. The software used for this study is available on GitHub.
SU‐C‐207B‐03: A Geometrical Constrained Chan‐Vese Based Tumor Segmentation Scheme for PET
Authors: Chen L , Zhou Z , Wang J 
Published: July 2016

Purpose:Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan‐Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices.Methods:The proposed scheme performs segmentation in a slice‐by‐slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering‐based segmentation method under user's guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan‐Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan‐Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme.Results:Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively.Conclusion:We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.
SU‐C‐207B‐04: Automated Segmentation of Pectoral Muscle in MR Images of Dense Breasts
Authors: Verburg E , de Waard SN , Veldhuis WB , van Gils CH , Gilhuijs KGA 
Published: July 2016

Purpose:To develop and evaluate a fully automated method for segmentation of the pectoral muscle boundary in Magnetic Resonance Imaging (MRI) of dense breasts.Methods:Segmentation of the pectoral muscle is an important part of automatic breast image analysis methods. Current methods for segmenting the pectoral muscle in breast MRI have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Hence, an automated method based on dynamic programming was developed, incorporating heuristics aimed at shape, location and gradient features.To assess the method, the pectoral muscle was segmented in 91 randomly selected participants (mean age 56.6 years, range 49.5–75.2 years) from a large MRI screening trial in women with dense breasts (ACR BI‐RADS category 4). Each MR dataset consisted of 178 or 179 T1‐weighted images with voxel size 0.64 × 0.64 × 1.00 mm3. All images (n=16,287) were reviewed and scored by a radiologist. In contrast to volume overlap coefficients, such as DICE, the radiologist detected deviations in the segmented muscle border and determined whether the result would impact the ability to accurately determine the volume of fibroglandular tissue and detection of breast lesions.Results:According to the radiologist's scores, 95.5% of the slices did not mask breast tissue in such way that it could affect detection of breast lesions or volume measurements. In 13.1% of the slices a deviation in the segmented muscle border was present which would not impact breast lesion detection. In 70 datasets (78%) at least 95% of the slices were segmented in such a way it would not affect detection of breast lesions, and in 60 (66%) datasets this was 100%.Conclusion:Dynamic programming with dedicated heuristics shows promising potential to segment the pectoral muscle in women with dense breasts.
SU‐C‐207B‐06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy
Authors: Riyahi S , Choi W , Bhooshan N , Tan S , Zhang H , Lu W 
Published: July 2016

Purpose:To compare linear and deformable registration methods for evaluation of tumor response to Chemoradiation therapy (CRT) in patients with esophageal cancer.Methods:Linear and multi‐resolution BSpline deformable registration were performed on Pre‐Post‐CRT CT/PET images of 20 patients with esophageal cancer. For both registration methods, we registered CT using Mean Square Error (MSE) metric, however to register PET we used transformation obtained using Mutual Information (MI) from the same CT due to being multi‐modality. Similarity of Warped‐CT/PET was quantitatively evaluated using Normalized Mutual Information and plausibility of DF was assessed using inverse consistency Error. To evaluate tumor response four groups of tumor features were examined: (1) Conventional PET/CT e.g. SUV, diameter (2) Clinical parameters e.g. TNM stage, histology (3)spatial‐temporal PET features that describe intensity, texture and geometry of tumor (4)all features combined. Dominant features were identified using 10‐fold cross‐validation and Support Vector Machine (SVM) was deployed for tumor response prediction while the accuracy was evaluated by ROC Area Under Curve (AUC).Results:Average and standard deviation of Normalized mutual information for deformable registration using MSE was 0.2±0.054 and for linear registration was 0.1±0.026, showing higher NMI for deformable registration. Likewise for MI metric, deformable registration had 0.13±0.035 comparing to linear counterpart with 0.12±0.037. Inverse consistency error for deformable registration for MSE metric was 4.65±2.49 and for linear was 1.32±2.3 showing smaller value for linear registration. The same conclusion was obtained for MI in terms of inverse consistency error. AUC for both linear and deformable registration was 1 showing no absolute difference in terms of response evaluation.Conclusion:Deformable registration showed better NMI comparing to linear registration, however inverse consistency of transformation was lower in linear registration. We do not expect to see significant difference when warping PET images using deformable or linear registration.This work was supported in part by the National Cancer Institute Grants R01CA172638.
SU‐D‐204‐04: Correlations Between Dosimetric Indices and Follow‐Up Data for Salivary Glands Six Months After Radiation Therapy for Head and Neck Cancer
Authors: Chera B , Price A , Kostich M , Amdur R , Mendenhall W , Sheets N , Green R , Marks L , Das S , Mavroidis P 
Published: July 2016

Purpose:To investigate the correlation between different dosimetric indices of salivary glands (as separate or combined structures) to patient‐reported dry mouth 6 months post radiotherapy using the novel patient reported outcome version of the CTCAE (PRO‐CTCAE).Methods:Forty‐three patients with oropharyngeal squamous cell carcinoma were treated on a prospective multi‐institutional study. All patients received de‐intensified 60 Gy intensity modulated radiotherapy. Dosimetric constraints were used for the salivary glands (e.g. mean dose to the contralateral‐parotid < 26 Gy). We investigated correlations of individual patient dosimetric data of the parotid and submandibular glands (as separate or combined structures) to their self‐reported 6 month post‐treatment dry mouth responses. Moderate dry mouth responses were most prevalent and were used as the clinical endpoint indicating response. The correlation of Dmean, Dmax and a range of dosevolume (VD) points were assessed through the area under the Receiver Operating Characteristic curve (ROC) and Odds Ratios (OR).Results:Patients reporting non/mild dry mouth response (N=22) had average Dmean = 19.6 ± 6.2Gy to the contralateral‐parotid compared to an average Dmean = 28.0 ± 8.3Gy and an AUC = 0.758 for the patients reporting moderate/severe/very severe dry mouth (N=21). Analysis of the range of VD's for patients who had reported dry mouth showed that for the contralateral‐parotid the indices V18 through V22 had the highest area under the curves (AUC) (0.762 – 0.772) compared to a more traditional dosimetric index V30, which had an AUC = 0.732. The highest AUC was observed for the combination of contralateral parotid and contralateral submandibular glands, for which V16 through V28 had AUC = 0.801 – 0.834.Conclusion:Patients who report moderate/severe/very severe dry mouth 6 months post radiotherapy had on average higher Dmean. The V16‐V28 of the combination of the contralateral glands showed the highest correlation with the clinical endpoint.
SU‐D‐204‐05: Fitting Four NTCP Models to Treatment Outcome Data of Salivary Glands Recorded Six Months After Radiation Therapy for Head and Neck Tumors
Authors: Mavroidis P , Price A , Kostich M , Amdur R , Mendenhall W , Sheets N , Green R , Das S , Marks L , Chera B 
Published: July 2016

Purpose:To estimate the radiobiological parameters of four popular NTCP models that describe the dose‐response relations of salivary glands to the severity of patient reported dry mouth 6 months post chemo‐radiotherapy. To identify the glands, which best correlate with the manifestation of those clinical endpoints. Finally, to evaluate the goodness‐of‐fit of the NTCP models.Methods:Forty‐three patients were treated on a prospective multiinstitutional phase II study for oropharyngeal squamous cell carcinoma. All the patients received 60 Gy IMRT and they reported symptoms using the novel patient reported outcome version of the CTCAE. We derived the individual patient dosimetric data of the parotid and submandibular glands (SMG) as separate structures as well as combinations. The Lyman‐Kutcher‐Burman (LKB), Relative Seriality (RS), Logit and Relative Logit (RL) NTCP models were used to fit the patients data. The fitting of the different models was assessed through the area under the receiver operating characteristic curve (AUC) and the Odds Ratio methods.Results:The AUC values were highest for the contralateral parotid for Grade ≥ 2 (0.762 for the LKB, RS, Logit and 0.753 for the RL). For the salivary glands the AUC values were: 0.725 for the LKB, RS, Logit and 0.721 for the RL. For the contralateral SMG the AUC values were: 0.721 for LKB, 0.714 for Logit and 0.712 for RS and RL. The Odds Ratio for the contralateral parotid was 5.8 (1.3–25.5) for all the four NTCP models for the radiobiological dose threshold of 21Gy.Conclusion:It was shown that all the examined NTCP models could fit the clinical data well with very similar accuracy. The contralateral parotid gland appears to correlated best with the clinical endpoints of severe/very severe dry mouth. An EQD2Gy dose of 21Gy appears to be a safe threshold to be used as a constraint in treatment planning.
SU‐D‐204‐07: Retrospective Correlation of Dose Accuracy with Regions of Local Failure for Early Stage Lung Cancer Patients Treated with Stereotactic Body Radiotherapy
Authors: Devpura S , Li H , Liu C , Fraser C , Ajlouni M , Movsas B , Chetty I 
Published: July 2016

Purpose:To correlate dose distributions computed using six algorithms for recurrent early stage non‐small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT), with outcome (local failure).Methods:Of 270 NSCLC patients treated with 12Gyx4, 20 were found to have local recurrence prior to the 2‐year time point. These patients were originally planned with 1‐D pencil beam (1‐D PB) algorithm. 4D imaging was performed to manage tumor motion. Regions of local failures were determined from follow‐up PET‐CT scans. Follow‐up CT images were rigidly fused to the planning CT (pCT), and recurrent tumor volumes (Vrecur) were mapped to the pCT. Dose was recomputed, retrospectively, using five algorithms: 3‐D PB, collapsed cone convolution (CCC), anisotropic analytical algorithm (AAA), AcurosXB, and Monte Carlo (MC). Tumor control probability (TCP) was computed using the Marsden model (1,2). Patterns of failure were classified as central, in‐field, marginal, and distant for Vrecur ≥95% of prescribed dose, 95–80%, 80–20%, and ≤20%, respectively (3).Results:Average PTV D95 (dose covering 95% of the PTV) for 3‐D PB, CCC, AAA, AcurosXB, and MC relative to 1‐D PB were 95.3±2.1%, 84.1±7.5%, 84.9±5.7%, 86.3±6.0%, and 85.1±7.0%, respectively. TCP values for 1‐D PB, 3‐D PB, CCC, AAA, AcurosXB, and MC were 98.5±1.2%, 95.7±3.0, 79.6±16.1%, 79.7±16.5%, 81.1±17.5%, and 78.1±20%, respectively. Patterns of local failures were similar for 1‐D and 3D PB plans, which predicted that the majority of failures occur in centraldistal regions, with only ∼15% occurring distantly. However, with convolution/superposition and MC type algorithms, the majority of failures (65%) were predicted to be distant, consistent with the literature.Conclusion:Based on MC and convolution/superposition type algorithms, average PTV D95 and TCP were ∼15% lower than the planned 1‐D PB dose calculation. Patterns of failure results suggest that MC and convolution/superposition type algorithms predict different outcomes for patterns of failure relative to PB algorithms.Work supported in part by Varian Medical Systems, Palo Alto, CA
SU‐D‐201‐01: A Multi‐Institutional Study Quantifying the Impact of Simulated Linear Accelerator VMAT Errors for Nasopharynx
Authors: Pogson E , Hansen C , Blake S , Arumugam S , Juresic J , Ochoa C , Yakobi J , Haman A , Trtovac A , Holloway L , Thwaites D 
Published: July 2016

Purpose:To quantify the impact of differing magnitudes of simulated linear accelerator errors on the dose to the target volume and organs at risk for nasopharynx VMAT.Methods:Ten nasopharynx cancer patients were retrospectively replanned twice with one full arc VMAT by two institutions. Treatment uncertainties (gantry angle and collimator in degrees, MLC field size and MLC shifts in mm) were introduced into these plans at increments of 5,2,1,−1,−2 and −5. This was completed using an in‐house Python script within Pinnacle3 and analysed using 3DVH and MatLab. The mean and maximum dose were calculated for the Planning Target Volume (PTV1), parotids, brainstem, and spinal cord and then compared to the original baseline plan. The D1cc was also calculated for the spinal cord and brainstem. Patient average results were compared across institutions.Results:Introduced gantry angle errors had the smallest effect of dose, no tolerances were exceeded for one institution, and the second institutions VMAT plans were only exceeded for gantry angle of ±5° affecting different sided parotids by 14–18%. PTV1, brainstem and spinal cord tolerances were exceeded for collimator angles of ±5 degrees, MLC shifts and MLC field sizes of ±1 and beyond, at the first institution. At the second institution, sensitivity to errors was marginally higher for some errors including the collimator error producing doses exceeding tolerances above ±2 degrees, and marginally lower with tolerances exceeded above MLC shifts of ±2. The largest differences occur with MLC field sizes, with both institutions reporting exceeded tolerances, for all introduced errors (±1 and beyond).Conclusion:The plan robustness for VMAT nasopharynx plans has been demonstrated. Gantry errors have the least impact on patient doses, however MLC field sizes exceed tolerances even with relatively low introduced errors and also produce the largest errors. This was consistent across both departments.The authors acknowledge funding support from the NSW Cancer Council.
SU‐D‐201‐04: Evaluation of Elekta Agility MLC Performance Using Statistical Process Control
Authors: Meyers SM , Balderson MJ , Letourneau D 
Published: July 2016

Purpose:to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools.Methods:Leaf positions were collected daily for 11 Elekta units over 5–19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field‐of‐view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1–2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out‐of‐control and out‐of‐specification events were determined.Results:Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09–0.44). Mean leaf position error was −0.03mm (range −0.89–0.83). Due to sporadic out‐of‐control events, the mean in‐control duration was 3.3 days (range 1–23). Data stayed within ±1mm specification for 205 days on average (range 3–372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0–17); however, our MLC leaves were not calibrated to this level of accuracy.Conclusion:The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in‐specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on‐going to determine if Agility performance can be optimized further using high‐frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image artifacts, are under investigation.