bluuebunny/crossref_metadata_2025_split huggingface dataset:
TU‐C‐17A‐10: Patient Features Based Dosimetric Pareto Front Prediction In Esophagus Cancer Radiotherapy
Authors: Wang J , Jin X , Zhao K , Peng J , Hu W 
Published: July 2014

Purpose:The purpose of this study is to study the feasibility of the dosimetric pareto front (PF) prediction based on patient anatomic and dosimetric parameters for esophagus cancer patients.Methods:Sixty esophagus patients in our institution were enrolled in this study. A total 2920 IMRT plans were created to generated PF for each patient. On average, each patient had 48 plans. The anatomic and dosimetric features were extracted from those plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose and PTV homogeneous index (PTVHI) were recorded for each plan. The principal component analysis (PCA) was used to extract overlap volume histogram (OVH) features between PTV and other critical organs. The full dataset was separated into two parts include the training dataset and the validation dataset. The prediction outcomes were the MHD and MLD for the current study. The spearman rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The PF was fit by the the stepwise multiple regression method. The cross‐validation method was used to evaluation the model.Results:The mean prediction error of the MHD was 465 cGy with 100 repetitions. The most correlated factors were the first principal components of the OVH between heart and PTV, and the overlap between heart and PTV in Z‐axis. The mean prediction error of the MLD was 195 cGy. The most correlated factors were the first principal components of the OVH between lung and PTV, and the overlap between lung and PTV in Z‐axis.Conclusion:It is feasible to use patients anatomic and dosimetric features to generate a predicted PF. Additional samples and further studies were required to get a better prediction model.
TU‐C‐17A‐12: Towards a Passively Optimized Phase‐Space Monte Carlo (POPMC) Treatment Planning Method: A Proof of Principle
Authors: Yang Y M , Zankowski C , Svatos M , Bednarz B 
Published: July 2014

Purpose:The advent of on‐line/off‐line adaptive, and biologically‐conformal radiation therapy has led to a need for treatment planning solutions that utilize voxel‐specific penalties, requiring optimization over a large solution space that is performed quickly, and the dose in each voxel calculated accurately. This work proposes a “passive” optimization framework, which is executed concurrently during Monte Carlo dose calculation, evaluating the cost/benefit of each history during transport, and creates a passively optimized fluence map.Methods:The Monte Carlo code Geant4 v9.6 was used for this study. The standard voxel geometry implementation was modified to support the passive optimization framework, with voxel‐specific optimization parameters. Dose‐benefit functions, which will increase a particle history’s weight upon dose deposition, were defined in a central collection of voxels to effectively create target structures. Histories that deposit energy to voxels are reweighted based on a voxel’s dose multiplied by its cost/benefit value. Upon full termination of each history, the dose contributions of that history are reweighted to reflect a contribution proportional to the history’s final weight. A parallel‐planar 1.25 MeV photon fluence is transported through the geometry, and re‐weighted at each dose deposition step. The resulting weight is tallied with the incident spatial and directional coordinates in a phase‐space distribution.Results:A uniform incident fluence was reweighted during MC dose calculations to create an optimized fluence map which would generate dose profiles in target volumes that exhibit the same dose characteristics as the prescribed optimization parameters. An optimized dose profile, calculated concurrently with the phase‐space, reflects the resulting dose distribution.Conclusion:This study demonstrated the feasibility of passively optimizing an incident fluence map during Monte Carlo dose calculations. The flexibility of the voxel‐specific optimization framework allows a variety and combination of optimization parameters to be calculated for each voxel at every transportation step.This work is partially supported by Varian. This work is partially supported by the NIH.
TU‐C‐12A‐01: Measurement of Skeletal Muscle Lipids in Type 2 Diabetes Using in Vivo Proton MR Spectroscopy
Authors: Valaparla S , Boone G , Ripley E , Abdul‐Ghani M , Duong T , Clarke G 
Published: July 2014

Purpose:To quantify and compare the intramyocellular (IMCL), extramyocellular (EMCL) lipids and total fat fraction in human vastus lateralis (VL) muscle between lean controls and type 2 diabetic (T2DM) subjects using long echo time in vivo proton MR spectroscopy (1H‐MRS)Methods:1H‐MRS single voxel (15 × 15 × 15 mm3) stimulated acquisition mode (STEAM) was performed in right vastus lateralis m. on 10 lean controls (age: 28.3±3.94 yrs, BMI: 24.25±3.20 kg/m2) and 7 type 2 diabetic (age: 54.28±6.42 yrs, BMI: 31.34±3.13 kg/m2) subjects with Siemens 3T MRI and four‐channel flex coil. Unsuppressed water spectra (NSA = 16) with TR/TE = 3000/30 msec, TM = 10 msec, BW = 2000 Hz, and water‐suppressed spectra (NSA = 128) with TR/TE = 3000/270 msec, TM = 10 msec, and fixed water suppression BW = 50 Hz were acquired. Spectral intensity ratios of IMCL‐CH2, EMCL‐CH2 and total lipid (IMCL + EMCL) with unsuppressed water signal (W) were converted into absolute concentrations expressed in mmol/kg. Fat fraction (100 × F/(W+F)) was calculated, where F includes the signal intensities of IMCL and EMCL methylene (CH2)n, peaks only.Results:Comparison of IMCL (controls: 11.70 ± 6.7, T2DM: 21.74 ± 10.2, p ≤ 0.01), EMCL (controls: 22.89 ± 18.42, T2DM: 77.21 ± 33.4, p ≤ 0.001) and total lipid (64.35 mmol/kg less in controls, p ≤ 0.001) showed statistical significance using two‐tailed student t‐test. Fat fraction (%) exhibited considerable inter‐individual variability for controls (3.14 ± 2.09; range: 1.34 – 7.04) and T2DM (9.34 ± 2.88; range: 4.15 – 13.67) and deemed significant (p ≤ 0.05Conclusion:Single voxel STEAM 1H‐MRS at long TE provides a robust non‐invasive method for characterizing lipids within localized muscle regions, with well‐resolved IMCL/EMCL peak separation. Regional lipid estimate and fat fraction in VL m. was significantly different in T2DM compared to lean controls.American Heart Association Southwest Affiliate Pre‐doctoral Fellowship
TU‐C‐12A‐03: Functional MRI of Esophageal Cancer: Repeatability and Inter‐Observer Reproducibility of Geometrically Corrected ADC Maps
Authors: van Lier A , van Rossum P , Lips I , Philippens M , Meijer G , van den Berg C , van Vulpen M , Lagendijk J 
Published: July 2014

Purpose:In preparation to treatment response monitoring with diffusion weighted imaging (DWI) of neoadjuvant chemoradiation of esophageal cancer, we determined the repeatability and inter‐observer reproducibility of the apparent diffusion coefficient (ADC). Geometrical corrections were performed prior to analysis, to enable multi‐parametric analysis on the same ROI.Methods:Ten patients with biopsy‐proven esophageal cancer underwent an MRI exam before, during and after neoadjuvant chemoradiotherapy. The scan protocol consisted of an anatomical T2w scan (TE/TR = 100/1938ms), a DWI scan (b = 0, 200, 800 s/mm2, voxel size 3.25×3.25mm) and a B0 map to monitor magnetic field distortions (dual echo, dual acquisition, TE1/TE2 = 4.6/9.2ms). DWI scans were obtained with a single shot echo‐planar read‐out (BW_phaseenc ∼ 30 Hz/pixel). Two acquisitions were performed with opposing phase‐encoding gradient directions, which results in opposing geometrical distortions. Subsequently, both DWI scans were geometrically corrected using the B0 map, and ADC maps were calculated. For analysis of the inter‐observer reproducibility, two clinicians delineated the tumor volume on the T2w image. The ROIs were transferred to the ADC map. Finally, the repeatability was determined by comparing the ADC within one ROI between the two DWI scans in an exam. The inter‐observer reproducibility was determined by comparing the ADC values within the two ROIs.Results:The average geometrical distortion over all ROIs was 2.67mm (range: 0.02‐17.66mm). The repeatability of geometrical corrected ADC maps was 10.0%, the inter‐observer reproducibility 7.9%. Both percentages are 95%‐confidence intervals (1.96x standard deviation of median ADC).Conclusion:Geometrical correction was required as considerable distortions were observed. Corrected ADC maps of esophageal cancer are associated with a modest repeatability error and inter‐observer reproducibility error, which are in the same range compared to previous reports in other malignancies. In future, we will incorporate repeatability and reproducibility data in MRI‐based (multi‐parametric) treatment response models of esophageal cancer.
TU‐C‐12A‐07: Characterization of Longitudinal Reproducibility of Quantitative Diffusion Imaging Data Acquired with Four Different Protocols Using a Phantom
Authors: Li X , Buzzelli M , Randazzo W , Yanasak N 
Published: July 2014

Purpose:To characterize and compare the longitudinal reproducibility of diffusion imaging data acquired with four different protocols using a phantom.Methods:The Diffusive Quantitative Imaging Phantom (DQIP) was constructed using fifteen cylindrical compartments within a larger compartment, filled with deionized water doped with CuSO4 and NaCl. The smaller compartments contained arrays of hexagonal or cylindrical glass capillaries of varying inner diameters, for differing restraint of water diffusion. The sensitivity of diffusion imaging metrics to signal‐to‐noise ratio (SNR) was probed by doping compartments with differing ratios of deuterium oxide to H2O. A cork phantom enclosure was constructed to increase thermal stability during scanning and a cork holder was made to reproduce scanner positioning. Four different protocols of DWI (diffusion weighted imaging) and DTI (Diffusion tensor imaging) imaging were assembled on a GE Excite HDx 3.0T MRI scanner to collect imaging data over 9‐10 days. Data was processed with in‐house software created in Matlab to obtain fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values.Results:All DTI and DWI sequences showed good longitudinal stability of mean FA and ADC values per compartment, exhibiting low standard deviation ∼9%. A t‐test was performed to compare mean FA values from the DTI clinical protocol to those of the DTI special protocol, indicating significantly different values in the majority of compartments. ANOVA performed on ADC values for all DTI and DWI sequences also showed significantly different values in a majority of compartments.Conclusion:This work has the potential for quantifying systemic variations between diffusion imaging sequences from different platforms. Characterization of DWI and DTI performance were done over four sequences with predictable results. This data suggests that the DQIP phantom may be a reliable method of monitoring day‐to‐day and scan‐to‐scan variation in diffusion imaging sequences from different platforms.Schott Glass North America and The Phantom Laboratory have donated materials and personnel time to this project.
TU‐C‐12A‐11: Comparisons Between Cu‐ATSM PET and DCE‐CT Kinetic Parameters in Canine Sinonasal Tumors
Authors: La Fontaine M , Bradshaw T , Kubicek L , Forrest L , Jeraj R 
Published: July 2014

Purpose:Regions of poor perfusion within tumors may be associated with higher hypoxic levels. This study aimed to test this hypothesis by comparing measurements of hypoxia from Cu‐ATSM PET to vasculature kinetic parameters from DCE‐CT kinetic analysis.Methods:Ten canine patients with sinonasal tumors received one Cu‐ATSM PET/CT scan and three DCE‐CT scans prior to treatment. Cu‐ATSM PET/CT and DCE‐CT scans were registered and resampled to matching voxel dimensions. Kinetic analysis was performed on DCE‐CT scans and for each patient, the resulting kinetic parameter values from the three DCE‐CT scans were averaged together. Cu‐ATSM SUVs were spatially correlated (rspatial) on a voxel‐to‐voxel basis against the following DCE‐CT kinetic parameters: transit time (t1), blood flow (F), vasculature fraction (v1), and permeability (PS). In addition, whole‐tumor comparisons were performed by correlating (rROI) the mean Cu‐ATSM SUV (SUVmean) with median kinetic parameter values.Results:The spatial correlations (rspatial) were poor and ranged from ‐0.04 to 0.21 for all kinetic parameters. These low spatial correlations may be due to high variability in the DCE‐CT kinetic parameter voxel values between scans. In our hypothesis, t1 was expected to have a positive correlation, while F was expected to have a negative correlation to hypoxia. However, in wholetumor analysis the opposite was found for both t1 (rROI = ‐0.25) and F (rROI = 0.56). PS and v1 may depict angiogenic responses to hypoxia and found positive correlations to Cu‐ATSM SUV for PS (rROI = 0.41), and v1 (rROI = 0.57).Conclusion:Low spatial correlations were found between Cu‐ATSM uptake and DCE‐CT vasculature parameters, implying that poor perfusion is not associated with higher hypoxic regions. Across patients, the most hypoxic tumors tended to have higher blood flow values, which is contrary to our initial hypothesis.Funding: R01 CA136927
TU‐C‐12A‐12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning
Authors: Perk T , Bradshaw T , Meyer E , Muzahir S , Jeraj R 
Published: July 2014

Purpose:[F‐18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F‐18]NaF scans that correspond to DJD.Methods:10 metastatic prostate cancer patients received whole body [F‐18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to train eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers.Results:The training set consisted of 37 DJD ROIs with 475 non‐DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non‐DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT.Conclusion:Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone.Research supported by the Prostate Cancer Foundation
TU‐F‐BRE‐01: A High Resolution Micro Fiber Scintillator Detector Optimized for SRS and SBRT in Vivo Real Time Treatment Verification
Authors: Izaguirre E , Price S , Knewtson T , Loyalka S , Rangaraj D 
Published: July 2014

Purpose:We have built a high resolution real time scintillating fiber detector prototype to determine in real time the accuracy of stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT) treatments when only a fraction of the planned dose was delivered. The motivation of this work is to enhance dose delivery accuracy and to achieve error free radiosurgery.Methods:A high density array of scintillating fibers and a high speed photo detectors array were integrated to implement a high resolution real time dosimeter that can sample with high resolution pulsed SRS and SBRT beams cross sections. The high efficiency of the developed system allows to read each linac pulse in real time and to compute the accumulated dose and dose errors when only a fraction of the beam was delivered. The fibers are highly packed in a substrate that is directly coupled to two 128 pixel arrays with a pitch matching the fiber spacing to achieve accurate spatial localization. The small cross section of the fiber array allows stacking multiple fiber arrays to measure independent angular profiles that are digitally processed in parallel for real time dosimetry.Results:We implemented a high density array detector prototype with a pitch of 0.5 mm, readout speed of 1.2 msec, and a response time of 0.5 usec. The fast reading speed has the capability to determining the dose in flattening free filter beams. The detector can be installed in transmission mode at the output port of a micro‐MLC. Treatment deviations smaller than 3% are detected when less than 1/100 of the planned dose was delivered.Conclusions:We built a prototype of a high resolution fiber scintillator array detector for SRS and SBRT in vivo dosimetry. Results show that the developed detector has the potential to assure error free SRS and SBRT treatments.
TU‐F‐BRE‐02: A Practical QA Method for Respiration‐Gated SBRT Verification Using 2D Detector Array
Authors: Chen X , Fan J , Lin T , Price R , Chen L , Ma C 
Published: July 2014

Purpose:Pre‐treatment plan verification for respiration‐gated SBRT is challenging but desired since this type of treatment involve more delivery uncertainty, e.g., tumor motion effect, tissue heterogeneity and gating system QA. This study is to design and verify a QA method for respirationgated SBRT pre‐treatment verification using a 2D detector array.Methods:A lung phantom was designed based on the MatriXX QA system, which includes a detector board and a MultiCube phantom. The major modification was that an air chamber (∼5cm thick) was inserted between the top portion of MultiCube phantom and detectors board. A half plastic ball (4cm diameter) was put in the air chamber and attached to the detector board surface to mimic a tumor. The whole lung phantom was placed on a moving stage with a superior‐inferior sinusoidal motion. The whole phantom was aligned to the treatment machine so that the S‐I and L‐R axes of the MatriXX were parallel to the corresponding machine axes during movement. In this way, the moving phantom is equivalent to a stationary phantom with only tumor motion. Following a typical planning procedure for respiration‐gated SBRT treatment, SBRT plans were created using the gated‐MIP as PTV. Treatment delivery was performed on Varian iX machine with respiration gating.Results:The measured 2D plane dose showed blurring effect induced by motion and this blurring effect was more noticeable at the peripheral region of the PTV. Nevertheless, the tumor dose was able to be identified by analyzing the detectors attached to the tumor and was well above the prescribed dose.Conclusion:The designed lung phantom for respiration‐gated SBRT pre‐treatment verification was tested and was able to identify the dose received by the tumor. Further tests based on large number of patients would be needed to for future routine application.
TU‐F‐BRE‐06: Flattening‐Filter‐Free Beam Quality Correction Factor Determination Using Experimental and Monte Carlo Methods
Authors: Wood M , Desai V , Simiele E , Taneja S , DeWerd L 
Published: July 2014

Purpose:To investigate beam quality correction factors for the flattening‐filter‐free (FFF) energies of the TrueBeam™ accelerator based on a dosimetry formalism for small and nonstandard fields.Methods:Three detectors – an Exradin W1 scintillator, Sun Nuclear EDGE diode, and LiF(Mg,Tl) TLD‐100 chips – were investigated to determine their applicability as tools to measure quality correction factors for ionization chambers in the small and nonstandard fields of the TrueBeam™. Volume‐averaging effects and energy dependence were observed in fields ranging from 1×1 to 40×40 cm2 for 6 MV and 10 MV beam energies using both FFF and flattened beam modes. Correction factors were determined for three ionization chambers: an Exradin A12 Farmer‐type chamber, an Exradin A1SL scanning chamber, and an Exradin A26 reference‐class microchamber. Beam quality corrections were also obtained using a benchmarked model of the TrueBeam™ created with the BEAMnrc user code of EGSnrc.Results:All three detectors demonstrated measureable energy dependence in the megavoltage range. The EDGE diode was deemed the most appropriate tool for beam quality correction factor measurements due to its low energy dependence and small size; however, alanine will be used in the future to reduce energy dependent effects even further. Measured kQmsr,Q corrections of up to 4% were found for the 6MV FFF and 10 MV FFF beams, corresponding to a discrepancy of up to 3% compared to TG‐51‐determined dose. Up to a 10% kQclin,Qmsr correction was measured for small fields referenced to a 10×10 cm2 field of the same energy. Much larger corrections were determined using the Monte Carlo model, and these discrepancies require further investigation.Conclusion:Progress has been made toward determining beam quality correction factors for the small and nonstandard fields of the TrueBeam™ accelerator. Further work must be done to ensure greater accuracy in patient treatments with this new modality.
TU‐F‐BRD‐01: Automatic Segmentation of Multiple Pelvic Organs On a Web‐Based Image Processing and Plan Evaluation Platform (WIPPEP)
Authors: Chai X , Liu L , Xing L 
Published: July 2014

Purpose:To present a novel automatic segmentation method for multiple pelvic organs and integrate into the web‐based image processing and plan evaluation platform (WIPPEP) to provide easy access through a web browser and leverage the power of cloud for faster computation.Methods:This segmentation method uses principal component analysis to model organ shapes and hierarchical clustering to model the appearance of each organ surface patch. Coarse and refined segmentation are done iteratively for each surface patch to find the best reference patch profile via a hierarchical cluster decision tree. Final segmentation is obtained using the generated global reference profile. The user chooses the CT image data to be segmented from the image server; the image volume is then visualized in the web browser and an HTTP POST request is sent to the computation server by clicking a button. When the computation server receives the segmentation request, it would then send an HTTP GET request to the image server to download the image volumes, call the segmentation program, and eventually send the segmentation results back to the web server. The segmentation results are transferred in JSON format and quickly passed to the canvas element in the web browser to display the contours.Results:For 5 test patients, the average DICE overlaps for prostate, bladder and rectum were 0.75, 0.78 and 0.57, respectively. With the platform running on hospital intranet, it took 20 seconds to load CT image volume to the web‐browser and 80 seconds to receive segmentation results from the computation server.Conclusion:This system allows users to access image data and send segmentation requests from a web browser, perform segmentation on a computation server, and quickly visualize resulting segmentation contours on a web browser. The software prototype achieves auto‐segmentation of prostate, bladder and rectum with moderate accuracy in a minute's timeframe.