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At least 379 records · Page 21
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DOE OSTI2016

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-201-01: A Multi-Institutional Study Quantifying the Impact of Simulated Linear Accelerator VMAT Errors for Nasopharynx

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.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-13: Decoding 18F-FDG Uptake Heterogeneity for Primary and Lymphoma Tumors by Using Texture Analysis in PET Images

Purpose: To explore 18F-FDG uptake heterogeneity of primary tumor and lymphoma tumor by texture features of PET image and quantify the heterogeneity difference between primary tumor and lymphoma tumor. Methods: 18 patients with primary tumor and lymphoma tumor in lung cancer were enrolled. All patients underwent whole-body 18F-FDG PET/CT scans before treatment. Texture features, based on Gray-level Co-occurrence Matrix, second and high order matrices are extracted from code using MATLAB software to quantify 18F-FDG uptake heterogeneity. The relationships of volume between energy, entropy, correlation, homogeneity and contrast were analyzed. Results: For different cases, tumor heterogeneity was not the same. Texture parameters (contrast, entropy, and correlation) of lymphoma were lower than primary tumor. On the contrast, the texture parameters (energy, homogeneity and inverse different moment) of lymphoma were higher than primary tumor. Significantly, correlations were observed between volume and energy (primary, r=−0.194, p=0.441; lymphoma, r=−0.339, p=0.582), homogeneity (primary, r=−0.146, p=0.382; lymphoma, r=−0.193, p=0.44), inverse difference moment (primary, r=−0.14, p=0.374; lymphoma, r=−0.172, p=0.414) and a positive correlation between volume and entropy (primary, r=0.233, p=0.483; lymphoma, r=0.462, p=0.680), contrast (primary, r=0.159, p=0.399; lymphoma, r=0.341, p=0.584), correlation (primary, r=0.027, p=0.165; lymphoma, r=0.046, p=0.215). For the same patient, energy for primary and lymphoma tumor is equal. The volume of lymphoma is smaller than primary tumor, but the homogeneity were higher than primary tumor. Conclusion: This study showed that there were effective heterogeneity differences between primary and lymphoma tumor by FDG-PET image texture analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-33: Can CT and CBCT Be Used Simultaneously for Radiomics Analysis

Purpose: To investigate whether CBCT and CT can be used in radiomics analysis simultaneously. To establish a batch correction method for radiomics in two similar image modalities. Methods: Four sites including rectum, bladder, femoral head and lung were considered as region of interest (ROI) in this study. For each site, 10 treatment planning CT images were collected. And 10 CBCT images which came from same site of same patient were acquired at first radiotherapy fraction. 253 radiomics features, which were selected by our test-retest study at rectum cancer CT (ICC>0.8), were calculated for both CBCT and CT images in MATLAB. Simple scaling (z-score) and nonlinear correction methods were applied to the CBCT radiomics features. The Pearson Correlation Coefficient was calculated to analyze the correlation between radiomics features of CT and CBCT images before and after correction. Cluster analysis of mixed data (for each site, 5 CT and 5 CBCT data are randomly selected) was implemented to validate the feasibility to merge radiomics data from CBCT and CT. The consistency of clustering result and site grouping was verified by a chi-square test for different datasets respectively. Results: For simple scaling, 234 of the 253 features have correlation coefficient ρ>0.8 among which 154 features haveρ>0.9 . For radiomics data after nonlinear correction, 240 of the 253 features have ρ>0.8 among which 220 features have ρ>0.9. Cluster analysis of mixed data shows that data of four sites was almost precisely separated for simple scaling(p=1.29 * 10{sup −7}, χ{sup 2} test) and nonlinear correction (p=5.98 * 10{sup −7}, χ{sup 2} test), which is similar to the cluster result of CT data (p=4.52 * 10{sup −8}, χ{sup 2} test). Conclusion: Radiomics data from CBCT can be merged with those from CT by simple scaling or nonlinear correction for radiomics analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-50: Radiation-Induced Changes in CT Number Histogram During Chemoradiation Therapy for Pancreatic Cancer

Purpose: In an effort of early assessment of treatment response, we investigate radiation induced changes in CT number histogram of GTV during the delivery of chemoradiation therapy (CRT) for pancreatic cancer. Methods: Diagnostic-quality CT data acquired daily during routine CT-guided CRT using a CT-on-rails for 20 pancreatic head cancer patients were analyzed. All patients were treated with a radiation dose of 50.4 in 28 fractions. On each daily CT set, the contours of the pancreatic head and the spinal cord were delineated. The Hounsfiled Units (HU) histogram in these contourswere extracted and processed using MATLAB. Eight parameters of the histogram including the mean HU over all the voxels, peak position, volume, standard deviation (SD), skewness, kurtosis, energy, and entropy were calculated for each fraction. The significances were inspected using paired two-tailed t-test and the correlations were analyzed using Spearman rank correlation tests. Results: In general, HU histogram in pancreatic head (but not in spinal cord) changed during the CRT delivery. Changes from the first to the last fraction in mean HU in pancreatic head ranged from −13.4 to 3.7 HU with an average of −4.4 HU, which was significant (P<0.001). Among other quantities, the volume decreased, the skewness increased (less skewed), and the kurtosis decreased (less sharp) during the CRT delivery. The changes of mean HU, volume, skewness, and kurtosis became significant after two weeks of treatment. Patient pathological response status is associated with the changes of SD (ΔSD), i.e., ΔSD= 1.85 (average of 7 patients) for good reponse, −0.08 (average of 6 patients) for moderate and poor response. Conclusion: Significant changes in HU histogram and the histogram-based metrics (e.g., meam HU, skewness, and kurtosis) in tumor were observed during the course of chemoradiation therapy for pancreas cancer. These changes may be potentially used for early assessment of treatment response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-55: Early Detection of Treatment Induced Bone Marrow Injury During Chemoradiation Therapy Using Quantitative CT

Purpose: Acute hematologic toxicity associated with bone marrow injury is a common complication of chemoradiation therapy (CRT) for pelvic malignancies. In this work, we investigate the feasibility of using quantitative CT to detect bone marrow injury during CRT. Methods: Daily CTs were acquired during routine CT-guided radiation therapy using a CT-on-rails for 15 cervical cancer patients. All patients treated with a radiation dose of 45.0 to 50.4 Gy in 1.8 Gy/fraction along with chemotherapy. For each patient, the contours of bone marrow were generated in L4, L5 and sacrum on the first daily CT and then populated to other daily CTs by rigid registration using MIM (MIM Software Inc., Cleveland, OH) with manual editing if possible. A series of CT texture parameters, including Hunsfield Unit (HU) histogram, mean HU, entropy, energy, in bone marrow contours were calculated using MATLAB on each daily CT and were correlated with the completed blood counts (CBC) collected weekly for each patient. The correlations were analyzed with Pearson correlation tests. Results: For all patient data analyzed, mean HU in bone marrow decreased during CRT delivery. From the first to the last fraction the average mean HU reduction is 58.1 ± 13.6 HU (P<0.01). This decrease can be observed as early as after first 5 fractions and is strongly associated with the changes of most CBC quantities, such as the reductions of white and blood cell counts (r=0.97, P=0.001). The reduction of HU is spatially varied. Conclusion: Chemoradiation induced bone marrow injury can be detected during the delivery of CRT using quantitative CT. Chemoradiation results in reductions in mean HU, which are strongly associated with the change in the pretrial blood cell counts. Early detection of bone marrow injury with commonly available CT opens a door to improve bone marrow sparing, reducing risk of hematologic toxicity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-43: A Software-Based Statistical Method to Compute Low Contrast Detectability in Computed Tomography Images

Purpose: The low contrast detectability (LCD) of a CT scanner is its ability to detect and display faint lesions. The current approach to quantify LCD is achieved using vendor-specific methods and phantoms, typically by subjectively observing the smallest size object at a contrast level above phantom background. However, this approach does not yield clinically applicable values for LCD. The current study proposes a statistical LCD metric using software tools to not only to assess scanner performance, but also to quantify the key factors affecting LCD. This approach was developed using uniform QC phantoms, and its applicability was then extended under simulated clinical conditions. Methods: MATLAB software was developed to compute LCD using a uniform image of a QC phantom. For a given virtual object size, the software randomly samples the image within a selected area, and uses statistical analysis based on Student’s t-distribution to compute the LCD as the minimal Hounsfield Unit’s that can be distinguished from the background at the 95% confidence level. Its validity was assessed by comparison with the behavior of a known QC phantom under various scan protocols and a tissue-mimicking phantom. The contributions of beam quality and scattered radiation upon the computed LCD were quantified by using various external beam-hardening filters and phantom lengths. Results: As expected, the LCD was inversely related to object size under all scan conditions. The type of image reconstruction kernel filter and tissue/organ type strongly influenced the background noise characteristics and therefore, the computed LCD for the associated image. Conclusion: The proposed metric and its associated software tools are vendor-independent and can be used to analyze any LCD scanner performance. Furthermore, the method employed can be used in conjunction with the relationships established in this study between LCD and tissue type to extend these concepts to patients’ clinical CT images.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-50: Finite Element-Based Deformable Image Registration of Lung and Heart

Purpose: Photodynamic therapy (PDT) is used after surgical resection to treat the microscopic disease for malignant pleural mesothelioma and to increase survival rates. Although accurate light delivery is imperative to PDT efficacy, the deformation of the pleural volume during the surgery impacts the delivered light dose. To facilitate treatment planning, we use a finite-element-based (FEM) deformable image registration to quantify the anatomical variation of lung and heart volumes between CT pre-(or post-) surgery and surface contours obtained during PDT using an infrared camera-based navigation system (NDI). Methods: NDI is used during PDT to obtain the information of the cumulative light fluence on every cavity surface point that is being treated. A wand, comprised of a modified endotrachial tube filled with Intralipid and an optical fiber inside the tube, is used to deliver the light during PDT. The position of the treatment is tracked using an attachment with nine reflective passive markers that are seen by the NDI system. Then, the position points are plotted as three-dimensional volume of the pleural cavity using Matlab and Meshlab. A series of computed tomography (CT) scans of the lungs and heart, in the same patient, are also acquired before and after the surgery. The NDI and CT contours are imported into COMSOL Multiphysics, where the FEM-based deformable image registration is obtained. The NDI and CT contours acquired during and post-PDT are considered as the reference, and the Pre-PDT CT contours are used as the target, which will be deformed. Results: Anatomical variation of the lung and heart volumes, taken at different times from different imaging devices, was determined by using our model. The resulting three-dimensional deformation map along x, y and z-axes was obtained. Conclusion: Our model fuses images acquired by different modalities and provides insights into the variation in anatomical structures over time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-09: Radioactive Seed Localization for Breast Lumpectomy - Towards Optimization

Purpose: The aim of this work is to propose a method to optimize radioactive source localization (RSL) for non-palpable breast cancer surgery. RSL is commonly used as a guiding technique during surgery for excision of non-palpable tumors. A collimated hand-held detector is used to localize radioactive sources implanted in tumors. Incisions made by the surgeon are based on maximum observed detector counts, and tumors are subsequently resected based on an arbitrary estimate of the counts expected at the surgical margin boundary. This work focuses on building a framework to predict detector counts expected throughout the procedure to improve surgical margins. Methods: A gamma detection system called the Neoprobe GDS was used for this work. The probe consists of a cesium zinc telluride crystal and a collimator. For this work, an I-125 Best Medical model 2301 source was used. The source was placed in three different phantoms, a PMMA, a Breast (25%- glandular tissue/75%- adipose tissue) and a Breast (75-25) phantom with a backscatter thickness of 6 cm. Counts detected by the probe were recorded with varying amounts of phantom thicknesses placed on top of the source. A calibration curve was generated using MATLAB based on the counts recorded for the calibration dataset acquired with the PMMA phantom. Results: The observed detector counts data used as the validation set was accurately predicted to within ±3.2%, ±6.9%, ±8.4% for the PMMA, Breast (75-25), Breast (25–75) phantom respectively. The average difference between predicted and observed counts was −0.4%, 2.4%, 1.4% with a standard deviation of 1.2 %, 1.8%, 3.4% for the PMMA, Breast (75-25), Breast (25–75) phantom respectively. Conclusion: The results of this work provide a basis for characterization of a detector used for RSL. Counts were predicted to within ±9% for three different phantoms without the application of a density correction factor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-99: Dose Accumulation and Evaluation in Lung SBRT Among All Phases of Respiration

Purpose: To calculate the total planning dose on lung tumors (GTV) by accumulating the dose received in all respiration phases. Methods: A patient 4D planning CT (phase-binned, from a Siemens Somatom CT) was used to locate the GTV of a lung tumor in all respiratory phases with Pinnacle (v9.10). GTV contours defined in all phases were projected to the reference phase, where the ITV was defined. Centroids were calculated for all the GTV projections. No deformation or rotation was taken into account. The only GTV contour as defined in the reference phase was voxelized to track each voxel individually. We accumulated the absorbed dose in different phases on each voxel. A 3DCRT and a VMAT plan were designed on the reference phase fulfilling the ITV dosimetric requirements, using the 10MV FFF photon model from an Elekta Versa linac. ITV-to-PTV margins were set to 5mm. In-house developed MATLAB code was used for tumor voxeling and dose accumulation, assuming that the dose distribution planned in the reference phase behaved as a “dose-cloud” during patient breathing. Results: We tested the method on a patient 4DCT set of images exhibiting limited tumor motion (<5mm). For the 3DCRT plan, D95 was calculated for the GTV with motion and for the ITV, showing an agreement of 0.04%. For the VMAT plan, we calculated the D95 for every phase as if the GTV in that phase had received the whole treatment. Differences in D95 for all phases are within 1%, and estimate the potential interplay effect during delivery. Conclusion: A method for dose accumulation and assessment was developed that can compare GTV motion with ITV dosage, and estimate the potential interplay effect for VMAT plans. Work in progress includes the incorporation of deformable image registration and 4D CBCT dose calculation for dose reconstruction and assessment during treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-102: Lower Esophagus Margin Implications Based On Rapid Computational Algorithm for SBRT

Purpose: To quantify inter-fraction esophagus-variation. Methods: Computed tomography and daily on-treatment 0.3-T MRI data sets for 7 patients were analyzed using a novel Matlab-based (Mathworks, Natick, MA) rapid computational method. Rigid registration was performed from the cricoid to the gastro-esophageal junction. CT and MR-based contours were compared at slice intervals of 3mm. Variation was quantified by “expansion,” defined as additional length in any radial direction from CT contour to MR contour. Expansion computations were performed with 360° of freedom in each axial slice. We partitioned expansions into left anterior, right anterior, right posterior, and left posterior quadrants (LA, RA, RP, and LP, respectively). Sample means were compared by analysis of variance (ANOVA) and Fisher’s Protected Least Significant Difference test. Results: Fifteen fractions and 1121 axial slices from 7 patients undergoing SBRT for primary lung cancer (3) and metastatic lung disease (4) were analyzed, generating 41,970 measurements. Mean LA, RA, RP, and LP expansions were 4.30±0.05 mm, 3.71±0.05mm, 3.17±0.07, and 3.98±0.06mm, respectively. 50.13% of all axial slices showed variation > 5 mm in one or more directions. Variation was greatest in lower esophagus with mean LA, RA, RP, and LP expansion (5.98±0.09 mm, 4.59±0.09 mm, 4.04±0.16 mm, and 5.41±0.16 mm, respectively). The difference was significant compared to mid and upper esophagus (p<.0001). The 95th percentiles of expansion for LA, RA, RP, LP were 13.36 mm, 9.97 mm, 11.29 mm, and 12.19 mm, respectively. Conclusion: Analysis of on-treatment MR imaging of the lower esophagus during thoracic SBRT suggests margin expansions of 13.36 mm LA, 9.97 mm RA, 11.29 mm RP, 12.19 mm LP would account for 95% of measurements. Our novel algorithm for rapid assessment of margin expansion for critical structures with 360° of freedom in each axial slice enables continuously adaptive patient-specific margins which may reduce overlap with high-dose regions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-174: A Series of Computational Human Phantoms in DICOM-RT Format for Normal Tissue Dose Reconstruction in Epidemiological Studies

Purpose: Epidemiological studies of second cancer risk in radiotherapy patients often require individualized dose estimates of normal tissues. Prior to 3D conformal radiation therapy planning, patient anatomy information was mostly limited to 2D radiological images or not even available. Generic patient CT images are often used in commercial radiotherapy treatment planning system (TPS) to reconstruct normal tissue doses. The objective of the current work was to develop a series of reference size computational human phantoms in DICOM-RT format for direct use in dose reconstruction in TPS. Methods: Contours of 93 organs and tissues were extracted from a series of pediatric and adult hybrid computational human phantoms (newborn, 1-, 5-, 10-, 15-year-old, and adult males and females) using Rhinoceros software. A MATLAB script was created to convert the contours into the DICOM-RT structure format. The simulated CT images with the resolution of 1×1×3 mm3 were also generated from the binary phantom format and coupled with the DICOM-structure files. Accurate volumes of the organs were drawn in the format using precise delineation of the contours in converted format. Due to complex geometry of organs, higher resolution (1×1×1 mm3) was found to be more efficient in the conversion of newborn and 1-year-old phantoms. Results: Contour sets were efficiently converted into DICOM-RT structures in relatively short time (about 30 minutes for each phantom). A good agreement was observed in the volumes between the original phantoms and the converted contours for large organs (NRMSD<1.0%) and small organs (NRMSD<7.7%). Conclusion: A comprehensive series of computational human phantoms in DICOM-RT format was created to support epidemiological studies of second cancer risks in radiotherapy patients. We confirmed the DICOM-RT phantoms were successfully imported into the TPS programs of major vendors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-180: A Reference Data Set for Testing Two Dimension Registration Algorithms

Purpose: To create and characterize a reference data set for testing image registration algorithms that transform portal image (PI) to digitally reconstructed radiograph (DRR). Methods: Anterior-posterior (AP) and Lateral (LAT) projection and DRR image pairs from nine cases representing four different anatomical sites (head and neck, thoracic, abdominal, and pelvis) were selected for this study. Five experts will perform manual registration by placing landmarks points (LMPs) on the DRR and finding their corresponding points on the PI using computer assisted manual point selection tool (CAMPST), a custom-made MATLAB software tool developed in house. The landmark selection process will be repeated on both the PI and the DRR in order to characterize inter- and -intra observer variations associated with the point selection process. Inter and an intra observer variation in LMPs was done using Bland-Altman (B&A) analysis and one-way analysis of variance. We set our limit such that the absolute value of the mean difference between the readings should not exceed 3mm. Later on in this project we will test different two dimension (2D) image registration algorithms and quantify the uncertainty associated with their registration. Results: Using one-way analysis of variance (ANOVA) there was no variations within the readers. When Bland-Altman analysis was used the variation within the readers was acceptable. The variation was higher in the PI compared to the DRR.ConclusionThe variation seen for the PI is because although the PI has a much better spatial resolution the poor resolution on the DRR makes it difficult to locate the actual corresponding anatomical feature on the PI. We hope this becomes more evident when all the readers complete the point selection. The reason for quantifying inter- and -intra observer variation tells us to what degree of accuracy a manual registration can be done. Research supported by William Beaumont Hospital Research Start Up Fund.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-209: Quantification of Image-Guidance Benefit in Image-Guided Radiotherapy of Cancers

Purpose: Image-guidance has been widely used in radiation oncology for accurate radiotherapy. The goal of this study is to quantify the benefit of image-guidance in image-guided radiotherapy (IGRT) of cancers. Methods: In this study, a new index termed image-guidance benefit (IGB), was proposed to quantify the benefit of image-guidance in cancer radiotherapy. It is calculated as a ratio of the square sum of dose differences between planning dose matrix and actual delivery dose matrix post image-guidance to the square sum of dose summation between the two matrixes, summing over all dose scoring voxels. Ranging from 0 to 1, larger IGB values indicate larger benefit out of image-guidance. With IRB approval, the DICOM RT files and 3D couch shifts applied during IGRT of 2219 patients were collected, based on which patient-specific IGB values were calculated with an in-house MATLAB code. Results: In this study, the mean IGB value was found to be 0.0398 (0.000583–0.999) with a positive correlation between IGB value and 3D couch shift vector at 0.0435 per cm (P<0.0001). With 2 mm shift as a threshold above which an image-guidance is deemed clinically necessary, the corresponding mean IGB value was 0.00457, much less than 0.0398 (P<0.001). However, the IGB values of 56 cases based on couch shifts were less than those based on 2 mm shift. Conclusion: The IGB values were patient-specific and site-dependent. Using 2 mm shifts as criterion for applying image-guidance, the applied image-guidance procedures were found clinically necessary and highly beneficial in 97.5% of cancer patients. However, image-guidance procedures were found over-used in about 2.5% of cancer patients in our current practices of IGRT, which should be avoided as they added no benefit in improving delivery accuracy while increasing cancer risk.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels with similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed positions would improve the algorithm speed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-46: The Effect of Inter-Seed Attenuation and Tissue Composition in Prostate 125I Brachytherapy Dose Calculations

Purpose: To investigate the difference of dose distributions with/without the effect of inter-seed attenuation and tissue compositions in prostate {sup 125}I brachytherapy dose calculations, using Monte Carlo simulations of Particle and Heavy Ion Transport code System (PHITS). Methods: The dose distributions in {sup 125}I prostate brachytherapy were calculated using PHITS for non-simultaneous and simultaneous alignments of STM1251 sources in water or prostate phantom for six patients. The PHITS input file was created from DICOM-RT file which includes source coordinates and structures for clinical target volume (CTV) and organs at risk (OARs) of urethra and rectum, using in-house Matlab software. Photon and electron cutoff energies were set to 1 keV and 100 MeV, respectively. The dose distributions were calculated with the kerma approximation and the voxel size of 1 × 1 × 1 mm{sup 3}. The number of incident photon was set to be the statistical uncertainty (1σ) of less than 1%. The effect of inter-seed attenuation and prostate tissue compositions was evaluated from dose volume histograms (DVHs) for each structure, by comparing to results of the AAPM TG-43 dose calculation (without the effect of inter-seed attenuation and prostate tissue compositions). Results: The dose reduction due to the inter-seed attenuation by source capsules was approximately 2% for CTV and OARs compared to those of TG-43. In additions, by considering prostate tissue composition, the D{sub 90} and V{sub 100} of CTV reduced by 6% and 1%, respectively. Conclusion: It needs to consider the dose reduction due to the inter-seed attenuation and tissue composition in prostate {sup 125}I brachytherapy dose calculations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-73: Experimental Determination of the Effective Point of Measurement in Electron Beams Using a Commercial Scintillation Detector

Purpose: The aim of this work was to determine experimentally the effective point of measurement (EPOM) in clinical electron beams for three cylindrical ionization chambers using a commercial scintillation detector as a reference detector. Methods: Percent depth dose (PDD) curves were measured using an Exradin W1 scintillation detector and were used as a representative PDD to water. Depth dose curves were measured with the Exradin A18, A1SL, and A28 ionization chambers. The raw ionization chamber curve data were corrected by the chamber fluence perturbation correction factor and restricted mass collisional stopping power ratio at each depth to obtain a percent depth dose curve to the gas volume (PDDGV) of the detector. Ratios of the W1 PDD to the ion chamber PDDGV were calculated for each measurement depth. The W1 PDD curve was shifted by small depth increments, Δz, until the ratio of the W1 PDD to the ion chamber PDDGV was depth-independent (optimal Δz). A MATLAB routine was developed to determine the optimal Δz value. Results: The optimal Δz shift was used as an estimate of the EPOM for each chamber. The average calculated EPOM shifts (expressed as a fraction of the chamber cavity radius) for the A18, A1SL, and A28 ionization chambers were 0.21 ± 0.04, 0.10 ± 0.05, and 0.22 ± 0.03, respectively. Conclusion: The experimentally determined EPOM values for the A18 and A1SL in this work agreed with the simulated values of Muir and Rogers (MedPhys 2014). The results also indicate that the Exradin W1 scintillator is water equivalent for electron energies of 6 MeV, 9 MeV, 12 MeV, and 16 MeV. In addition, we confirmed that the AAPM TG51 recommended EPOM shift of 0.5 times the cavity radius is not accurate for the A18 and A1SL chambers.

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