bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐106: Atlas‐Based Segmentation: Evaluation of a Multi‐Atlas Approach for Lung Cancer
Authors: Pirozzi S , Horvat M , Piper J , Nelson A
Published: June 2012

Purpose: Previous studies have shown atlas‐based segmentation using a single best matched (SBM) atlas subject can significantly reduce contouring time. A new multi‐atlas approach has been shown to provide greater accuracy than SBM for cancer of the head and neck. The goal of this study was to evaluate the multi‐atlas technique for lung cancer treatment planning. Methods: An institution's SBRT lung atlas containing 82 subjects was utilized for atlas segmentation. Each atlas subject contained manually defined contours of the esophagus, cord, heart, left lung, right lung, and trachea. CT scans and contours for 16 subjects were evaluated. SBM used the one automatically determined best match for segmentation. Multi‐atlas used multiple automatically determined best matches: 3, 4, and 5, respectively. The final segmentation for multi‐atlas was generated using Majority Vote which comprises the area of overlap for at least half of the individual segmentations (2 of 3, 2 of 4, and 3 of 5, respectively). Average Dice Similarity Coefficients (DSC) were calculated for each structure to compare against manually defined ‘gold’ standard contours for that subject. Overall percent improvement was calculated as the proportion of the error corrected by the method, or % difference on 1‐DSC. Results: All multi‐atlas methods were significantly more accurate than SBM (p‐value < 0.0005) with average DSC of 0.802 +/− 0.172, 0.809 +/ 0.163, 0.802 +/− 0.182 respectively for Multi‐3, Multi‐4, and Multi‐5 compared to 0.773 +/− 0.187 for SBM. No significant differences existed between the different multi‐atlas approaches. Overall, Multi‐4 showed the greatest improvement over SBM with 16% improvement followed by Multi‐3 and Multi‐5 at 12%. Conclusions: Each multi‐atlas approach resulted in significantly more accurate contours compared to the SBM. While still requiring some editing, this method for segmentation using multiple atlases shows promise for further decreasing the contouring time required for lung cancer. MIM Software Inc.
SU‐E‐J‐108: Quantitative Analysis of Longitudinal Cognitive Impairment Due to Radiation Therapy Based on Automatic Segmentation of Hippocampus and Subcortical Structure
Authors: Lin Y , Chang D , Bota D , Roa D , Al‐Ghazi M , Yu H , Kuo J , Nie K , Fwu P , Su M
Published: June 2012

Purpose: In this study, we developed a quantitative analysis tool based on patient's longitudinal MR images to 1) measure the radiation dose received by each subcortical structure, 2) follow the change of volume and shape of each structure longitudinally. This tool provides a systematic approach to study the radiation therapy (and subsequent chemotherapy) associated with cognitive impairments. Methods: MRI scans of one patient taken before and after radiation therapy are demonstrated in this study. 3D Conformal radiation therapy was performed on RapidArc™. An open source MRI analysis tool, FMRIB's Integrated Registration and Segmentation Tool (FIRST), was used for segmentation. The images are registered to a standard template with expert‐defined labeling for all sub‐cortical structures, and the labeling of each structure is mapped back to the individual MRI space for segmentation. After the segmentation, the radiation dose map was coregistered to the MRI space to calculate the dose received by each structure. Results: For the structure that is contained within the radiation zone, we can calculate the total dose based on the volumetric distribution of radiation dose. For the structure that is outside the radiation field, we can calculate the distance from the radiation zone. We have demonstrated in this work that the analysis can be done for all segmented sub‐cortical structures. The change of volume before and after radiation treatment can be analyzed, and the results can be correlated with the change of cognitive performance over time. Conclusions: We presented an automated tool for efficient, quantitative and user‐independent measurements of radiation dose in subcortical structures. The obtained results can be correlated with the cognitive test score and the clinical outcome to evaluate radiation and the subsequent chemotherapy induced changes in brain structures and functions.
SU‐E‐J‐110: A Novel Level Set Active Contour Algorithm for Multimodality Joint Segmentation/Registration Using the Jensen‐Rényi Divergence
Authors: Markel D , Naqa I El , Freeman C , Vallières M
Published: June 2012

Purpose: To present a novel joint segmentation/registration for multimodality image‐guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen‐Renyi (JR) divergence to achieve improved noise robustness in a multi‐modality imaging space. Methods: To present a novel joint segmentation/registration for multimodality image‐guided and adaptive radiotherapy. A major challenge to this framework is the sensitivity of many segmentation or registration algorithms to noise. Presented is a level set active contour based on the Jensen‐Renyi (JR) divergence to achieve improved noise robustness in a multi‐modality imaging space. Results: It was found that JR divergence when used for segmentation has an improved robustness to noise compared to using mutual information, or other entropy‐based metrics. The MI metric failed at around 2/3 the noise power than the JR divergence. Conclusions: The JR divergence metric is useful for the task of joint segmentation/registration of multimodality images and shows improved results compared entropy based metric. The algorithm can be easily modified to incorporate non‐intensity based images, which would allow applications into multi‐modality and texture analysis.
SU‐E‐J‐111: Compute the Optimal Threshold for PET Functional Volume Delineation
Authors: Tan S , Chen W , D'Souza W , Lu W
Published: June 2012

Purpose: To construct a method being able to compute the optimal threshold (OT) directly from PET images for functional volume delineation without any a priori information about scanners, imaging protocols and tumor size. Methods: A mathematic model for partial volume effect (PVE) was analyzed for a spherical object with a constant activity imaged by a scanner with Gaussian point spread function (PSF). The theoretic analysis showed that information about some factors that are related to the PVE such as the PSF and tumor size had been actually embedded into PET images. We developed a new concept ‐ the Optimal Area Ratio (OAR) that is a function of the object radius and can be derived from the model with some mathematic manipulations. With this new concept, information about PSF and tumor size can be recovered directly from PET images, and the OT can be calculated accordingly. The basic idea is to use the whole dynamic behavior (the behavior of the volume sizes due to different thresholds uniformly distributed in [0,1]) of a region‐growing algorithm to find the OT. A computer simulation and experiments on phantoms with different object sizes and different noise levels were conducted to evaluate the above theoretic analysis. Results: For the computer simulation, the calculated OT was exactly the real OT. In the experiments on phantoms, the calculated OT led to visually satisfactory delineation results. Conclusions: The new theoretic analysis showed that the OT can be calculated without any a priori information about scanners, imaging protocols and tumor size. The computer simulation and phantom experiments validated the theoretic analysis.
SU‐E‐J‐112: A Splatting Method to Generate DRRs for Deformed CT Volume
Authors: Zhong Z , Cai Y , Guo X , Solberg T , Mao W
Published: June 2012

Purpose: To develop an efficient algorithm for generating high‐quality digitally reconstructed radiographs (DRRs) for regularly and irregularly sampled volumes based on a splatting method with dynamic elliptical Gaussian kernels, and to evaluate this method using ray tracing. Methods: The traditional ray tracing method, which takes every intersected voxel into account, produces high quality DRRs but is very time consuming. Additionally, it is not suitable for handling irregularly‐sampled volumes since it always requires image re‐sampling, which leads to inaccuracy. We present a splatting approach to compute the ‘footprint function’, facilitating efficient perspective projection of elliptical Gaussian kernels at very low cost. This reported framework allows dealing with both regularly and irregularly sampled volumes effectively and efficiently. An XCAT digital phantom was used to generate 3D chest volumes at different respiratory phases, and CT projections are generated using ray tracing and the splatting method, respectively. Normalized cross correlation (NCC) is applied to evaluate the DRR similarity of two methods. Results: Respiratory Phases one and four are used as volume datasets from the 4D XCAT digital phantom as they represent the lung at end inhale and end exhale, respectively. The dataset of Phase one is the basic regularly‐sampled volume while the dataset of Phase four is deformed from that of Phase one, and resampled to a regularly sampled dataset for the ray tracing method. NCC between splatting and ray tracing DRRs are 0.9980 and 0.9977 for Phase one and Phase four, respectively. The calculation speed of the splatting method is 3 times faster than that of ray tracing. Conclusions: Our splatting approach can generate high‐quality DRRs efficiently, and is a good alternative for current DRR generation techniques for deformed volume datasets. This research is supported by CPRIT Individual Investigator Award RP110329.
SU‐E‐J‐113: A New Quantification Measure of the Difference Between Two Organ Contours
Authors: Kim H , Park SB , Lo S , Monroe JI , Sohn JW
Published: June 2012

Purpose: To develop a measuring method between two contours, which can be used for validating a PTV during IGRT, organ motion and/or deformation studies. Methods: Quantifying the geometric difference between two organ/target surfaces is essential for Radiation therapy planning and delivery. Point‐to‐surface distance measures have been utilized to evaluate and visualize the local surface differences. However, previously well‐known distance measures have critical shortfalls. Normal distance (ND) measure suffers when the reference surface is strongly curved. Minimum distance (MD) measure (a.k.a. Hausdorff distance) suffers when the test surface is strongly curved. Our new distance measure named Error‐Proof Distance (EPD) can deal with both difficult cases.EPD measure calculates the maximum value between the Forward Minimum Distance (FMinD) and the Backward Maximum Distance (BMaxD) at each point. The FMinD denotes the minimum distance to the test surface from a point on the reference surface. The BMaxD means the maximum value among the minimum distances from all points of the test surface to the point on the reference surface. We tested EPD using three 2‐D contour examples including a 20mm shifted contour, and two 3‐D clinical cases. Results: In case of 2‐D contour examples, ND and MD measure failed in strongly curved areas, but EPD measure outperformed the others. The maximum distance measured between a reference and a 20mm shifted test contour should be equal to 20mm, but ND erroneously measured 24mm. Furthermore, ND reported erroneous distances where the reference surface is strongly curved in 3‐D clinical cases. Conclusions: We succeeded to prove that a new EPD is arobust and accurate distance measure to compare two 2D or 3D surfaces. EPD measure can be used to evaluate and visualize the surface difference of organ contours. It is also helpful for proving PTV margin during IGRT, and organ motion and/or deformation studies. This project is partially supported by the Agency for Healthcare Research and Quality (AHRQ) grant 1R18HS017424‐01A2.
SU‐E‐J‐115: Graticule for Verification of Treatment Position in Neutron Therapy
Authors: Halford R , Snyder M
Published: June 2012

Purpose: Until recently the treatment verification for patients undergoing fast neutron therapy at our facility was accomplished through a combination of neutron beam portal films aligned with a graticule mounted on an orthronormal x‐ray tube. To eliminate uncertainty with respect to the relative positions of the x‐ray graticule and the therapy beam, we have developed a graticule which is placed in the neutron beam itself. Methods: For a graticule to be visible on the portal film, the attenuation of the neutron beam by the graticule landmarks must be significantly greater than that of the material in which the landmarks are mounted. Various materials, thicknesses, and mounting points were tried to gain the largest contrast between the graticule landmarks and the mounting material. Results: The final design involved 2 inch steel pins of 0.125 inch diameter captured between two parallel plates of 0.25 inch thick clear acrylic plastic. The distance between the two acrylic plates was 1.625 inches, held together at the perimeter with acrylic sidewall spacers. This allowed the majority of length of the steel pins to be surrounded by air. The pins were set 1 cm apart and mounted at angles parallel to the divergence of the beam dependent on their position within the array. The entire steel pin and acrylic plate assembly was mounted on an acrylic accessory tray to allow for graticule alignment. Conclusion: Despite the inherent difficulties in attenuating fast neutrons, our simple graticule design produces the required difference of attenuation between the arrays of landmarks and the mounting material. The graticule successfully provides an in‐beam frame of reference for patient portal verification.
SU‐E‐J‐116: Investigation of Accuracy and Precision of ITV Delineation by PET SUV Threshold
Authors: Hartl B , Serratore D , Chan P , Neicu T , Li S
Published: June 2012

Purpose: To determine PET SUV values in Pinnacle TPS and to assess the accuracy and precision of the ITV according to SUV thresholds. The goal is to minimize errors in target definition by using SUV showing enhanced metabolic activity of the tumor and fast CT imaging with less motion artifacts. Methods: Mean PET values in individual patients were obtained from statistics of body contours for whole body PET‐CT scans. The SUVs were calculated by normalizing the PET values in any voxel by the mean body PET value. These clinically acquired SUVs were plotted against values reported at the time of the scan for verification. GTVs were contoured on petCT scans and the ITV's were contoured using three published methods of SUV thresholds at 2.5, at ratio of 40 percent of the maximum, and 30 percent of the maximum added to 60 percent of the mean body. GTV volumes were plotted against the 3 sets of ITVs to investigate their relationships. Results: Examination of 11 patients showed a strong linear relationship between clinically determined SUVs and those reported; indicating the validity of our SUV definition. Plots of GTV volumes versus ITV volumes for each of the 3 thresholds revealed a less clear relationship. The effectiveness of each method to generate a reasonable ITV was highly patient dependent; in general the 2.5 threshold gave the best results, while the 40 percent maximum produced the worst. Conclusions: While more data is required to make a definitive statement about the value of PET SUV threshold defined ITVs, the findings do seem to reveal a pattern between GTV and ITV size. If an appropriate SUV threshold is chosen, the GTV‐ITV volume relationship is nearly linear, which suggests extending the GTV in volume rather than the margin distance as is common in ITV delineation.
SU‐E‐J‐121: A New 4D Radiotherapy Planning Strategy Using Synthesized Tumor‐Motion‐Compensated Computed Tomography
Authors: Cohen P , Li D , Xie H , Low D , Rimner A , Li G
Published: June 2012

Purpose: To validate a four‐dimensional radiotherapy (4DRT) planning based on a synthesized CT image compensating for tumor motion, accounting for tumor rotation, deformation and distortion due to motion artifacts, and producing realistic normal tissue density in motion‐tracking beam eye view. Methods: 4D computed tomography (4DCT) images of six patients with peripheral lung lesions in mid or lower lobs (motion range: 0.5–3.5cm and size: 1.5±2.0 cm3) were used. A customized program was used for simulating the patient anatomy with a motion‐compensated tumor using 4DCT by aligning the tumor and averaging the 4DCT into a static 3.5DCT image. The gross tumor volume (GTV) was delineated semiautomatically using a threshold algorithm. Variation of GTV in each phase CT was assessed across all phases. 3DRT plans were generated using 3.5DCT and 4DCT and compared for validation. An integrated dose volume histogram (iDVH) from all phase plans and dose warping using deformable image registration (DIR) were used for evaluating 4D plans and comparing with 3.5D plans. Results: The range of tumor volume variation over the mean within a breathing cycle was 87%±46%. The 3.5DCT produced an ‘averaged’ GTV, more reliable than that from any phase CT. The results show that the 3.5D plan is equivalent to the 4D plan, except for low dose area, using the iDVH evaluation. On average, the percentage difference for the areas under the DVH and iDVH is 4.3%±2.7%, while 2/3 of the difference results from low dose region blow D20%. Using DIR‐based dose warping, PTV coverage varies due to DIR uncertainty for the small lesions. Conclusions: The 3.5D plan is equivalent to the 4D plan for peripheral lung lesions, yet requires much less clinical workload. The 3.5D plan accounts for tumor motion and tumor variation for a more reliable delineation, and for realistic normal tissue representation for motion tracking.
SU‐E‐J‐181: Explore the Value of 18F‐FDG, PET and CT Scan for the Clinical Stage of Esophageal Carcinoma
Authors: Li J , Zhu S , Liu Z
Published: June 2012

Purpose: To evaluate the sensitivity, specificity and accuracy of 18F‐FDG, PET and CT were used for T and N stage for esophageal carcinoma, and explore the SUV value evaluating prognosis of esophageal carcinoma. Methods: PET and CT scan as well as T and N stage were carried out for 29 cases of preoperative esophageal carcinoma patients, those results were analyzed and compared with postoperative pathological TNM stage. Results: CT scan diagnosis the T stage was obviously higher than PET in term of determining T staging, while PET scan diagnosis the N stage was higher than CT, but there was no significantly difference. It was clear that only the Kappa value of preoperative PET scan in T stage was poor (Kappa value, 0.4), the Kappa values for PET scan in N stage, as well as preoperative CT scan in T and N stage were good (Kappa value.0.4). The SUV values were increasing with the lesion volume and length of PETsuv2.5 becoming bigger. The survival was better when patients with SUV value, 9.0 than those with SUV value=9.0. Conclusions: CT was better than PET in term of determining clinical T stage of esophageal carcinoma, while PET diagnosis the N stage was good the same as CT. SUV 9.0 may be regarded as a practical indexes to determine prognosis in esophageal carcinoma.
SU‐E‐J‐182: Patient Specific Assessment of External Radiation Exposure to Bystanders Interacting with Patients Following 131I Therapy
Authors: Han E , Lee C , McGuire E , Brown T , Penagaricano J , Corry P , Bolch W
Published: June 2012

Purpose: Conventional calculation methods of patient release criteria for compliance with NRC regulations are based on the assumption that both patient and bystander are each a single point in space. This study was intended to assess the patient‐specific external radiation exposure to a bystander interacting with the patient following radionuclide therapy with 131I. Methods: 131I‐sodium iodide treatment for hyperthyroidism and thyroid cancer and 131I‐tositumomab treatment of non‐Hodgkin's lymphoma were considered. 131I distribution provided by the patient SPECT image was rendered on the SPECT‐fused CT images. The CT images were then imported to a Monte Carlo based simulation code, MCNPX 2.7, as a source phantom. For a target phantom, we employed the adult male hybrid phantom developed at the University of Florida and National Cancer Institute. A single orientation ‐ patient and a bystander facing one another at 1.0 m ‐ was considered. S factors (dose per unit cumulative activity (A)) for each organ in a bystander was obtained from the MC calculations and effective dose (EDE) per A was calculated based on tissue‐weighted individual organ doses. The results were compared with the calculations using UF/NCI adult hybrid source/target phantoms and the revised adult ORNL stylized source/target phantoms. Results: EDE per A of the stylized phantom was 1.5% higher than that of the hybrid phantom for uniform source localization in the thyroid. However, EDE per A of the hybrid phantom was 20% less than that of stylized phantoms for a torso source. The difference is attributed to the realistic shape of the frontal body comparing to the simple ellipsoidal trunk of the stylized phantom. Conclusions: Based on the realistic hybrid phantoms and accurate MC radiation transport calculation tools, patient specific dosimetry for a bystander is feasible. S factors will be calculated using the patient CT image with 131I bio‐distributions and hybrid phantoms.
SU‐E‐J‐183: Dose Distribution Generated from 131I Radionuclide Using SPECT‐CT
Authors: Itoh S , Haga A , Fukuda Y , Sasano N , Onoe T , Shibata S , Nakagawa K
Published: June 2012

Purpose: 131I radionuclide therapy is widely performed in a thyroid cancer treatment, but there has been almost no evaluation of the dose distribution. The aim of this work is to develop the calculation system using the data of SPECT‐CT and to examine the effects of their image resolutions on the dose distribution. Methods: We designed and constructed an acrylic phantom for measurement. A radioactive iodine capsule and glass dosimeters can be set in the layer structure of the phantom. We put iodine capsules (37MBq, 111MBq, 185MBq) in the middle of the phantom and acquired SPECT‐CT (Infinia Hawkeye4 (GE)) images. Both the CT value data (image resolution: 1.1 mm) and the intensity map data of SPECT (image resolution: 4.4 mm) were independently used for the estimation of the cumulative dose distribution generated from the radioactive iodine in the phantom. We adopted Monte Carlo program PHITS2.0 as the simulation of the dose calculation. The absolute dose was measured by glass dosimeters. Results: The measurement result by glass dosimeters was very similar to the Monte Carlo simulation result, in which the difference was about 0.3 %. We obtained the dose distributions reconstructed by the radioactive iodine distribution using CT value data and SPECT data, respectively. The iodine distribution from CT could be finer than that of SPECT data because of its higher image resolution. As a result, the difference was found to be factor two in the middle of the iodine distribution. On the other hand, both of the dose distribution was almost same above 2.2 cm distance from the center. Conclusions: We can reconstruct the 131I dose distribution using SPECT‐CT data. For more accurate calculation of the dose distribution, it would be crucial to increase the resolution of SPECT data.
SU‐E‐J‐184: Liver Dose Calculation for 90Y Radioembolization with PET/CT
Authors: Yu N , Xia P
Published: June 2012

Purpose: 90Y radioembolization is a promising treatment for unresectable hepatocellular carcinoma. A dose of 120 Gy is typically prescribed to a segment or lobe of the liver, but dose of 90Y in the liver tumor is highly heterogeneous. Recently developed 90Y PET/CT enables the visualization of the injected 90Y distribution with high resolution. The purpose of this study is to develop an integrated system to quantify 3D dose distribution using PET/CT images. Methods: According to our institution's protocol, 90Y PET/CT scan is acquired for each patient two hours after radioembolization. Based on the 90Y activity distribution from PET scans, we used a convolution method to calculate radiation dose with a published dose kernel for 90Y. The gross tumor volume (GTV) was retrospectively contoured by a clinician using contrast‐enhanced diagnostic CTs. Because of breathing motion and different patient positions between the diagnostic CT and PET/CT, we manually registered these two CT scans by aligning 200 Gy dose line with the center of the intended treatment GTV. Results: The system was integrated with a commercial RT PACS/Review station (MIMVista) through DICOM. The dose obtained was of the resolution of PET scans (2–4 mm). This distribution enables physicians to quantify treatment volume and dose relationships either through 3D dose distributions or dose volume histograms. For the five patients tested for our program, liver mean doses agree with clinical results within 5%. We were able to quantify radiation doses to 95% of GTV, and radiation dose received by the CTVs extended 5 mm from the GTV. Conclusions: The system is easy to use for radiation oncologists. With this system, clinicians can quantitatively evaluate delivered dose of 90Y treatment in high resolution. No conflict of interest for this work
SU‐E‐J‐186: CT Textures Can be Predictive for Tumor Shrinkage
Authors: Hunter L , Chen Y , Zhang L , Liao Z , Dong L , Court L
Published: June 2012

Purpose: To determine if NSCLC GTV CT textures can be predictive for tumor shrinkage after proton therapy with concurrent chemotherapy. Methods: Simulation and weekly 4DCTs were obtained from 25 patients with locally advanced NSCLC treated by proton therapy at a 74 Gy dose level with concurrent chemotherapy. In‐house deformable image registration software based on the demons algorithm propagated the physician‐generated planning GTV onto each weekly image set. GTV soft tissue volumes were then extracted from CT images by in‐house software which pruned out voxels below a cutoff HU threshold. Patients whose normalized end‐treatment pruned GTV was less than the group median were classified as ‘strong responders.’The remaining patients were classified as ‘weak responders.’ For each pruned GTV, the MaZda texture analysis software generated quantitative 3D texture features based on the image histogram, absolute gradient, co‐occurrence matrix, and run‐length matrix. Using the Fisher coefficient, a subset of these texture features that were relevant to distinguishing the two response groups was obtained. In‐house software and MaZda were then used to perform neural network data classification with leave‐one‐out cross‐validation based on this texture feature subset. The true positive rate (TPR), false positive rate (FPR), classification accuracy, and one‐sided p‐values were recorded. Results: Six classification models were tested. Each model had a unique combination of feature extraction and analysis parameters. Taking ‘strong responders' to be the ‘positive’ class, the average TPR was 0.66 (0.60 – 0.75), the average FPR was 0.36 (0.31 – 0.40), the average classification accuracy was 64.8% (60.0% – 68.0%), and the average one‐sided p‐value was 0.118 (0.055 – 0.215). Conclusions: There is some evidence showing that NSCLC GTV CT textures can be predictive for tumor shrinkage after proton therapy. Further work should be done to include more patients, texture metrics, and data analysis techniques. The presenting author receives funding support from a Hertz Foundation Applied Science Fellowship.
SU‐E‐J‐188: Lung CT Density Changes during the Conformal Radiotherapy: A Prediction for Lung Radiation Pneumonitis
Authors: Ma C , Yin Y
Published: June 2012

Purpose: Radiologic lung density changes are observed durng radiotherapy for lung cancer. We studied the relationship between dose and computed tomography (CT) density changes during the treatment, a prediction for lung radiation pneumonitis. Methods: 30 Stage â…¢ lung cancer patients with CRT were thirty fractions of 2.0 Gy, prescribed at the 90% isodose. Follow‐up CT scans performed at less than 3 weeks (n=15) and between 3 and 6 weeks (n=15) after CRT radiotherapy were reviewed. New CT scans were coregistered with baseline scans using CT‐CT normalized mutual information registration algorithm. Voxel‐Hounsfield unit histograms were created for doses between 0.5 and 50 Gy. Linear mixed effects models were used to assess the effects of CRT dose on CT density, and the influence of possible confounders was tested. Results: Increased mean CT density was associated with higher dose, increasing planning target volume size, and increasing time after CRT (all p <0.05). Density increases were apparent in areas receiving 20 Gy, but seemed to plateau above 40 Gy. In regions receiving >30 Gy, the reduction in air‐filled fraction of lung during treatment was up to 20%. No increase in CT density was observed in the contralateral lung receiving less than 5 Gy. Conclusions: A dose‐response relationship seems to exist for quantitative CT density changes during CRT radiotherapy. The density between old and new CT during the treatment could be a prediction for lung injury.
SU‐E‐J‐190: A Possible Non‐Typical Measurement in Dynamic Contrast MRI for Brain Tumor Therapy Response Evaluation
Authors: Yee S , Hahn S
Published: June 2012

Purpose: Dynamic susceptibility contrast (DSC) MRI technique can be used for brain tumor therapy response evaluation. However, the typical modeling (Eqs.1) is sometimes problematic in the vicinity of tumor due possibly to the disrupted microvascular environment associated with the disease. The example is well illustrated in Fig.1B, where some MRI time series curves (TSC) keep increasing beyond the baseline value after some time points, which is theoretically prohibited in the typical modeling. This behavior is not related to other systemic error such as motion or system instability, which can be verified by the steady TSC from a CSF region (Fig.1C).To improve the utility of DSC‐MRI even in the problematic, but highly interested, regions of the brain, the non‐complying TSC was assumed legitimate and its increase rate, linked to the degree of contrast's leakage, was analyzed. Methods: DSC‐MRI (with manually injected multihance, 0.2 ml/kg) was applied for high grade brain tumor before and after radiation therapy. The scan technique was based on T2*‐weighted PRESTO implemented on a Philips 1.5T scanner. The analysis of TSC was performed using an in‐house software developed in MATLAB. Results: The TSCs from several regions of interest (ROI) were obtained (Fig.1). Before radiation therapy, those from ROIs A and C increase beyond the baseline after about 60 frame that coincides with the passage of contrast in other complying TSC. The slope of increasing TSC was determined as shown in Figs. 1B and 1D. After therapy (Fig 1D), the compliance of TSC in ROI C to the typical modeling was quite improved and the degree of leakage shown as slope, as well as the area of leakage (Fig 2), decreased after the therapy, suggesting the therapy possibly repaired the leakage. Conclusion: A non‐typical measurement was introduced to improve DSC‐MRI's utility in the brain tumor therapy monitoring. American Cancer Society Institutional Research Grant/Upstate Cancer Research Institute
SU‐E‐J‐192: Static Breath‐Hold MRI Based Measurement of Change in Pulmonary Function Following a Course of Radiation Therapy
Authors: Ding K , Cao K , Du K , Chen Q , Ennis D , Christensen G , Reinhardt J , Libby B , Benedict S , Sheng K
Published: June 2012

Purpose: Radiation Therapy (RT) induced pulmonary function change may depend on the location, underlying function of that lung prior to radiations, radiation dose/fractionation and other factors. We propose to evaluate the radiation induced pulmonary function change using static breath‐hold MRI scans with vascular information and 3D deformable image registration which can provide pulmonary function relative to RT dose on a regional basis. Methods: A MRI scan pair near the end of inhale and near the end of exhale with breath hold were acquired for one lung cancer patient before RT and 6 months after RT. The patient was treated with SBRT with 55 Gy to PTVs in the right and the left lung respectively. B‐spline based vesselness preserving image registration algorithm was applied to register the MRI pair for the calculation of local lung expansion as a measurement of regional pulmonary function (PF). The PF maps before RT and after RT were then mapped to the planning CT using the same algorithm tuned for MRI‐CT registration. The pulmonary function change was calculated via the PF ratio between two MRI pairs. Results: Strong spatial correlation was found between the irradiated lung region and the region with greatly decreased PF. Based on dose and PFC distribution, no strong determinant factor was found for PF lost in the left lung while the right lung shows that all the lung tissue receiving dose larger than 28 Gy will have a decreased PF. Conclusions: We demonstrated a method that uses static breath‐hold MRI based lung imaging to evaluate radiation induced pulmonary function change which can be applied to study the dose and the pulmonary function change in a regional basis. This work is supported by NIH grant support 1R21CA144063
SU‐E‐J‐198: Bioluminescence Monitoring of Metastatic Breast Cancer: Quantitative Assessment of Radiation Treatment Effects and Tracking of Tumor Cells
Authors: Park J , Schmidt T , Graves E , Bazalova M , Lee J , Contag C , Suh T
Published: June 2012

Purpose: To evaluate radiation treatment effects on mammary carcinoma cells, quantitative photon radiance were monitored to track light‐emitting cancer cells and metastasis using in vivo bioluminescence imaging. Methods: Eight female BALB/c mice aged 8 weeks were orthotopically injected with 5×104/cc 4T1 tumor cells into the abdominal mammary gland. The firefly luciferase‐based bioluminescence images were acquired every 2–3 days for 1 month. Bioluminescent intensity was analyzed in average surface radiance (photons/sec/cm2/sr) taken in 3‐dimensional bioluminescence tomography (BLT). After 1 week, single‐radiation dose of 20 Gy was delivered by orthovoltage X‐rays. Variation of detected bioluminescence signals emitted from molecular cancer cells was depicted on BLT images. To delineate tumor volumes according to bioluminescence intensity on anatomical images for radiation therapy, BLT images were registered with the micro computed tomography (CT) images using surface‐constrained warping. Results: Multispectral BLT images elaborated on early detection of cancer cells, characteristics of tumor growth, and metastasis for more accurate determination of internal bioluminescent sources. The radiation‐treated mice having only primary tumor volumes showed 67% decrease in bioluminescent signals, while the mice with metastatic cancer cells suggested 88% reduction, as compared to the control group. Registration of BLT with CT images guided molecular cancer cells on anatomical coordinates. Conclusions: The BLT imaging was a useful tool to localize cancer cells and to quantify radiation response. Application of BLT led to more accurate definition of tumor volumes including molecular probe‐based microscopic cancer cells. Monitoring of bioluminescence signals enables to diagnose real‐time metastatic behavior of cancer cells and determine optimal radiation treatment strategies adapted to tumor characteristics.
SU‐E‐J‐199: A Novel Method to Evaluate Local Control and Recurrence Using 18F‐FDG PET After Lung SBRT
Authors: Shang C , Kathriarachchi V , Williams T , Cole J , Kasper M , Shope J , Benda R
Published: June 2012

Purpose: The goal of this study is to evaluate the predictability of a novel method using a self background‐corrected maximum Standard Uptake Value (cSUVmax) from 18F‐FDG Positron Emission Tomography (PET) of the patients following lung cancer stereotactic body radiosurgery (SBRT). Methods: 20 qualified patients treated out of 38 patients treated with SBRT for a single lung malignant lesion between May 2009 and December 2009 were enrolled in the cohort study. All had pre‐ and at least one post‐treatment PET images available at the time of study. The mean normal tissue SUV from the descending aorta was sampled as baseline to divide SUVmax of tumor site. The resultant cSUVmax was used for assess the local control or possible recurrence. The Result was then compared with that using SUVmax alone method. Results: The average follow‐up length was 48.9 weeks ranging from 18.6 to 115.0 weeks. The mean SUV of aorta was measured as 1.821±0.364, ranging from 1.173 to 2.576. From the pre‐treatment PET, 70% and 65% was indicated positive correspondingly when using SUVmax with 2.50 and cSUVmax with 1.52 thresholds. When PET was taken < 29 weeks post‐SBRT, 75% and 67% respectively showed higher values in the locally controlled group. For PET = 29 weeks after SBRT, with cSUVmax both locally controlled and recurrent groups are accurately identified, while SUVmax shows 5% false positive and one possible false negative. Conclusions: The SUVmax in lung tumor site corrected by the mean SUV of descending aorta or cSUVmax provided a more reliable parameter than using SUVmax alone in predicting the local control and recurrence for follow‐up PET of patients after lung SBRT. The method used in this study objectively displayed a strong correlation between low cSUVmax and local control following lung SBRT in this investigation, otherwise a local recurrence is suggested.
SU‐E‐J‐204: The Dosimetric Impact of Daily Setup Error and Inter‐Fraction Prostate Motion on the Calculated Treatment Plan for Patients Receiving IMRT Based Radiation Therapy
Authors: Algan O , Jamgade A , Ali I , Christie A , Thompson J , Thompson D , Ahmad S , Herman T
Published: June 2012

Purpose: Evaluate the dosimetric impact of daily setup error and inter‐fraction organ motion on the radiation treatment plan. Methods: Twelve patients undergoing definitive IMRT treatments for prostate cancer were evaluated. All patients underwent fiducial marker placement prior to treatment planning CT scan, and were treated to a dose of 8100cGy given in 45 fractions. We retrospectively created a plan for each treatment day that had a shift available. To calculate the dose the patient would have received with no setup correction, we mathematically ‘negated’ the shift by moving the isocenter in the opposite direction of the shift. The individualized daily plans were combined to generate an overall plan sum. The dose distribution from these uncorrected plans was compared to the actual treatment plans. Results: A total of 390 shifts were negated and their corresponding plans evaluated. The mean isocenter shift based on the location of the fiducial markers was 3.3±6.5mm to the right, 1.6±5.1mm posteriorly, and 1.0±5.0mm along the caudal direction. The mean D95 dose when setup error was corrected and uncorrected for the PTV8100 volume was 8089cGy and 7303cGy (p < 0.001) and for prostate volume was 8228cGy and 7844cGy (p < 0.002). The mean V95 values when setup error was corrected and uncorrected for the PTV8100 volume was 99.9% vs. 87.3% (p < 0.0001). At an individual level, the difference in D95 value approached 1200cGy for the prostate volume and 2000cGy for the PTV8100 volume. There was no statistically significant difference in the D35 parameter for the surrounding normal tissue except for the dose received by the right hip and the penile bulb. Conclusion: This work indicates that significant underdosing, approaching 2000cGy for the PTV8100 volume, can occur as a Result of inaccurate patient setup, and emphasizes the importance of accurate patient setup and target localization.
SU‐E‐J‐206: Are We Treating the Entire Lung Tumor by Contouring ITV on 4DCT MIP?
Authors: Tang X , Morris D , Rosenberg L , Wang A
Published: June 2012

Purpose: To describe a case report of lung radiosurgery in which defining a target using a maximal intensity projection CT omitted a portion of the target due to an overlapping projection of the diaphragm. Methods: A Lung 4DCT was obtained, and all ten breathing phases from 0% to 90% were reconstructed. The gating window was set to treat phases 30% to 70%, and the corresponding Maximum Intensity Projection (MIP) was generated. An ITV was contoured on the MIP. Tumor GTVs were contoured on all phases. Another ITV was generated by using the overlapped projection of all GTVs in the gating window when suspicion arose that part of the target was omitted. The volumes of the ITVs and GTVs were calculated. DVHs were analyzed on the plan generated to cover the MIP ITV. Results: The initial ITV based on the MIP omitted 15% of the target Since the ITVs of the diaphragm and the tumor overlap in the MIP, and the imaging technique cannot differentiate them, contouring tumor only on MIP has risk of missing part of the tumor. We have also found that the volumes of all 10 phases fluctuate with a standard deviation of 5.6%. This indicates the imperfection of 4DCT generation. If the treatment plan was generated based on MIP ITV, DVH shows the lack of coverage for the true ITV. Conclusions: We propose two methods to accurately delineate tumor ITV from 4DCT for cases in which the target is located adjacent to diaphragm. If it is desired to start with contouring ITV on MIP, thorough QA needs to be done by going through all gating phases to adjust the ITV accordingly. A more straightforward method is to contour the GTV on all gating phases and use the overlapped projections as the ITV.
SU‐E‐J‐208: Dosimetric Assessment of Treatment Using CBCT Images
Authors: Dyess A , Poon E , Seuntjens J , Parker W
Published: June 2012

Purpose: To evaluate target coverage for five breast patients receiving boost treatment to the tumor bed by calculating the daily dose on cone beam computed tomography (CBCT) images and utilizing deformable image registration techniques. Methods: The daily dose is calculated on pretreatment CBCT images using the same beam configuration as the original volumetric modulated arc therapy (VMAT) plan. Calculations were done with two different isocenter positions according to: (1) the initial patient setup and (2) the shifts applied for treatment based on CBCT verification. The daily doses are deformed and accumulated onto the planning CT using commercially available deformable image registration software. The dose distribution is compared to the original distribution and tumor and PTV coverage is evaluated for both situations (initial and shifted positions). The deformation accuracy is evaluated by calculating the change in centroid location and the Dice coefficient of deformed contours. Results: The tumor bed is adequately covered regardless of the treatment position. The average dose received by 98% (D98) of the tumor bed volume differs from the original plan by +1.6% and −0.2% for the shifted and initial positions respectively. However, when dose is accumulated in the initial setup position PTV coverage is lost; the average D98 for the PTV changes by −15.8% and −26.9% for the shifted and initial positions respectively. The average change in centroid location is 0.43 mm and 1.53 mm for the left and right lung contour respectively. The Dice coefficient for the left and right lung is 0.94 and 0.95 respectively. Conclusions: The margins used to define the PTV are sufficient to ensure tumor bed coverage for the given positioning variability. We are also confident in the deformation used to deform and accumulate dose based on deformed contour comparison. Hardware provided by MIM Software Inc.
SU‐E‐J‐211: Development of Respiratory Training System Using Individual Characteristic Guiding Waveform
Authors: Kang S , Yoon J , Kim T , Suh T
Published: June 2012

Purpose: The purpose of this study is to develop the respiratory training system using individual characteristic guiding waveform to reduce the impact of respiratory motion that causes artifact in radiation therapy. Methods: Respiratory training system was developed by LabView (National Instruments, version 8.6). The real‐time respiratory signals were acquired using in‐house developed belt type sensor and more user‐comfortable HMD was used for visual guiding (Vuzix, Wrap 920). The respiratory training program consists of three main components. It is (1) respiratory signal reading and peak detection program (2) individual characteristic guiding waveform generation program (3) respiratory signals acquisition and visual guiding program. In order to evaluate the feasibility of in‐house developed respiratory training system, 5 volunteers were included and their respiratory signals were acquired using the in‐house developed belt‐type sensor. Respiratory training system needs 10 free breathing cycles of each volunteer to make individual characteristic guiding waveform based on Fourier series and it guides patient's next breathing. For each volunteer, free breathing and guided breathing which uses individual characteristic guiding waveform were performed to acquire the respiratory cycles for 3 min. The root mean square error (RMSE) was computed to analyze improvement of respiratory regularity in period and displacement. Results: It was found that respiratory regularity was improved by using respiratory training system. RMSE of guided breathing decreased up to 40% in displacement and 76% in period compared with free breathing. The average of RMSE was decreases from 0.012V to 0.008V in displacement and from 0.432 sec to 0.192 sec in period. Conclusions: In conclusion, since the guiding waveform was easy to follow for the volunteers, The respiratory regularity was significantly improved by using in‐house developed respiratory training system. So it would be helpful to improve accuracy and efficiency during 4D‐RT, 4D‐CT.
SU‐E‐J‐212: Real‐Time Treatment Failure Detection for Moving Tumor with Image Guidance
Authors: Wu H , Zhao Q , Das I
Published: June 2012

Purpose: Respiratory motion can cause misalignment of the radiation beam and will greatly degrade the effectiveness of cancer radiation treatment. Failure of radiation treatment can be detected by real‐time image. The efficiency of both internal tracking and external tracking has been assessed in this. Methods: Both 3D internal fiducial marker motion and 1D external abdominal surface motion data of 11 patients were collected simultaneously. Linear and quadratic functions have been applied to correlate the internal and external motion signal. To detect tracking failure of external imaging, three approaches have been proposed: (i) correlation only: which will compare directly between the tracked and the based curve. (2) fixed baseline shift: which interferes the treatment by adjusting the baseline of tracked signal to compensate target motion for each treatment fraction. (3) dynamic baseline shift: dynamically interfere with treatment for each breathing cycle. (4) dynamic baseline with base curve update, is the third approach with base curve for each fraction. Results: Generally, the quadratic function produced better correlation results than the linear correlation. With more historical data available, the quadratic prediction gives better correlation outcomes. The tracking frequency of 30, 10, 5, 2, 1, and 0.2Hz has been evaluated. Although high imaging frequency usually results in better correlation, imaging rate of 1 Hz will results acceptable results. For the failure detection, the average Euclidean misalignment is 3.46, 2.42, 2.21, 1.80 for the above four approach. With treatment interference, the beam‐on‐time percentages are 54.03, 58.26, 63.94, and 60.48 respectively. Conclusion: High imaging frequency is not required. approaches with baseline shift and base curve update will improve the treatment efficiency and treatment accuracy. Guidance using hybrid imaging with high‐frequency of external imaging and low internal imaging is needed to compensate tumor respiratory motion.