bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐176: A Simple 4D MRI Technique for Radiation Therapy Planning
Authors: Yee S , Ionascu D , Yan D
Published: July 2013

Purpose: 4D CT is routinely performed for radiation therapy planning when patient motion is a significant factor. As more benefits of using MRI become clearer in treatment planning, the feasibility of 4D MRI, however, has been questionable. The challenge of 4D MRI is the balance between satisfactory image quality and the speed to capture patient breathing cycle. So far, neither the image quality nor the speed has been satisfactory. Here, a simple MRI technique to do 4D MRI is introduced. Methods: 3T MRI (Philips Ingenia) was used for scanning. For monitoring breathing cycles, a regular respiratory belt was used. The breathing cycle was then divided into multiple phases. For each breathing phase, a predefined respiratory trigger time was set and the multi‐slice (30 slices) MRI scan was performed for each breathing phase. A single‐shot fast spin echo with partial k‐space filling was used with varied trigger time from 0 up to the length of breathing cycle. Results: As seen in Fig 1 (1 of 30 slices), the image quality for each breathing phase is quite good. The line profiles from the line ROI in Fig 1, which was placed over the liver dome, are shown in Fig 2 for a few different breathing phases. The position of the line was fixed across the breathing phases to see the displacement caused by breathing. This demonstrates the liver dome displacement (along the x axis) during breathing cycles. Conclusion: The 4D MRI for treatment planning can be done with satisfactory imaging quality and speed with a regular respiratory triggering technique, which can be directly implemented on most MRI devices without the need of extra device. However, since only a brief time‐window during each breathing cycle is used for acquisition, total acquisition time may be longer (∼30 min), and the improvement is undergoing.
SU‐E‐J‐178: Patient‐Specific Bulk Electron Density Overridden Planning for Prostate and Brain Cancer
Authors: Du D , Wooten H , Feng Y , Olsen J , Mutic S , Hu Y
Published: July 2013

Purpose: To evaluate the use of patient‐specific bulk electron density overridden plans for prostate and brain IMRT dose calculations. Methods: CT images and IMRT treatment plans for 10 prostate cancer patients and 10 brain cancer patients were retrospectively reviewed and analyzed. For each patient, a region of interest(ROI) was selected to cover all radiation beam paths plus 1cm margins in both superior and inferior directions. Patient anatomy within the ROI was segmented into fat, non‐fat soft tissue, bone and air based on CT number ranges of [−,‐50], [−,49,200], [201,4000], [− 1200,−800], respectively. Average electron densities for each tissue type were obtained from CT values for each patient, and all plans were recalculated on patient‐specific density overridden datasets to generate patient‐specific bulk density overridden plans. These overridden plans were compared to the clinically approved CT‐based plan using dose‐volume metrics for PTV coverage and OAR sparing. Results: Dose deviations between the patient‐specific density overridden plan and the clinically approved plan were minimal. Most parameters used to describe planning target volume coverage and OAR sparing were within 0.3% of approved CT based plans and all parameters were within 1% of the approved CT based plans. Conclusion: This study demonstrates that by assigning patient‐specific bulk electron densities to different tissue types, the dosimetric error of the patient‐specific bulk density overridden plans is within 1% of the approved CT based plan. This, patient‐specific bulk density override technique may potentially be used to map the CT values from CT images to MR images.
SU‐E‐J‐181: Imaging and Radiation Therapy: GATE Monte Carlo Simulation of a MV‐CBCT Flat Panel with Specific Application in Head and Neck Cancer
Authors: Benhalouche S , Bert J , Visvikis D , Pradier O , Nicolas B
Published: July 2013

Purpose: In radiotherapy treatment, it is necessary to position the patient before each session to ensure that the beams target the tumor while sparing the surrounding healthy tissue. This step is made possible via different imaging techniques like the “electronic portal imaging device” (EPID) which is the most common approach. In this configuration, the imaging source is the same as the treatment source, while a flat panel detector is automatically placed under the patient via an articulated arm. In this context, our study was focused on the Monte Carlo modeling of such an EPID in order to simulate both planar and 3D CBCT images. Methods: The work presented here follows a preliminary study in which the treatment part of a clinical linear accelerator LINAC (Siemens Oncor Impression) was modeled in GATE v6.2. The objective was to calculate the dose delivered by complex treatments like IMRT . The objective of the present study is to extend this model by incorporating the imaging part of the LINAC, which consists of a flat panel detector (Perkin Elmer XRD1640). For this purpose, geometrical and physical data provided by the manufacturer were first accurately modeled in GATE. As a second step, 2D and 3D images simulations were performed according to clinical protocols: projections over a 200° arc, 1° increments, and 41cm square field of view. IMRT QA and, RANDO anthropomorphic phantom and head‐and‐neck patient CT were used as input objects for simulations. Results: From the obtained series of 2D projections a 3D reconstruction was performed in each case, and the dimensions of the reconstructed structures were compared with actual objects. Conclusion: We have developed within the GATE platform a complete and detailed model of a 160 MLC LINAC with its MVCBCT flat panel. Future work will consist in simulating epid‐based transit dosimetry.
SU‐E‐J‐182: Validation of Two Mathematical Formalisms for Tissue Characterization in Dual Energy Computed Tomography
Authors: Bourque A , Carrier J , Bouchard H
Published: July 2013

Purpose: While dose calculations are typically performed using a simplistic correspondence of HU to electron density (ED), recent developments in DECT for radiotherapy could provide significant improvements in characterizing human tissues for such purpose. We aim to compare and validate two DECT mathematical formalisms and evaluate their accuracy in terms of ED and effective atomic number (Z) for radiotherapy applications. Methods: Two cylindrical phantoms (Catphan 504 and Gammex 467) containing tissue substitutes are scanned with a Philips Gemini GXL CT at 90, 120 and 140 kV. Two mathematical formalisms are developed and implemented using MATLAB, allowing the extraction of ED and effective Z maps of various materials, given a pair of CT images taken at two distinctive energies. The first formalism is based on a parameterization of XCOM cross sections and uses generic photon spectra provided by the manufacturer. The second formalism is based on a stoichiometric calibration of HU and uses experimental data and the substitutes' composition. A novel definition of effective Z is developed for both formalisms. Results: With the 90–120 kV energy pair, the extraction of relative ED of the Catphan materials leads to a maximum relative error of 5% for the XCOM‐based formalism and 2% for the stoichiometric‐based formalism. In the instance of the Gammex materials, higher density materials, as bones, present errors up to 31% and 15% respectively. Conclusion: While the stoichiometric‐based formalism demonstrates a clear advantage over the XCOM‐based formalism in the analysis of CT data acquired clinically, both yield reasonable accuracy for low‐Z elements materials. Conversely, the results for high‐Z materials are negatively affected by discontinuities present in photoelectric effect cross sections. An advanced formalism, which would precisely parameterize this effect, is expected to yield improvements in accuracy and lead the way to a successful implantation of DECT in radiotherapy treatment planning.
SU‐E‐J‐183: Modeling of a Single Blood Vessel Embolized with Yttrium‐90 Or Praseodymium‐142 Glass Microspheres Using Monte Carlo Simulation
Authors: Ferreira M , Podder T , Jung J
Published: July 2013

Purpose: To develop a realistic model for the dosimetric distribution and to evaluate biological effective dose (BED) of Praseodymium‐142 (Pr‐142) and Yttrium (Y‐90) glass microspheres in blood vessel within tumor for treating hepatocellular carcinoma (HCC). Methods: Blood vessels filled with uniformly distributed microspheres were modeled and positioned along the central axis of the tumor. Cylindrical blood vessels had diameters ranging from 25.0 to 75.0 μm, while tumor radii were varied from 0.2 to 1.0 cm, based on average values reported in the literature. Physical dose distributions due to a single blood vessel were simulated using Monte Carlo (MCNPX2.6) for large combinations of blood vessel and tumor sizes. HCC doubling times (DT) ranging from 17 to 720 days were used as a parameter in the BED calculations. To quantify the BED coverage throughout the entire tumor volume, a BED volume histogram (BEDVH) was calculated for each nuclide. Results: The physical dose distribution for both nuclides was comparable, e.g. dose per decay of 1.95 × 10−12 Gy and 2.36 × 10−12 Gy were obtained for Pr‐142 and 90‐Y point source at the same point, respectively. Pr‐142 distribution yielded higher BED coverage of the tumor volume for all DT, blood vessel sizes, and tumor sizes. The differences were higher for fast proliferating tumors and smaller tumor and blood vessel sizes, e.g. for 17 days DT, 20 μm blood vessel diameter and 0.2 cm tumor radius, BED coverage of 150 Gy was 48.7 % higher for Pr‐142. Conclusion: Blood vessels modeled within tumors made possible the quantification of the dose range due to a single embolized blood vessel. From BEDVH evaluation it appeared that the biological effectiveness throughout the tumor was sensiμtive to the radionuclide used, tumor sizes, and blood vessel sizes. The work was partially supported by a Ralph E. Powe Junior Faculty Enhancement Award provided by Oak Ridge Associated Universities.
SU‐E‐J‐184: A Dose‐Independent Relative Biological Effect Quantity for Targeted Alpha‐Particle Therapy in the EQD2 Formalism
Authors: Hobbs R , Sgouros G , Howell R
Published: July 2013

Purpose: Alpha particle radiopharmaceutical therapy (&agrRPT) is currently enjoying increasing attention as a viable alternative to chemotherapy for targeting of disseminated micrometastatic disease. The relative biological effectiveness (RBE) quantifies the different biological effects per unit absorbed dose of α particles versus traditional electron mediated radiation damage. However, the RBE is dependent on the dose and is therefore limited in its utility. We propose the adoption of a quantity, called the RBE2, a dose‐independent ratio between the existing linear equivalents (i.e., EQD2s) of the different radiation doses. Methods: Recently, the equivalent 2 Gy per fraction been chosen as the linear standard by the ICRU, denoted EQD2. Since the electron mediated dose has a linear equivalent, and the α particle dose response curve is characterized by a linear coefficient, we have chosen to define the RBE2 as the ratio between the two linear coefficients and propose to adopt it as the relevant quantity to be reported. The theoretical framework for the proposed new formalism is presented as is the application to two tumor cell lines. Results: The radiobiological parameters are obtained by fitting the corresponding linear quadratic and linear curves to the dose response data from two cell lines: a murine breast cancer cell line, and a human breast cancer cell line. From these, the linear coefficient for both the BED and the EQD2 response lines are derived for fractionated irradiation as are the RBE values: 2.4 9.0 for the murine cell line and 2.4 – 6.0 for the human cell line. The invariant RBE2 values were 5.9 and 4.5. Conclusion: RBE2 defined as a ratio between the electron mediated 2 Gy fraction equivalent EQD2 and the linear alpha response is independent of dosimetric endpoint and provides a more logical formalism than the dose dependent definition for data reporting and dose conversions.
SU‐E‐J‐189: Validation of An Iterative Deconvolution Algorithm for the Post Treatment Y90 PET Image of Liver Cancer
Authors: Qu H , Yu N , Xia P
Published: July 2013

Purpose: To validate the interactive deconvolution method for dose calculation from breathing effect removed Y90 PET image and to quantify impact of the breathing pattern variation on the dose distributionMethods: A computer phantom simulating the PET image of a liver tumor was constructed to investigate an internally developed program using the Van Cittert of deconvolution to remove the breathing motion effect from PET images. The tumor radius was 4 cm. Typical pixel values and noise inside and outside the tumor were taken from real patient Y90 PET images (inside tumor 415±164, outside tumor 15±37, negative pixels were clipped to 0). The phantom PET image was convolved with a breathing kernel (breathing amplitude 2 cm). These images were then deconvolved with the Van‐Cittert deconvolution. The pixel values of the convolved and the deconvolved image are compared with the original image. Results: The root mean square (rms) of the pixel difference between the original and convoluted image was 3.33, and between the original and the deconvolved images was 0.54. Conclusion: The deconvolution removed most of the changes in pixel value sue to breathing.
SU‐E‐J‐193: Evaluation of Variations in Gross Tumor Volume and Tumor Control Probability During Treatment Using KV Cone‐Beam CT in Non‐Small Cell Lung Carcinoma
Authors: Ali I , Algan O , Gunter T , Herman T , Ahmad S
Published: July 2013

Purpose: To investigate quantitatively the variations of gross tumor volume (GTV) as a function of time and cumulative dose during radiotherapy treatment and evaluate corresponding tumor control probability (TCP) in non‐small cell lung carcinoma (NSCLC). Methods and Materials: CT and cone‐beam CT images for 20‐patients NSCLC were reviewed, retrospectively, which included CBCT imaging that was used to setup seventeen patients treated with hypofractionated stereotactic body radiation therapy (SBRT) and three‐patients treated with conventional fractions. The visible GTVs were outlined on CT from simulation and used in treatment planning to obtain optimized dose coverage. Furthermore, GTVs were outlined on CBCT images from daily setup with IGRT. The GTV variations were investigated as a function of time and cumulative dose delivered during the treatment course. TCP was calculated using GTV and mean dose from different CT imaging sets considering alpha‐beta ratio of 10, survival fraction of 0.5 and colognes cancer density of seven millions for NSCLC. Results: For the patients treated with conventional fractionation, the GTV decreased to 30% of initial volume as a function of cumulative delivered dose through the treatment course. In contrary, GTVs for SBRT patients increased up to 45% relative to the original volume as a function of cumulative dose delivered within few days. The volume shrinkage in conventional fractionation and enlargement in SBRT represented the response of tumor cells which was employed in determining TCP. Although, the variations in the mean dose were within 3%, the corresponding TCP varied by 10% during the course of treatment. Conclusions: In NSCLC, GTV and corresponding TCP varied significantly during the treatment with SBRT or conventional radiation therapy. The GTV variations provided quantitative measures of tumor response to radiation therapy that can be extracted from volumetric on‐board CBCT imaging which can be employed in treatment planning to perform adaptive radiation therapy.
SU‐E‐J‐194: Radiation‐Induced CT Number Changes Observed During Radiotherapy for Head and Neck Cancer
Authors: Moraru I , Yang C , Li X
Published: July 2013

Purpose: To characterize radiation‐induced CT number (CTN) changes in tumor from the CT data acquired during IGRT for head and neck cancer and to investigate the potential for using this change as a indicator for response‐adapted radiation therapy (RT). Methods: CTN changes are measured on daily diagnostic‐quality CTs acquired during image‐guided IMRT using a CT‐on‐rails (CTVision, Siemens) for 28 patients with head and neck cancers, treated with 70 Gy in 35 fractions and concurrent chemotherapy. Differences in the distribution of CTN in Hounsfield units (HU) for various regions of interest (ROI) between daily CT sets were quantified. These RIOs included gross tumor volume (GTV), the spinal cord irradiated and un‐irradiated tissues. Statistical analyses compared histogram mean, standard deviation, CTN of peak and asymmetry, to indentify inter‐fraction trends in each ROI. Correlation of the CTN changes in GTV with tumor regression and local control will be evaluated. Results: Inter‐fraction changes in the CTN histograms for the GTV were observed with no ascertainable correlations to tumor regression. Similar, but reduced changes, were observed in the cord. There were no noticeable CT number differences in un‐irradiated tissue, indicating that the changes observed in the GTV and cord were radiation‐induced. For the GTV, the mean CT number varied as much as +13 HU and −12.5 HU over 30 fractions. Conclusion: Radiation‐induced CT number changes in tumor were observed from the daily CTs acquired during RT delivery for patients with head and neck cancer. The inter‐fraction CTN variation was patient‐specific and showed no correlation to tumor regression for the patients studied. The correlation of the CTN change in tumor with local control will be presented.
SU‐E‐J‐198: Preliminary Study On Liver Radiation Response to Stereotactic Body Radiotherapy in Patients with Hepatocellular Carcinoma Based On MRI
Authors: Liang Y , Kirichenko A , Klepchick P , Gayou O
Published: July 2013

Purpose: To develop a method to study the MRI response of tumor and hepatic parenchyma to stereotactic body radiotherapy (SBRT) in patients with hepatocellular carcinoma (HCC). Methods: Two adult patients with HCC who received SBRT were recruited in this IRB‐approved study. T1‐weighted delayed phase contrast Eovist enhanced MRI were performed immediately prior to and three months after SBRT. The time between contrast injection and MRI acquisition was identical for both pre‐and post‐SBRT imaging sessions. The patients received 3D conformal SBRT with a total dose of 40 Gy in 5 fractions. Both MRI images were rigidly registered to the planning computed tomography (CT) data set and the 3‐dimensional (3D) dosimetry data, focusing on local alignment in the tumor region. The average MRI intensity in the spleen was calculated for the pre‐and post‐SBRT images, and subtracted to reduce the effects of different imaging conditions. For each 4.9mm by 4.9mm by 6mm voxel in a region of interest (ROI) delimited by the liver contour and the 20 Gy isodose line, the difference in MRI intensity between pre‐and post‐SBRT images was generated and correlated to radiation dose deposited in that voxel. Results: The Pearson correlation coefficient between MRI intensity change and dose was 0.58 (p<0.0001) for patient A and 0.33 (p<0.0001) for the patient B. Conclusion: We have developed a method to study tumor and hepatic parenchyma response to radiation dose in HCC patients treated with SBRT. Our preliminary data suggest a correlation between the radiologic response and dose in HCC p333atients treated with SBRT, and warrant further study with more patients.
SU‐E‐J‐205: Fast Computation of Actual Dose Delivered to the Patient for IMRT Treatment Quality Assessment and Adaptive Radiotherapy
Authors: Lee J , Jacques R , Li W , Plishker W , Moore J , Shekhar R , McNutt T , Wong J
Published: July 2013

Purpose: To fast and accurately compute the actual dose delivered to the patient during the intensity‐modulated radiotherapy (IMRT) for treatment quality assessment and adaptive radiotherapy. Methods: A fast deformable image registration (DIR) and dose computation engines both accelerated by a graphics processing unit (GPU) are implemented and integrated into a unified framework. DIR automatically aligns the planning CT (pCT) to daily cone‐beam CTs (CBCTs) by a rigid registration followed by a b‐spline registration using mutual information as a similarity metric. The dose engine computes the dose on the registered pCT by using a superposition/convolution technique. The implemented tool was tested on a retrospective image data set of a head and neck cancer patient. The pCT was registered to 13 CBCTs (among 30 fractions) and the actual dose was computed on each registered CT. Results: The average computation time for DIR of the pCT and CBCT of 512×512×136 was less than one minute. The dose was computed on a 171×171×136 grid with the grid size of 3.8×3.8×3.0 mm3, and the average computation time was 0.6 second/beam. The registered pCT showed 7.5% improvement in terms of normalized cross‐correlation with the CBCT while simple couch shifts showed only 4.4% improvement. Our GPU dose engine enables over 30 times speed‐up over the highly optimized commercial treatment planning software (Pinnacle, Philips, Madison, WI). Conclusion: A unified framework for computing the actual dose delivered to the patient was implemented. The implemented tool significantly improves the accuracy of dose computation by DIR followed by direct dose computation on the deformed pCT while maintaining a substantially improved performance over CPU‐based implementations. The superior performance of this tool will enable an efficient and accurate on‐line dose computation for treatment quality assessment and adaptive radiotherapy. This work was supported in part by National Science Foundation under grant No. EEC9731748, in part by Johns Hopkins University internal funds, and in part by National Institute of Health ‐ National Cancer Institute under grant No. 2R42CA137886.
SU‐E‐J‐207: Dosimetric Effects of Diaphragm Motion On Spine SBRT Treatments
Authors: Winey B , Chen H , Daartz J , Gierga D , Shin J , Schwab J , Oh K
Published: July 2013

Purpose: To evaluate the dosimetric effects of diaphragm motion on spine SBRT treatments with IMRT and VMAT deliveries. Methods: Five 4D‐CTs were acquired for lung and liver SBRT patients. The vertebral body closest to the diaphragm level was contoured as the CTV. The thecal sac was used as a surrogate for the spinal cord PRV. A 3 mm expansion was used for the PTV with the PTV cropped to the cord PRV. Plans were optimized for PTV maximal coverage at 1800 cGy and cord PRV Dmax was no more than 1200 cGy. Plans were created for 7 and 11 field IMRT and partial arc VMAT modalities, all on the average intensity CT (AvgCT) and recalculated on the 4D CT data set. D95, D1, and Davg were recorded for the different delivery methods and all 11 CT sets (AvgCT+10 phases). Dose differences were calculated for each phase versus the planning AvgCT and for the maximum difference amongst the phases to evaluate worst‐case scenarios. Results: Regarding the PTV coverage, the IMRT‐7 plans always exhibited the largest dosimetric differences amongst metrics with Davg having the largest range (Davg[max‐min]: 63 cGy) versus the IMRT‐11 (51 cGy) and VMAT (49 cGy) out of the prescription dose of 1800 cGy. All other dosimetric points had smaller differences for PTV metrics. The maximum difference always occurred between exhale and inhale phases. The cord PRV D1% had smaller differences than PTV metrics with the maximum dose difference of 44 cGy for the IMRT‐7 plans, and IMRT‐11 had a maximum of 17 cGy and VMAT 23 cGy. Conclusions: Clinically relevant spine SBRT plans display less than 4% maximum difference in PTV dose (Davg) and 3% variability in the cord PRV D1% metric between inhale and exhale phases for IMRT‐7 plans in the worst case scenario.
SU‐E‐J‐208: Synthetic Datasets for Adaptive Radiotherapy Simulations in Lung Cancer Using Principal Component Analysis (PCA)
Authors: Dial C , Hugo G , Siebers J
Published: July 2013

Purpose: To evaluate feasibility of PCA to produce realistic lung cancer datasets that demonstrate clinically relevant trends of tumor regression for simulating adaptive radiotherapy treatments. Methods: For four non small cell lung cancer patients, weekly helical CTs were acquired during the course of radiotherapy under active breathing control (ABC) and structures of interest were delineated by a qualified physician. Following a rigid alignment, week one images were registered to each of the remaining weeks using the ITK Demons algorithm. Resulting displacement vector fields (DVF) were reshaped into a composite matrix for PCA, with each row consisting of weekly displacements for homologous points. Intermediate and extrapolated PCA coefficients were drawn from a linear fit of the data and from a patient specific PDF constructed using kernel density estimation of the original coefficients. Principal components and the intermediate coefficients were employed to generate synthetic DVFs. A pseudo inverse DVF was generated from each intermediate synthetic DVF and used to warp week one images and contours for each patient resulting in eight sets of images and contours. Dice coefficients for week one risk structures and all propagated contours of the same are calculated in an effort to quantify the realistic nature of the images i.e. how well each represents a realistic pose of patient anatomy. Tumor volumes are calculated for each time point and are compared with clinically observed regression trends. Results: Dice coefficients for all (both linear and random samples) heart, left lung, and right lung contours were respectively: 0.9, 0.9, 0.9, for patient 1; 0.7, 0.6, 0.7, for patient 2; 0.6, 0.7, 0.7 for patient 3; and 0.3, 0.3, 0.2 for patient 4. Mean decrease in tumor volume was 36.2 percent. Conclusions: PCA has the potential to produce realistic poses of patient anatomy that exhibit clinically relevant trends of regression. Funded by P01‐CA116602
SU‐E‐J‐210: Setup Error Effects of Different Cycles During Treatment Process of Esophageal Cancer Radiotherapy
Authors: Shang K , Chi Z , Wang J
Published: July 2013

Purpose: The purpose of this study is to measure setup errors before treatment delivery and observe the change of setup errors throughout the entire course of radiotherapy. Methods: There were 23 esophageal cancer patients were treated by Elekta linear accelerator, a total of 405 CBCT scans were analysed. The systematic and random errors were calculated in the X, Y, Z axis. The date of each group was analysed by SNK statistical method. Results: All data can be divided into 6 groups. It is respectively the first to fifth week and general setup errors. The systematic and random errors (cm) in the X axis were 0.33, 0.32, 0.34, 0.37, 0.45,0.35 and 0.28, 0.28, 0.31, 0.32, 0.34, 0.31. The systematic and random errors (cm) in the Y axis were 0.61, 0.62,0.65, 0.66, 0.61, 0.63 and 0.5, 0.5, 0.53, 0.54,0.46, 0.53. The systematic and random errors (cm) in the Z axis were 0.26, 0.26, 0.29, 0.29, 0.37,0.29 and 0.19, 0.2, 0.21, 0.21, 0.24, 0.21. It has found that the systematic error gradually increased with the increase of treatment time in the X, Z axis, setup errors have no obvious change in the Y axis. No significant correlations were found between each group in the Y axis, P value > 0.05. The fifth week and other groups was found to be significant in the X and Z axis, P values < 0.05, no significant correlations were found between the rest groups, P value > 0.05. Conclusion: The first week or previous setup errors can guide subsequent treatment, but in the end of treatment (after at most 20 times) the treatment plan should be redesigned. Because each treatment situation is different, if allows, the CBCT should be used for each treatment.
SU‐E‐J‐211: Hierarchical Clustering and Classification Improve the Prediction of GTV Response Throughout the Course of Head and Neck IMRT
Authors: Yock A , Rao A , Dong L , Beadle B , Zhang L , Yang J , Kudchadker R , Court L
Published: July 2013

Purpose: To better predict the changing oropharyngeal tumor volume throughout the course of treatment for response assessment and potential adaptive intervention. Methods: Nineteen patients with an aggregate of 35 GTVs (primary and nodal volumes) received daily CT‐on‐rails imaging for image‐guided IMRT of the oropharynx. Daily images were registered to planning CTs using deformable image registration. Resulting vector fields were used to propagate contours from the planning CT to each daily CT. The time course of the changing volume of each GTV was smoothed by Gaussian process regression. Each GTV was selected as a test case, while the remaining 34 were used to generate class models using principal component analysis and hierarchical clustering. A time course of each class model was generated from the median principal component score of that class. For each test case, the accuracy of the optimal class model was compared with that of a static model assuming no change in volume from the planning CT, and with that of a linear model assuming GTV volume decreased at a constant rate. Subsets of test case daily imaging data were used to represent daily, every other day, weekly, and biweekly imaging schemes. For each scheme, the accuracy of classifying the test case as the optimal class model was evaluated at various treatment times. Results: The root mean square error of the optimal class model was less than that of the static and linear models by 10.1% and 9.7% of the initial GTV volume, respectively. This improved accuracy was statistically significant at the p<0.001 level. The accuracy of the classification was around 50% at the start of treatment and increased with the number of treated fractions. It did not vary considerably across imaging schemes. Conclusion: Class solutions predict the GTV volume throughout treatment with accuracy greater than conventional clinical methods. The presenting author acknowledges financial support from: 1) The University of Texas Graduate School of Biomedical Sciences at Houston; The University of Texas MD Anderson Cancer Center, Houston, TX 77030; 2) American Legion Auxiliary Fellowship, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX 77030.
SU‐E‐J‐216: The Effect of Using Visicoil Fiducial Marker
Authors: Alkhatib A
Published: July 2013

Purpose:: In our clinic we are looking to start using Visicoil fiducial marker for prostate. This study is to see the effect of using Visicoi Fiducial marker. Methods and materials: Tissue equivalent solid phantom and 1 gold marker (1.10 mm diameter*, 10mm).. The gold marker was placed at 4 cm depth and three LiF (TLD‐100) size 3.2mm2*, 0.9mm)was placed at 2 mm downstream position relative to the gold marker. We used a16 MVphoton beam and 10*,10 cm2 field size. The phantom was set at 100 cm SSD. 200MUwas delivered. After the gold was removed, another f three TLD were irradiated withthe same photon beam, field size, and SSD. TLD Were analyzed with Harshaw TLD reader 3500. Microsoft Office Excel was also used to analyze the dataResults and discussion: We found that adding the fiducial marker caused a 15% dose enhancement. Because the pair production dominates at this energy andwith high Z material (gold foil), it is possible that the enhancement was due to the pairProduction. Conclusions: This study showed that there is dose enhancement between due to gold MarkerThe Result suggests the possibility of hot spots up to 15 % due to this type of fiducial marker.
SU‐E‐J‐220: Respiration Simulating Phantom for Verifying Four‐Dimensional Radiation Therapy
Authors: Lim S
Published: July 2013

Purpose: To develop the respiration simulating phantom moves real organ motion for verifying 4D radiotherapy such as gated radiotherapy, breathing control radiotherapy and dynamic tumor tracking radiotherapy. Methods: Conventional 4D verifying phantoms move periodically and regularly, unlike the real patients' organs. The phantom developed in this study to simulate patients' organ motion. The controller of moving phantom monitors and records the patients' respiratory waves measured by various modalities, and moves the phantom according to the prerecorded respiratory signal. The organ and the phantom motion were compared with its respiratory curves to evaluate the simulating phantom. Results: Comparing with the respiration signals and the phantom motion, the correlation coefficient between the curves were acceptable relative to the organ motion. The discrepancy between the respiratory curve and the organ motion was 8.52% of motion rangeConclusion: The phantom simulated the organ motion according to the respiratory signal. The relationship between the signal of respiration and the phantom motion showed strong correlation. It is expected that the record and playable simulating phantom is useful to verify the 4D radiotherapy. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education, Science and Technology (2010‐0013701).
SU‐E‐J‐222: An Analysis of the Deep Inspiration Breath Hold (DIBH) Treatment Delivery Uncertainties
Authors: Tang X , Cullip T , Dooley J , Zhu X , Lian J , Lawrence M , Zagar T , Jones E , Marks L , Chang S
Published: July 2013

Purpose: Depends on how stable the patient can hold her breath during beam on time, there are always some degrees of uncertainties associated with the Deep Inspiration Breath Hold (DIBH) delivery. We herein analyze 1) how much motion patients have during delivery; 2) how much dosimetric uncertainties are resulted due to motion. Methods: The data from three tangential‐field DIBH breast patients were studied. AlignRT (Vision RT Ltd., London, UK), a 3D surface matching‐based system was used for the DIBH clinical implementation. AlignRT calculated and recorded, in realtime, the differences between the treatment surface and planned surface, including 3 translational and 3 rotational displacements (RTDs). We programmed our in‐house TPS to read RTDs and calculate the final dose distribution and DVHs patients have received. The received dose and DVHs were compared with the planned ones. We also calculated the mean (m) and standard deviation (σ) of the RTDs, trying to correlate them to the delivery uncertainties. Results: All patients had <0.2mm and 0.7° σ. Patient A had m_A = (−0.02mm, −0.05mm, 0.02mm, 1.11°, −0.36°, 0.45°). Her delivered dose distribution is slightly different than the plan with 3D max dose difference of 0.4%. No noticeable differences on the DVHs. Patients B and C had larger rotational displacement with m_B = (−0.07mm, 0.01mm, 0.12mm, 1.80°, 2.17°, 1.06°) and m_C = (−0.04mm, −0.04mm, −0.04mm, 2.09°, −0.22°, 0.97°). The resulted 3D max dose differences are both 1%. Although dose distributions in the center treatment regions are as planned, there are different hot spots and cold spots in the peripheral areas. Both patients had slightly lower received heart, lung, and target DVHs. Conclusion: No significant translational motion was observed, and its impact to the delivery uncertainties seems small. Larger rotational displacements seem to cause some dosimetric uncertainties. More data need to be collected for quantitative results.
SU‐E‐T‐02: Novel GPU‐Based Pencil Beam Dose Calculation Including Beam Characteristics
Authors: Sullivan A
Published: July 2013

Purpose: A fast, accurate pencil beam dose calculation method for IMPT can be used to produce higher quality treatment by enabling many different treatment plans to be tested and compared rapidly. It is important that the dose calculation correctly include the effects of the finite SAD, and changes in beam emittance associated with changes in energy and treatment port. Methods: We exploit the combined capabilities of both CUDA and OpenGL to propagate a set of Gaussian pencil beams decomposed into weighted superposition of elementary Gaussian beamlets. In alternating phases the beamlets are advanced using CUDA to compute the beam propagation and OpenGL to compute the energy deposition into voxels. Using tabulated integrated Bragg curves and beam divergences obtained from measured machine‐specific data, CUDA is used to compute energy lost by the beamlets due to the stopping power of the tissue obtained by conversion from CT, increases in beamlet size using the differential Highland formula to model scattering, and changes in the beamlet shapes due to finite SAD and beam emittance. The OpenGL geometry processor uses the beamlet centroid position, size, eccentricity and orientation to generate oriented rectangular geometry that is projected onto dose volume slices. The OpenGL fragment processor uses a scaled Error Function texture map to deposit beamlet energy into voxels. Both protons and carbon ions are easily handled as is the contribution of large angle scattered ions. Results: Using 25 beamlets per pencil beam, the dose can be computed a square grid of 24×24×36 beam spots in a 90× 90× 250 mm3 voxel volume (1.0 mm voxel size) in 4.1 seconds using nVidia GTX580 graphics card with high accuracy. Conclusion: We have developed a novel algorithm that exploits the strengths of both CUDA and OpenGL to enable fast, accurate dose calculation from ions in heterogeneous, high resolution CT volumes.
SU‐E‐T‐04: Application of a Semi‐Empirical Formula to Determine Spread Out Bragg Peak Factors and Range Shifter Factors From Measured Depth Dose Curves for Passively Scattered Proton Beams
Authors: Sahoo N , Dhanesar S , Poenisch F , Lii M , Li H , Zhang X , Amos R , Wu R , Gillin M , Zhu X
Published: July 2013

Purpose: To apply two semi‐empirical formulas to calculate the spread out Bragg peak ( SOBP ) factors ( SOBPFs ) and range shifter ( RS ) factors (RSFs) used for monitor unit calculation1 of passively scattered proton beams (PSPB). Methods: Following two recently developed2 and refined formulas were used to calculate the SOBPFs and RSFs from measured depth dose curves for various options in our PSPB. For a specific option,SOBPF(W)=PDDWr(0)/PDDW(0).((SSD+dcW)/(SSD+dcWr))2 .NSF ( W).For the same SOBP width and option, but for different RS thickness, t,RSF (W, t) = (( SSD+dcWt) /)( SSD+dcW)) 2.( PDDW( 0) /PDDW(t)). NSF(W,t),W is the width of the SOBP under consideration, Wr is the reference SOBP width relative to which other SOBPF are determined, SSD is the source to surface distance for the percentage depth dose (PDD) measurement, PDDWr(0) and PDDW(0) are PDDs at the surface, and dcW and dcWr are the depths of the centers of SOBP under consideration and reference SOBP respectively, t is the RS thickness, dcWt and dcW are the depths of the centers of SOBP under consideration with RS of thickness t and without any RS respectively, and PDDw(t) and PDDw(0) are the PDD at depth t and surface respectively for the SOBP width W for which the RSFs are being determined. NSF(W) and NSF (W,t) accounts for the contribution of the nozzle elements including the aperture to the back scatter dose at the dose monitor. Results: The SOBPFs and RSFs calculated using above formulas agreed within 2 percent with their respective measured values using an ionization chamber in a water tank for all of our clinically used options for PSPB. Conclusion: The proposed formulas were found to be valid to calculate the required SOBPFs and RSFs for monitor unit calculation of PSPB fields from measured PDD curves. 1N. Sahoo et. al, Med. Phys. 35, 5088, (2008).2N. Sahoo et. al, IJROBP, 84(3S) S849, (2012).
SU‐E‐T‐16: A Feasibility Investigation: Radiotherapy Dose Calculation On KV Cone‐Beam CT Image for Lung Tumor
Authors: Ma C
Published: July 2013

Purpose: To eveluate the performance of the KV CBCT image data used for dose calculation with precise calibration of Hounsfield units(HU)to electron density(HU‐density). Methods: The CBCT calibration was analysed with different field angle in this study. A clinical treatment planning system was employed for plan CT and KV CBCT image to dose calculations and subsequent comparisons. Two HU‐density tables generated using the Computerized Imaging Reference Systems(CIRS) phantom. By performing dose comparision between CT and CBCT image‐based plans. Results: The results showed that a max of about 4% dose discrepancy was observed for inserts.The sigle field isodose curves were very close and less discrepant. The lung clinical patient study indicated that the volume of lung tumor achieved the prescribed dose in CBCT was lower than the CT plan. Conclusion: Our study showed that the dosimetric accuracy of CBCT‐based dose calculation for lung tumor is acceptable only for the purpose of dosimetric checks with calibration applied. KV CBCT images can not replace the traditional CT images for accuracy dose calculation.
SU‐E‐T‐17: Validation of in House Developed Software Designed Ad Hoc for IMRT QA Analysis Using EPID Based Images
Authors: Aranda F Salinas , Sansogne R , Suarez V , Franco M , Escobar J , Arbiser S
Published: July 2013

Purpose: To validate a recently installed portal dosimetry system that uses an in‐house developed analysis application for IMRT QAMethods: 6 MV X‐ray Intensity modulated radiation therapy fields were delivered by a Varian 6EX linear accelerator (Varian Associates, Palo Alto, CA, USA) equipped with Millennium 120 multileaf collimator. An aS1000 Portal Vision electronic portal imaging device (EPID) was installed on the accelerator. In‐house gamma analysis software was designed to compare the dosimetryc distributions predicted by the XiO 4.50 treatment planning system (Elekta, Stockholm, Sweden) and those measured by the EPID system. The EPID was calibrated in absolute dose following the vendor recommended procedure. IMRT fields were acquired at a source to detector distance of 105 cm. To validate the results of the in‐house software the same IMRT fields were also acquired using a commercial 2D array of 445 n‐type diodes system arranged in a 22 cm side cm2 area subdivided in two density regions. A central 10 cm side region containing 221 diodes spaced 7.07 mm and an outer region containing 221 diodes spaced 14.14 mm. Gamma distribution map and percentage of points with gamma <1 using a 3% 3mm criterion were used in both cases as QA pass/fail criteria. Results: For one given field, gamma distribution maps of both detectors were similar in the inner region of the field but EPID based images showed more discrepancies in the borders of the fields due to higher resolution of the EPID panel. Percentage of points with gamma < 1 was between 97.2 and 99.7 for EPID images and between 96.0 and 100% for 2D diode array. Conclusion: The software developed ad hoc for EPID based IMRT QA proved to be a valuable and affordable solution. Similar results confirm soundness of the analysis performed by the software and validates its clinical use.
SU‐E‐T‐19: Monte Carlo Simulation of XHA600D 6MV Linear Accelerator
Authors: Dai Y , Lin H , Wu B , Cai J , Pei X , Cao R , Chen C
Published: July 2013

Purpose: A Monte Carlo model of XHA600D 6MV Linac is built, which provides the tool to validate and analyses the beam feature for ARTS (Accurate Radiotherapy System). Methods: Monte Carlo method is a useful tool to build the Linac model for an accurate TPS. This work uses BEAMnrc and DOSXYZnrc to model XHA600D Linac. The simulation is divided into three parts: the first part is the accelerator fixed part for different fields, which includes from the target to the mirror. This part PSF (phase space file) is very important to ensure the precision of the following simulation. Thus the largest PSF is simulated without variance reduced techniques except for the high electron cut‐off (1MeV) to ensure the efficiency of recorded particles. The second part is the beam adjustable part including JAW and MLC to form different fields. A set of PSFs is obtained to store the different field information. The third part is the phantom simulation irradiating by the 2th PSF using DOSXYZnrc. The energy of the incident mono‐energy electron is adjusted by matching the simulation to the measured PDD and OAR for 10cmx10cm field, as well as referring to other fields, such as 5cmx5cm and 20cmx20cm. Results: After many iterations of trial and error, the optimized 6 MV is ensured for XHA600D 6MV. A Gaussian beam profile (with σ = 1 mm, FHWH= 0.0 MeV) is used for the incident electron beam at the target surface. The difference is under 2%/3mm for in the field and in the penumbra. An irregular field forming by MLC also is simulated. Conclusion: This work builds a Monte Carlo model of XHA600D 6MV Linac to provide the tool for the dose validation and the beam feature analysis for ARTS.
SU‐E‐T‐23: Evaluation of Inherent Dose‐Uncertainty for VMAT Using a Dose‐Uncertainty Model
Authors: Inoue T , Sugimoto S , Horikawa Y , Kurokawa C , Fukata K , Isobe A , Ito K , Sasai K
Published: July 2013

Purpose: To investigate the dose‐uncertainty, which can occur during treatment planning and dose delivery in volumetric modulated arc therapy (VMAT), by generalizing the dose‐uncertainty model. Methods: QA plans for prostate cancer patient treated with VMAT were created for a water phantom (40×40×20 cm3) using Pinnacle3 radiation treatment planning (RTP) system (Philips Radiation Oncology Systems, WI). The plans were exported as DICOM RT dose files from RTP, and the files were processed using an in‐house program to obtain the dose‐uncertainty map. Inherent uncertainty of dose calculation (IU) was evaluated in this study. The IU considered was originated from the error of dose in a high‐dose gradient region caused by finite size of the calculation grid and the finite size of the detector system during RTP commissioning. The IU was calculated for each control point and was summed up to obtain total IU for each VMAT plan. Results: Dose‐uncertainty distribution on the isocenter plane in the axial slice was investigated. Comparing with a dose‐uncertainty distribution of a prostate IMRT plan, which was evaluated by other group, dose‐uncertainty distribution of VMAT plan spread out around the target, because beams entered from all directions over 360 degrees. The maximum IU, which was around 10 % of the prescribed dose, appeared the boundary between the target and the OARs (bladder and rectum). Conclusion: In this study, dose distribution with the inherent dose‐uncertainty was evaluated using the dose‐uncertainty model generalized for VMAT, and applied to VMAT plans for prostate cancer patients. IU of VMAT spread out around the target if compared with that of IMRT plan. The maximum dose‐uncertainty was found in the boundary between the target and OARs and was about 10 % of prescribed dose. This work was supported by JSPS KAKENHI Grant Number 23791449