bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐99: Utilizing CT Dual DFOV in Treatment Planning System
Authors: Lee HyunSeok , Kim Jung‐in , Sung Wonmo , In Park Jong , Ye Sung‐Joon
Published: July 2013

Purpose: Typically, a Display Field of View (DFOV) parameter is set to accommodate the wide part of anatomy in the scan, which is the shoulder in head and neck (HN) cases. The dual DFOV can make use of two image resolutions in one CT set. However, the Eclipse™ treatment planning system (TPS) accepts only the larger DFOV when reconstructing the body volume and thus particularly reduces in‐plane resolution of the head region. In this study, we developed a method to preserve the original resolution of head CT images. Methods: The anthromorphic phantom was scanned from head to shoulder using the GE LightSpeed RT16 CT by applying small and large DFOV to head and shoulder region, respectively. Our in‐house software enables dual DFOV CT images to be entered into Eclipse™ TPS. Basically, the software converts the pixel size of head CT images from 512 × 512 to 1024 × 1024 by expanding the DFOV outside. Results: The original and processed CT sets were loaded by Eclipse™ TPS and the differences were analyzed. Processed CT images showed two times better resolution than original CT images in head region. Distinguishing target and OAR in processed CT images is also clearer than before. We also confirmed that the mean percentage difference of CT numbers before and after processing was negligible for total phantom volume of the head region except steep gradient region which CT number changes drastically. Conclusion: The advantage of dual DFOV images was fully taken in the Eclipse™ TPS. In treatment planning, defining target and organ at risk which is the most important part for accurate treatment can be improved using high resolution dual DFOV CT images.
SU‐E‐J‐100: Automatic CT‐To‐CT Contour Segmentation Using Deformable Image Registration Software for Head and Neck (H&N) Cancer Adaptive Radiotherapy
Authors: Kumarasiri A , Kim J , Liu C , Siddiqui F , Chetty I
Published: July 2013

Purpose: The significant amount of time required for target delineation continues to be one of the major challenges associated with adaptive radiotherapy. The aim of this study is to evaluate the accuracy of efficient deformable image registration algorithms (available in the VelocityAI and Varian SmartAdapt commercial systems) for automatic segmentation of physician contours from planning CT to mid‐treatment CT images for H&N adaptive radiotherapy. Methods: Ten head and neck cancer patients were considered for this study, each with a planning CT (CT1) and a second CT (CT2) taken approximately 3 weeks into treatment. Treatment volumes and organs were manually delineated by a physician on both sets of CT scans. B‐spline‐based VelocityAI and Demons‐based SmartAdapt DIR algorithms were used to automatically deform CT1 and the relevant contour sets onto corresponding CT2 images. For each DIR, the volume of interest was set to encompass the whole contour set. The agreement of the automatically propagated contours with manually drawn contours of CT2 was visually evaluated by a physician, and the volume overlap was quantified using DICE coefficients. Results: The overall mean (1SD) DICE indices were 0.72(0.11) for VelocityAI and 0.68(0.17) for SmartAdapt. Both software attained a high degree of correlation for well differentiated and relatively large organs, with DICE indices often exceeding 0.8. Organs with small volumes and/or those with poorly defined boundaries showed less correlation (DICE: ̃0.5), likely due to volume averaging effects. Target volume contours generally aligned well (DICE: 0.7–0.8 for PTVs and 0.8–0.9 for GTVs). Conclusion: Use of automatic DIR‐based contour segmentation in H&N adaptive RT is likely to mitigate the need for manual delineation and thereby improve efficiency. More work is needed to evaluate the accuracy of these tools for routine clinical use, particularly for organs with small volumes, or those with poorly defined boundaries. This work is supported in part by Varian Medical Systems, Palo Alto, CA
SU‐E‐J‐102: Evaluation of a Free‐Form Intensity Based Deformable Contouring Method for Prostate Image‐Guided Adaptive Radiation Therapy (IGART): Multiple Observer Comparison
Authors: Ellis R , Traughber B , Kaminsky D , Kan C , Shen Z , Pirozzi S , Piper J , Nelson D , Nelson A
Published: July 2013

Purpose: To evaluate a deformable contouring method for prostate CBCT as part of an automated image‐guided adaptive radiation therapy (IGART) workflow using manual contours from multiple observers for comparison. Methods: Three CBCTs were randomly selected from 6 patients with prostate cancer. Prostate, bladder, and rectum contours were manually defined on the first CBCT by a single observer and on the second and third CBCTs by 3 observers. A commercial free‐form intensity based deformable contouring method was used to deform contours from the first CBCT to the second and third CBCTs. The Dice coefficients and the absolute differences in measured daily delivered dose values were calculated among the deformed and manual contours. Two‐sided t‐tests were performed to determine whether the automated deformable method has similar variability to manual contours from multiple observers. Results: The mean(SD) Dice coefficients calculated between the deformed contours and each of the manual contours were 0.72(0.10), 0.75(0.13), 0.64(0.10) for the prostate, bladder, and rectum, respectively, and were 0.76(0.08), 0.87(0.05), 0.68(0.10), respectively, averaged between multiple observer comparisons. These differences were not significant for the rectum (p=0.095) but were significant for the prostate (p=0.025) and bladder (p<0.001). The absolute differences in measured daily delivered dose values between the deformed and manual contours were not significantly different when compared to the variability for multiple observers for both the prostate and rectum (p > 0.05). The difference was significant for the bladder (p <0.05). Conclusion: A free‐form intensity based deformable contouring method for IGART was shown to have similar variability to that of multiple observers both for delivered dose evaluation of the prostate and rectum as well as contour similarity of the rectum while a significant difference existed for bladder dose evaluation. This method has the potential to show deviations from planned treatment as part of an automated prostate IGART workflow. J Piper, D Nelson, and A Nelson are owners of MIM Software Inc. Z Shen and S Pirozzi are employees of MIM Software Inc.
SU‐E‐J‐104: Developing a New Automatic Fast Algorithm for Recognizing the Seed Positions in Permanent Brachytherapy of Prostate
Authors: Gholamian M , Yazdi M , Faghihi R , Mohammadi M
Published: July 2013

Purpose: Permanent prostate seed implant brachytherapy is one of the effective treatments for prostate cancer. Using three‐film technique in which radiographic planar images are taken from three directions is a common method in evaluating the location and the number of seeds implanted in the prostate.The aim of this study is to validate the seeds' location and estimate accurately the number of implanted I‐125 seeds using an automatic method. Methods: We first enhance the resolution of seeds by applying some image preprocessing techniques on radiographic images. Then for recognizing automatically all seeds in the images we extract certain features and design filters based on mean and standard deviation of gray values for removing noises from images. Eventually we can segment the image into some distinguished regions. After filtering, without cropping any region of the image (for instance prostate) mean and standard deviation are measured for each region in the image which determine if this region belongs to seeds or not. At last we analyze histogram of seeds' region to still determine if this region belongs to an isolated seed or some overlapped seeds. Results: Postimplant fluoroscopic images of 27 patients that were taken from three different directions were used in our study. Our algorithm effectively processed properties of three images of each patient and quickly and automatically recognized the seeds. The algorithm was able to recognize one hundred percent of seeds without missing any image properties. Conclusion: previous works crop region of prostate that includes majority of seeds and then perform noise removal and seeds recognition. So they ignore seeds out of that region. In contrast, the merit of our presented algorithm is not only in recognizing automatically seeds in a short time by using adaptive filters but also not in not missing any seeds even out of prostate region.
SU‐E‐J‐105: Applying Fuzzy Similarity Index and Ground Truth Fuzzy Contour to Evaluate the Segmentation Accuracy
Authors: Kim H , Monroe J , Machtay M , Ellis R , Lo S , Yao M , Sohn J
Published: July 2013

Purpose: To evaluate the segmentation accuracy by using our novel Fuzzy Similarity Index (FSI) and Ground Truth Fuzzy Contour (GTFC) with the consideration of inter‐and intra‐observer variation Methods: We developed GTFC to build consensus truth segmentation(contour) and FSI to score segmentation for an objective and quantitative evaluation of in‐vivo medical image segmentation. GTFC is built by applying Fuzzy theory to consider with inter‐and intra‐observer variation. GTFC has the Fuzzy Membership Function(FMFn) which can assign a weight to each expert depending on their experience, unlike STAPLE. By using GTFC, we formulate a quantitative scoring index to evaluate the segmentation accuracy.When a test segmentation is evaluated, we calculate the membership value of FMFn at every point in the test segmentation(contour). Then, we can make a distribution of membership value as Membership Score Histogram(MSH). We enhanced FSI to make more responsive. While generating a single value index(FSI) from MSH, we adopt the strategy of penalizing lower membership values. The resultant FSI equation is formulated by combining MSH with a penalty constant. We tested the FSI by applying to a brain case. Ten experts segmented a region in the brain and six non‐experts independently delineated the same place. GTFC was created from segmentations of expert to evaluate the accuracy of segmentations of non‐experts. Then, we calculated FSI after making a MSH per test segmentation. Results: The order in higher similarity to GTFC is non‐expert 6, 1, 5, 2, 3, and 4. Non‐expert 2, 3, and 4 are significantly deviated from GTFC. Their FSIs are 0.845, 0.476, 0.125, 0.085, 0.078, and 0.005, respectively. Conclusions:FSI can sensitively reflect the accuracy of test segmentation. It can be used to develop unbiased educational tools or credential process for clinicians. It can be also used to evaluate the performance of automated segmentation tools.
SU‐E‐J‐106: The Correct Formula to Calculate Volume Overlap
Authors: Lu W , Tan S
Published: July 2013

Purpose: To describe formulas that are often used incorrectly in calculating overlap between two tumor volumes, and to illustrate the correct formula with simulation and patient data. Methods: A review of the literature showed that there are various formulas used for calculating volume overlap between two volumes A and B. We demonstrated that formulas using one volume as base (BinA, AinB) led to overestimation or underestimation of the overlap. We illustrated the correct formula of Overlap based on Dice coefficient: 2&astInterception/(A+B). We compared the differences with both simulation and patient data. Results: When A and B had the same volume, there was no differences between all formulas. When the volume of A was larger than the volume of B, BinA overestimated the Overlap while AinB underestimated the Overlap in both simulation and patient data. The formula using max(BinA, AinB) overestimated the Overlap by a factor of almost 2. Overlap based on Dice coefficient agreed well with perceived overlap. Conclusion: The Dice Coefficient with the sum of two volumes as base should be used to calculate volume overlap. National Cancer Institute Grant R21 CA131979
SU‐E‐J‐112: Improving Target Delineation by Using Deformably Registered Multi‐Modality Images for Radiation Therapy of Pancreatic Cancer
Authors: Cungeng Y , Moraru I , Dalah E , Hart V , Paulson E , Erickson B , Li X
Published: July 2013

Purpose: Combined multi‐modality images can provide unsurpassed target recognition, yet the large variation of contour volumes from different modalities prevents accurate contour transfer onto CT images for radiation therapy (RT) planning. This work demonstrates improved agreement between target contours from multi‐modality images after deformable multi‐modality image registration for pancreatic cancer RT. Methods: PET, and various MRI including T1, T2, DWI and DCE, were rigidly registered with CT for representative patients with pancreatic cancer. Gross tumor volume (GTV) and organs at risk (OARs) were delineated on multi‐modality images and were exported into a presently‐developed deformable multi‐modality image registration tool. The derived deformation fields were applied to deform the corresponding modality contours, which were later overlapped onto the planning CT. Results: Substantial variations among contours from multi‐modality images were observed for both GTV and OARs. When using T1 weighted contour volume as nominator, the delineated volumes vary from 0.5 to 1.8 for GTV, from 0.81 to 1.05 for OARs between the image modalities. The overlapping ratio between different modalities varies from 0.22 to 0.74 for GTV, and from 0.65 to 0.84 for OARs. After deformable image registration, the contour volumes are changed by deformation fields by 6% to 11%. These changes do not impact volume variation between different modalities, but improve significantly the overlapping ratio between contours from different modalities, for example, changing from 0.55 to 0.82 for GTV. Conclusion: The deformable image registration of multi‐modality images increases the agreement between contours from different image modalities, improving accuracy for target and normal structure delineation for radiation treatment planning of pancreatic cancer.
SU‐E‐J‐118: Quantifying Intrafractional Prostate Rotation From Cone‐Beam Computed Tomography with Radiopaque Markers
Authors: Huang CY , Tehrani J , Ng J , Keall P
Published: July 2013

Purpose: Rotation greater than one degree can severely compromise the accuracy of radiotherapy treatments. Prostate intrafractional rotation is not negligible because peristaltic motion has a time scale that is shorter than the delivery time of a single fraction. The objectives of this study were (a) to investigate a clinically feasible method to calculate tumor rotation with radiopaque markers; (b) to evaluate the accuracy of the method and (c) to quantify prostate intrafractional rotation in intensity‐modulated arc radiotherapy (IMAT). Methods: 2D kilovoltage (kV) cone‐beam computed tomography (CBCT) images were acquired for prostate cancer patients with three implanted gold fiducial markers as surrogates for the tumor position during fractionated IMAT. Post‐treatment, 3D marker trajectories were determined from the 2D projection images by maximum likelihood estimation of a 3D probability density function. Rotations about each axis (roll (right/left), pitch (superior/inferior) and yaw (anterior/posterior)) were then calculated based on the iterative closest point algorithm (ICP) method, with the origin set at the centroid of the three markers. Results: Intrafractional rotation was studied for10 patients, 268 fractions. Average roll, pitch and yaw were calculated as 0.85±1.16, 0.41±0.51 and 0.37±0.4 degrees, respectively. Roll was the predominant rotation among the three axes which is consistent with literature. Rotations showed a similar pattern among different fractions for the same patient. The pattern acquired at the first few fractions could be used as a prediction for the rest of the fractions. Conclusion: This study demonstrated a novel method to quantify intrafractional tumor rotation from 2D kV projections with radiopaque markers. Intrafractional rotation is small but significant for certain cases. Real‐time radiopaque marker‐based adaptive radiotherapy with rotation correction could make a positive impact on reducing treatment margins. This project is supported by an NHMRC Australia Fellowship and NHMRC project grant 1034060.
SU‐E‐J‐121: Assess Interfractional Tumor Motion in SBRT Lung Treatment Using 4D Cone‐Beam CT
Authors: Li J , Yu Y , Xiao Y
Published: July 2013

Purpose: The aim of the study was to assess interfractional tumor motion in SBRT lung treatment using 4D cone‐beam computed tomography (CBCT) to check if tumor motion changes during treatment course. Methods: 4D CBCT scans of eight SBRT lung patients were studied. Each patient had 3‐6 fraction treatments. The patients were scanned on an Elekta XVI cone‐beam imaging system with 4D acquisition module before each fraction treatment. Reconstructed 4D CBCT images were binned into 10 phases (frames). Tumor motion was assessed on the XVI system by measuring the maximum displacements of tumor border positions among 10 frame images, in superior‐inferior (SI), anterior‐posterior (AP), and lateral (LAT) directions. Interfactional tumor motion change was determined by calculating the difference of tumor motion of each fraction and that measured on planning 4DCT. Results: The tumor motion changes were in the ranges of −0.5∼0.4 cm, −0.3∼0.2 cm, and −0.3∼0.2 cm, in the SI, AP, and LAT directions, respectively. Conclusion: 4D CBCT was used to assess interfractional tumor motions. The results showed that the tumor motion changes were clinically insignificant, with respect to the 1‐cm margin used to generate planning target volume from internal target volume.
SU‐E‐J‐126: Development of a Prospective Gating Algorithm for a Novel 4DCT Technique: Retrospective Data Analysis
Authors: Low D , Thomas D , White B , Gaudio S , Jani S , Lee P , Lamb J
Published: July 2013

Purpose: To develop an algorithm for prospective CT scan selection to support a novel 4DCT acquisition techniqueMethods: The new 4DCT acquisition protocol utilizes repeated rapid helical CT scans. Unless the CT scanner is synchronized with breathing, scans may be acquired at similar breathing phases, adding little to the breathing‐motion characterization and therefore unnecessarily irradiating the patient. A retrospective patient dataset that consisted of 25 repeated CT scans and the accompanying breathing‐motion model was used to evaluate possible algorithms. A single coronal slice through the lungs was used for the preliminary analysis. The mean model discrepancy for the 25 scans was 0.63mm. The model was fit using every subset of N scans out of the first 15. The discrepancies ranged from nearly 0.68mm to 9mm. The variation was hypothesized to be due to the level of redundancy in the scans used to generate the motion models. Scans were evaluated based on how the breathing depth and breathing rate (the two independent variables in the breathing motion model) differed at each CT slice. To maximize the variety of breaths selected from the 15, the mean square root difference D of the breathing depths and breathing rates were calculated and D correlated against the model discrepancies. Results: The square‐root difference function provided excellent separation between scan combinations that had small and large model errors. As expected, scans sets with small model errors were well spaced in breathing depth and rate. An optimal combination of breathing depth and rate was identified and shown to better select good scan combinations relative to separately analyzing depth or rate. The optimal scans had residual errors of 0.68mm compared with the baseline of 0.63mm. Conclusion: The use of the mean square‐root difference in breathing depth and rate will provide guidance for prospective scanning algorithm development. Supported by NIH R01 CA096679
SU‐E‐J‐127: Diaphragm Motion Heterogeneity as a Result of Gravity‐Induced Pleural Pressure Variation During Free Breathing
Authors: Li G , Rosu M , Wei J , Kincard R , Mechalakos J
Published: July 2013

Purpose: To investigate the physical relationship between diaphragm motion heterogeneity and gravity direction for lung motion modeling. It is hypothesized that diaphragm motion overcomes gravity‐induced pressure (GIP) to provide even common pleural pressure (CPP) around the chest wall during free breathing. Methods: A respiratory motion model was developed to account for GIP over CPP at posterior (P) and anterior (A) chest wall. The movement of the diaphragm to compensate for GIP was assumed proportional to GIP, given GIP<|CPP‖max. Thus, the P/A ratio of diaphragm motion (M‐PA) should equal the P/A ratio of pleural pressure (P‐PA). To estimate GIP, lung density and AP separation were measured on full‐inhalation phase CT, assuming the lung as viscous fluid. At anterior side(supine), both S‐I and P‐A motions produced CPP with an assumed equal contribution. Software was developed to quantify and visualize lung density gradient. 4DCT images from 12 supine patients and 5 patients in both supine and prone positions were analyzed. Local lung densities were monitored at 4 regions: A, P, I and S, and the density ratios (D‐PA and D‐SI) were calculated for all supine and prone scans. Results: For the 12 supine patients, it was found that diaphragm motion was location dependent: A=0.7(σ=0.3)cm, I=1.4(σ=0.5)cm, and P=2.4(σ=1.0)cm. A lung gradient aligned with gravity: D‐PA=1.6(σ=0.5) while D‐SI=1.0(σ=0.2). Calculated GIP=3.7(σ=0.9)cmH2O [=2.7(σ=0.7)mmHg] was similar to |CPP|max (− 3mmHg at inhalation). The estimated M‐PA=4 [=P‐PA=(2xCPP)/(0.5xCPP)] was consistent with the measured M‐PA=3.9(|=1.6). In the 5 patients with both supine and prone scans, 4 reversed the motion ratio from supine [M‐PA=3.7(|=1.6)] to prone [M‐PA=1.1(|=0.8)]. Conclusion: This study demonstrated a population‐based physical relationship of local motion of the diaphragm and the direction of gravity. Posterior diaphragm (supine) was predicted and confirmed to move about 4 times more than anterior diaphragm. Further study on individual‐based physical relationships is currently under investigation.
SU‐E‐J‐130: Impact of Audiovisual Biofeedback Respiratory Training On 4D‐CT Image Quality
Authors: Yamamoto T , Gopalan S , Berger J , Johnston E , Chung M , Eclov N , Diehn M , Loo B , Keall P
Published: July 2013

Purpose: Current 4D‐CT methods show artifacts of >4 mm in 90% of cases. The purpose of this study was to quantify the impact of audiovisual (AV) biofeedback respiratory training on 4D‐CT image quality. Methods: In an IRB‐approved clinical trial, two 4D‐CT scans, with free breathing and AV biofeedback, were acquired with a cine method for ten thoracic cancer patients. AV biofeedback was based on: (1) a patient‐specific representative waveform and a dot moving according to the abdominal displacement, displayed on the video screen of the goggles; and (2) musical guides provided with a beat period corresponding to the waveform period. The phase‐sorted 4D‐CT images with free breathing and AV biofeedback at the 0% (peak‐inhale), 30%, 50% (peak‐exhale) and 80% phases were analyzed for artifacts by: (1) comparing normalized cross correlation (NCC)‐based scores (artifacts give high scores) of the two images; and (2) quantifying a score for each pair based on visual assessment of the two image sets (positive scores mean that AV biofeedback 4D‐CT is of higher quality). We tested the hypothesis that AV biofeedback significantly reduces 4D‐CT artifacts using the two‐tailed paired t‐test. Results: The impact of AV biofeedback varied widely with patients and phases, suggesting inconsistent patient compliance. For example, patient 5 demonstrated a visual assessment score of 4 at the 0% phase indicating that AV biofeedback 4D‐CT was of higher quality than free breathing, while a score of −3 at the 50% phase indicating the opposite. Overall, no statistically significant differences were found in both the NCC‐based score (free 0.60±0.32 vs. AV 0.65±0.27, p=0.32) and visual assessment score (0.0±2.7, p=0.95). Conclusion: A 10‐patient study demonstrated no statistically significant impact of AV biofeedback respiratory training on 4D‐CT image quality. Future studies will investigate strategies to improve patient compliance by optimizing the biofeedback interface or increasing patient comfort. NIH/NCI 2 R01 CA 093626; Stanford BioX Interdisciplinary Initiatives Program
SU‐E‐J‐131: Investigation of Inter‐Fraction Organ Motions for Patients with Pancreatic Cancer
Authors: Amoush A , Xia P , Abdel‐Wahab M
Published: July 2013

Purpose: To investigate the inter‐fractional pancreatic and right kidney motions for patients with pancreatic tumors using daily KV‐CBCTs, acquired prior to each treatment. Materials:Five patients with pancreatic adenocarcinoma, who had the stent placements and received IMRT with daily KV‐CBCT, were selected for this study. Five weekly CBCTs were input to the Pinnacle treatment planning system and aligned with the planning CT (pCT) with the three different registration methods to investigate inter‐fractional organ motions. For each patient, a triple imaging registration was performed, (a) bone‐based registration; (2) right‐kidney based registration; (3) marker‐based registration; Subtracting shifts of the latter two registrations from the bone registration, we obtained the interfraction motions for the pancreas and the right kidney. The CTV contours on the CBCTs were transferred from the pCT by using a point‐based registration. The right kidney contours on the CBCTs were manually defined. Subsequently, IMRT plans were applied to these CBCTs with the iso‐center shifted according to the different registration methods. Dose calculations on the CBCT were set to a density of 1 g/cc. The CTV receiving 95% of the prescription dose (V95%) and the mean dose to the right kidney were evaluated. Results: A total of 25 CBCT images were analyzed. The average kidney‐motion was 3.58±0.43 mm, 2.08±0.36 mm, and 5.94±0.87 mm in the lateral, vertical and longitudinal directions, respectively. The CTV motion was 5.09±0.31 mm, 2.75±0.17 mm, and 7.80±0.92 mm, respectively. V95% of the CTV was 95.65±5.46 %, and 94±11.47 % for the bone‐based registration and marker‐based registrations, respectively. The mean dose of the right kidney was 6.16±1.87 Gy and 7.48±2.96 Gy, respectively. Conclusions:The CTV V95% is comparable between bone‐registration and marker‐based registration and the mean dose to the right kidney is well below the tolerance. Update results from more patients and more daily CBCTs will be reported.
SU‐E‐J‐135: Measurements of Non‐Linearity Features of Breathing Patterns Using Recurrence Quantification Analysis (RQA) and Dynamic Complexity (DC)
Authors: Kuo H , Tome W , Hong L , Yaparpalvi R , Garg M , Guha C , Kalnicki S
Published: July 2013

Purpose: To investigate if there exists a difference in breathing patterns between patients treated with SBRT and IMRT using RQA and DC (using K2: correlation entropy and D2: correlation dimension) measures. Methods: 9 patients treated with SBRT and 8 treated with IMRT were scanned with 4D CT and the breathing patterns were acquired. One of the SBRT patients was scanned with and without meditation. Each breathing signal consisted of a scalar time series and recurrence quantification analysis (RQA) was utilized to determine the following measures: Periodicity of the system as percentage of recurrence points (%RR), determinism (DET), maximum diagonal line length (L_max) whose inverse the divergence (DIV) is measure for how fast trajectories diverge from each other, the average diagonal line length (L) that can be interpreted as the mean prediction time of the signal, and the entropy (ENTR) a measure of information complexity. In addition the invariant measures of K2 and D2 were also estimated. A locally nonlinear forecast was applied to predict future breathing signals of N time step ahead for the patient with and without meditation. Results: Our results showed %RR has significant correlation with L_max and has inverse correlation with DIV. DET has significant correlation with Lmax, L, ENTR and DIV. Independent t test suggests there is no difference between the SBRT and IMRT groups in terms of the RQA measures and K2. Patient that had undergone meditation showed improvement in %RR, L_max, DIV, K2 and had an estimated correlation dimension of 1.7. Prediction showed similar results for one and three time step ahead but meditation one had better prediction horizon when time step was higher. Conclusion: RQA is a powerful tool that allows one to analyze the dynamic nature of breathing pattern. No significant difference was found in the dynamical complexity of SBRT and IMRT patients.
SU‐E‐J‐139: Real‐Time Motion Management Will Increase the Patient Population Eligible for Lung SBRT
Authors: Berbeco R , Chen A , Keall P , Kozono D , Mak R , Lewis J
Published: July 2013

Purpose: Lung toxicities encountered during the early lung SBRT clinical trials have been largely abated by adhering to strict planning limits on lung dose and PTV size. This restricts the maximum safely treatable GTV size in the presence of respiratory motion. We hypothesize that real‐time motion management (e.g. tracking) will enable the maximum GTV size to increase, thereby making more patients eligible for SBRT. Methods: Consensus maximum PTV size (50 cc) was determined from recent clinical trials and our own clinical experience. PTV as a function of GTV was found for four treatment scenarios: 1 cm motion, no tracking; 0.5 cm motion, no tracking; no motion, no tracking; and, arbitrary motion, with tracking. We included a 0.5 cm/1.0 cm asymmetric CTV to PTV margin for no tracking (RTOG recommendation) and a 0.3 cm margin for tracking, as suggested by the results of Rottmann (2010). No GTV to CTV margin is included. A large population study is used to determine the increase in numbers of patients eligible for SBRT. Results: PTV < 50 cc corresponds to a maximum GTV of 11, 13, 17 and 33 cc, for 1 cm motion (no tracking), 0.5 cm motion (no tracking), no motion (no tracking) and arbitrary motion (with tracking). With tracking, empirically‐derived normal tissue dose constraints can be maintained while increasing the volumes of tumors that we could treat with SBRT by >250%, going from a limit of 13 cc, for tumors moving more than 5 mm, to 33 cc for all motions. This translates to a ∼50% increase in the numbers of patients eligible for lung SBRT. Conclusions: Restrictions on PTV size limit patient eligibility for SBRT, excluding patients with smaller GTVs and respiratory motion. Real‐time motion management will enable a substantial increase in the numbers of patients eligible for SBRT. The project described was supported, in part, by Award Number R21CA156068 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
SU‐E‐J‐146: Effectiveness of Daily Endorectal Balloon for Post‐Prostatectomy Patients Undergoing Pencil Beam Scanning Proton Therapy
Authors: Yin L , Vapiwala N , Sutton D , Ding X , Swisher‐McClure S , Bui V , Deville C , Tochner Z , Both S
Published: July 2013

Purpose: Pencil beam scanning proton therapy was found more sensitive to organ motion as compared to conventional IMRT. Significant target under dose may be caused by interfractional tumor motion. The objectives of this study are to investigate the role of endorectal balloon (ERB) in daily patient setup throughout the course of proton therapy and its dosimetric impact. Methods: Ten consecutive post‐prostatectomy patients enrolled on a prospective IRB approved institutional study were analyzed. Patients underwent CT/MRI simulation and treatment with daily ERB. Six T2‐MRI verification scans were performed during the treatment course. A pencil beam scanning proton therapy plan was generated for each patient. Organ at risks and CTVs were contoured by physician on each of the weekly MRI scans. The MRI scans were subsequently rigidly fused to the CT simulation images in order to simulate 2 possible daily kV‐kV patient setups: 1) alignment to bony anatomy and 2) alignment to pelvic bone in cranial caudal direction and ERB along the AP and lateral direction. The dosimetric impact of interfraction target motion on target coverage was compared for both scenarios. Results: We found that target interfraction motion in the AP direction (average 4.1mm) is consistently larger than those in the lateral and cranial caudal directions (2.9 mm and 3mm respectively) most likely due to variations in bladder volume. ERB can be used to further improve the patient bony alignment(∼1mm), in AP and lateral directions. Overall when both ERB pelvic bony structures are used for patient alignment, we observed a median 4.03% decrease in V98% of CTV as compared to 8.27% when only bony alignment is used. Conclusion: In post‐prostatectomy patients undergoing pencil beam scanning proton therapy, daily patient setup via bony alignment and ERB can stabilize the target and maintain the dose coverage within 5% relative to the initial treatment plans.
SU‐E‐J‐150: Investigation of Feasibility of Concurrent Orthogonal KV and MV X‐Ray Imaging for Tagrget Tracking During Dose Delivery for Stereotactic Body Radiotherapy (SBRT): A Pilot Phantom Study
Authors: Chang Z , Yan H , Yin F
Published: July 2013

Purpose: To investigate feasibility of motion tracking using concurrent orthogonal kV and MV x‐ray images acquired during dose delivery for stereotactic body radiotherapy (SBRT).Method and Materials: The study was performed on a Varian TrueBeam treatment unit, which is equipped with research capability. The research capability allows users to program a procedure of treatment and imaging that are unavailable in conventional clinical environment. The concurrent orthogonal kV and MV x‐ray imaging is novel and provides 3‐dimentional spatial information in real‐time during radiation delivery. In this study, concurrent orthogonal kV and MV x‐ray imaging was programed to be part of 3D‐conformal SBRT delivery and are used for assessment on accuracy of traget motion tracking during radiation delivery. Two experiements were carried out on a CIRS dynamic thorax phantom with a built‐in BB as a tartget: one without motion; the other with motion in a sinusoidal profile with the motion amplitude of 16.0 mm. The motion trajectories acquired by the concurrent orthogonal kV and MV x‐ray imaging were compared against the references. In the work, the discrepancies were quantified using mean and standard deviation (SD). Results: The trajectory profiles acquired by the the concurrent orthogonal kV and MV x‐ray imaging show good agreement with references. A quantitative analysis shows that the average mean discrepancy between kV imaging and known references is 0.0+/−0.2 mm and 0.2+/−0.3 mm for stationary and sinusoidal motion conditions. Similarly, the average mean discrepancy between MV imaging and known references and MV is 0.1+/− 0.2 mm and 0.3+/−0.5 mm for stationary and sinusoidal motion conditions. Conclusion: The results indicate the concurrent orthogonal kV and MV x‐ray imaging can track motion and provide target targeting during radiation delivery with reasonable accuracy. Further work is required to investigate in arc mode and gating delivery mode.
SU‐E‐J‐151: An Off‐Line QA Tool for Evaluating Reproducibility of Deep Inhalation Breath‐Hold Treatment for Breast Radiotherapy
Authors: Yang W , Yue Y , Mirhadi A , Hakimian B , Burnison M , Tuli R , Sandler H , Fraass B
Published: July 2013

Purpose: To develop an off‐line QA tool for evaluating reproducibility of deep inhalation breath hold (DIBH) breast treatment using a spirometer‐based breathing control system with video guidance. Methods: Tangential beam plans (open medial field; lateral IMRT field) used for whole breast radiation with DIBH‐guided by a spirometer‐based breathing control system have been studied. An imaging sequence was set‐up at the treatment machine to acquire multiple (>15) EPID image frames during beam‐on with source‐to‐imager distance 150cm. Images were exported from the EPID and imported into an off‐line QA tool developed in‐house for evaluation of intra‐and inter‐fractional uncertainties for DIBH treatments. The user defines a region of interest (ROI) (e.g., expander, clip(s), chestwall) on the first (reference) image. A rectangular imaging template is created from the ROI with a defined expansion (e.g., 1cm). The template is then matched to each of the following image frames using cross‐correlation. Template movement is used to estimate breast motion during one or multiple breath‐hold sessions. The breath‐hold lung volume information is extracted from the spirometer and compared to the corresponding motion curve estimated from the EPID images. Results: Motions of different regions of the breast can be visualized and shown to be constant within 1mm at the imager (∼.7mm in the breast) for some patients. Both drifting and random motions can be identified on individual patients on specific days. Off‐line analysis of these data may provide a useful tool to assure that individual patients are adequately performing the DIBH technique, and to identify patients who require new margins or planning strategies. Conclusion: An off‐line QA motion evaluation tool has been designed for DIBH breast treatment, and is an effective method for evaluation of DIBH reproducibility both intra‐and inter‐fraction.
SU‐E‐J‐154: Effect of Metal Hip Prostheses On the Accuracy of Electromagnetic Localization and Tracking
Authors: Kurko B , Butler W , Merrick G
Published: July 2013

Purpose: To determine the effect of metal hip prostheses on the accuracy of Calypso electromagnetic transponder tracking. Calypso warns that prosthetic pelvic implants are contraindicated. Methods: Beacon transponders were implanted into prostate phantoms — one as a triangular array and the other with the transponders side‐by‐side. The electromagnetic tracking array was placed 14 cm above the isocenter set by the transponders in the phantom, and two different metal hips were placed either singly or on both sides of the phantom, resting on Styrofoam to be level with the isocenter plane. For each phantom and hip setup, positional data was acquired by the Calypso at 10 Hz for 30 s. Results: With the Beacons forming an isosceles triangle with 1.5 cm long legs, there was no configuration of metal hips, either singly or doubly, that caused an isocenter offset greater than 0.1 mm in any Cartesian direction or in the calculated net vector displacement. However, in the model of a poorly executed Beacon placement with transponders nearly side‐by‐side, almost all the measured mean displacements were greater than 0.1 mm, but only when the proximal part of the metal hip was 5 cm from isocenter. Maximum displacements for single hips were in the lateral direction (∼ 0.5 mm), while displacements in the orthogonal plane were less than half as great. The presence of bi‐lateral hips 5 cm from isocenter had countervailing but not negating effects because the composition of each hip was different. The mean vector displacement was 0.3 mm with close bilateral hip placement and was 0.5 mm with either single hip. Conclusion: Calypso localization and tracking had an error greater than 0.1 mm only when the metal hips were within 5 cm of the transponder isocenter. At clinically realistic distances, all isocenter displacements for mono‐and bi‐lateral hips were negligible.
SU‐E‐J‐158: A Phantom Study Based Simulation to Quantify the Motion and Tumor Volume Affected 18F‐FDG PET Uptake Distribution Within the Tumor
Authors: Wijesooriya K , Griffin C , Pan T , Read P , Benedict S , Larner J
Published: July 2013

Purpose: Molecular imaging studies reveal tumor heterogeneity which may explain therapeutic resistance. Alternatively, variations observed in the SUV uptake may not be due to intrinsic heterogeneity of the tumor, but due to effects of position resolution (volume dependence) or tumor motion . The objective of this study is to evaluate motion artifacts on PET SUV uptake within tumors for different volumes. Methods: We conducted a phantom study: spheres of homogeneous18F‐FDG activity of motion amplitudes (0, 5, 10, 15, 20, 25, 30 mm) and volumes (internal diameter 10, 13, 17, 22, 28, 37 mm). Any variation observed in SUV uptake distribution with homogeneous activity spheres must be due to motion and volume effects. Therefore, we used this data to quantify uptake variations as a function of motion and volume. PET SUV uptake values for each tumor were fit with a Woods‐Saxon model with 2 parameters: radius and skin depth. When the ratio of radius to skin depth becomes small, this simplifies to a Gaussian. By using this parameterization for stationary phantoms, using the probability density function for motion, and using an image convolution technique we were able to simulate the SUV uptake distribution of moving tumor in the central 2D plane. Taking voxel by voxel SUV uptake distribution in 2D we quantified the accuracy of our simulation for motion affected, tumor volume affected SUV uptake heterogeneity within the phantom. Results: Phantom study for stationary spheres show motion has a high impact on SUV uptake distributions. Maximum percentage uptake variation across the phantoms was 22%. Convolution results confirm that motion is the largest contributor to the SUV heterogeneity of the tumors. Average difference between convolution and measurement is 6.7%. Conclusion: These results will impact the studies that utilize tumor heterogeneity such as tumor detection, planning with dose painting, and treatment response. UVA Radiation Oncology george Amarino Grant ‐ 2012
SU‐E‐J‐169: CT Simulation Optimization for Prostate Cancer Patients in Radiation Therapy: A General Strategy
Authors: Li H , Yu L , Low D , Gay H , Michalski J , Mutic S
Published: July 2013

Purpose: To develop a general strategy that allows for automatically, prospectively, and objectively determine the optimal CT simulation technique for individual prostate cancer patients based on radiation‐therapy goals: maintenance of contouring quality and integrity while minimizing patient CT simulation dose. Methods: An image quality index (IQI) was defined to characterize the simulation performance on target delineation required in radiation therapy. The optimal IQI is the one requiring the minimum dose to provide an image dataset that yields target contours that are equivalent to those segmented from greater‐dose‐technique scans. An anthropomorphic pelvis phantom with added‐bolus layers were used to mimic prostate cancer patients with 38–53 cm lateral diameters for the experiments and the prediction model development. The manual prostate contour delineated from the highest dose CT scan was used as the ground truth to determine the optimal IQI for each patient size. A clinical workflow was recommended for selecting optimal CT simulation techniques, incorporating patient size, target delineation, and dose efficiency. Results: The median optimal IQI for accurate manual segmentation (compared to the ground truth) was 5.27 (range 4.8 to 6.32) for patient sizes of 38‐53 cm. 120 kVp scan protocols could not reach the optimal IQI due to the tube power limit for patient sizes above 53 cm. The optimal tube potentials (yielding the lowest radiation dose) for patient sizes of 38–53 cm were 120, 140, 140, and 140, respectively. The corresponding minimum CTDIvol for achieving the optimal image quality were 16.7, 24.7, 48.7, and 96.9 mGy without tube current modulation enabled. Conclusions: The proposed strategy can predict the optimal CT simulation scan protocols with minimum radiation dose while taking into account patient sizes and treatment planning tasks. We expect that the optimal image quality index will vary for automatic segmentation algorithms compare to the manual method.
SU‐E‐J‐171: Variation of the Hounsfield Unit On CT Scanning Parameters and Reconstruction and Its Effect On Dose Calculations
Authors: Lee J , Chan R
Published: July 2013

Purpose: To assess the variation of Hounsfield units (HU) when using different CT scanning parameters. The effect of HU‐to‐electron density (ED) table on the accuracy of dose calculations was evaluated. Methods: A CIRS (model 62) phantom was used for all the tests. Various kVp, field‐of‐view(FOV), reconstruction algorithms with contribution of adaptive statistical iterative reconstruction(ASiR™) (0‐80%) were applied during the scanning process(GE lightspeed CT simulator). HU for each insert was measured. A HU‐ED table was acquired for Philips PET/CT scanner as well. A phantom and clinical cases were selected for dosimetric comparisons using different HU‐ED tables. Results: No significant difference in HU for low electron density material was observed regardless the scanning parameters. The dependency of HU on tube voltage and FOV was observed for bone‐equivalent materials. HU for dense bone scanned with 80kVp was 39.4% higher than HU with 120kVp. HU with small FOV relative to large FOV was 11.9% higher for trabecular bone. The maximum deviation was observed for bone‐equivalent material when the bone algorithm, producing a 6.2% change in HU, or 80% of ASiR™ contribution, were applied; however, the difference in HU was still within the standard deviation. The difference in HU between two scanners was less than the SD observed on GE CT simulator, except for liver‐and bone‐equivalent materials. Using a HU‐ED table with the worse case scenario (80kVp, bone reconstruction with 80% ASiR™), it was shown that the deviation was within 1% for phantom studies and −7.4%−2.7% for clinical cases. The maximum of 5.9% deviation was observed when calculated with a table from the different scanner. Conclusion: The maximum deviation in HU was observed for bone‐equivalent materials when low kVp was applied. Use of a wrong HU‐ED table could introduce −7.4%−2.7% dosimetric error at certain regions of interest.
SU‐E‐J‐175: Comparison of Prone and Supine MRI Sequences for the Purpose of Radiotherapy Treatment Planning for Breast Cancer
Authors: Holloway L , Batumalai V , Moran C , Liney G , Koh E , Lazarus E , Dimigen M , Papadatos G , Boxer M , Chan C , Delaney G
Published: July 2013

Purpose: The delineation of treatment volumes for breast cancer radiotherapy utilising standard CT images is associated with significant inter‐observer variability. It has been proposed that MRI with improved quality soft tissue definition may reduce this inter‐observer variability. The purpose of this study was to determine the optimum set‐up and sequence for MRI‐defined radiotherapy volume delineation. Methods: Six observers (2 radiologists, 4 radiation oncologists) were asked to score MRI quality for the purposes of radiotherapy delineation using the following scoring criteria where 0 — margin not recognisable (inability to discriminate) through to a score of 3, when margins were distinct (excellent discrimination). Supine breast MRI was achieved using a foam bridge to support a surface coil without touching the patient surface. T1 and T2 sequences were compared for both 1.5Tesla (1.5T) and 3T MRIs for a volunteer. Prone breast images were obtained using a 4 channel breast coil. Supine and prone breast MRIs were compared for T1 and T2 sequences for 3T for 2 volunteers. Results: Supine breast images were shown to be acceptable for the purpose of radiotherapy delineation with the 3T 2D T2 turbo spin echo sequences scoring 2s (fair discrimination) and 3s (excellent discrimination), most optimal. The prone 3D T1 gradient echo was the optimum of the supine and prone 3T images, however results were inconsistent with this sequence receiving a score of zero from one observer. Conclusion: Supine breast MRI scans can be achieved for the purpose of radiotherapy delineation. Scoring between observers was inconsistent when comparing scan sequences. This work has been supported by project grant 1033237 from Cancer Australia and The National Breast Cancer Foundation