bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐FF‐J‐71: Gross Tumor Volume Measurement of Simulated Lung Nodules Using PET Imaging
Authors: Mawlawi O , Erasmus JJ , Qiao F , Pan T
Published: July 2006

Objective: The aim of this study is to determine the PET image threshold that will generate the most accurate measurement of gross tumor volume (GTV) of lung nodules. Methods: A NEMA IEC phantom containing six spheres of different sizes (10, 13, 17, 22, 28, and 37 mm diameter) was scanned on a DISCOVERY ST PET/CT scanner. The phantom was scanned three times. In each case, the sphere to back ground ratio was varied (3.4:1, 7.4:1, 13.5:1) while keeping the activity concentration in all spheres constant. To simulate tumor motion, the phantom was positioned on a unidirectional translating platform. A sinusoidal waveform (5 sec cycle, 2cm peak to peak) was used to drive the platform during the PET data acquisition. The platform motion was tracked using an RPM device which sent a trigger signal to the PET scanner at a specific phase of every repeating cycle of the waveform. PET data was acquired in 2D for 30 minutes using LIST mode (10 bins per cycle). PET images were then reconstructed using OSEM. An in‐house software program was then used to find the percent threshold that best estimated the true known sphere volume. The program was written to perform the analysis at an increment of 1% in threshold. Results: Spheres that have minimum partial volume effects (>17 mm) had an average threshold of 32% ±2.8 at different contrast ratios. The smaller spheres had a larger threshold value and a larger standard deviation. The results also showed that the threshold for the small spheres increased with decreasing contrast ratio. For the 13 mm sphere the threshold changed from 27% to 55% when using a contrast of 13.5:1 to 3.4:1. Conclusion: A threshold of 32% gave the most accurate GTV. The effects of scan duration on this threshold will also be presented.
SU‐FF‐J‐72: Growth and Initial Area Under Curve Correlation Data From DCE‐MRI For Treatment Planning and Monitoring in Orthotopic Tumor Models
Authors: Shetty A , Stafford R , Esparza‐Coss E , Elliott A , Hazle J
Published: July 2006

Purpose: Human bladder carcinoma cells are grown in murine bladders to study antitumor effects of new‐targeted therapies. Lack of data on their neovascularity in mice, makes it difficult to evaluate new therapies like anti‐angiogenesis or potential of combinatorial treatments like radiation and hyperthermia. The purpose of this study is to understand the changes in neovascularity with tumor growth in relevant organ environment, with dynamic contrast enhanced magnetic resonance.

Method and Materials: Eleven athymic nude mice underwent orthotopic injection of 106 253‐J B‐V cells. MR imaging was performed on a 4.7 T small animal MR scanner (Bruker Biospin). Axial T1 weighted spin‐echo and T2 weighted fast spin‐echo acquisitions were performed for volumetric measurements. For dynamic studies, an axial 3D fast RF‐spoiled gradient‐echo acquisition (TR:40ms, TE:1.4ms,slice thickness:1mm) with gadolinium‐DTPA (Magnevist, Schering) was used. The time points for imaging were day 15, day 21 and day 30 after inoculation. Results: The MR volume measurements were made from the fused T1 and T2 images, which were shown to better delineate tumor margins in previous experiments. The initial area under the curve was plotted at 90 seconds, 120 seconds and 200 seconds. An inverse relationship was noted between larger tumor sizes and their IAUC90 (correlation=−0.866 (p<0.05). There is no correlation between the slow growing tumors and the IAUC90 (p=0.708). No significant difference was noted between IAUC 90,120,200 of the whole tumor and tumor rim (p<. 05). Conclusion: The inverse relationship between the IAUC and tumor growths maybe indicative of developing necrotic core. The IAUC and tumor growth data correlation could be a predictive tool for radiation response. It could also assist in planning time points for anti‐angiogenic therapy. In future, we plan to use a higher molecular weight gadolinium agent, and stain the tissue for mean vessel density.
SU‐FF‐J‐74: High Accuracy of Volumetric Image Registration of CT, MR and PET Images
Authors: Li G , Xie H , Ning H , Citrin D , Capala J , Arora B , Coleman C , Camphausen K , Miller R
Published: July 2006

Purpose: To test the accuracy of the 3D volumetric image registration technique, the registration has been evaluated against “co‐registered” CT phantom images, MR/MR intramodality images and PET/CT images. Method and Materials: The 3D volumetric image registration is voxel‐based, using the homogeneous color distribution in the volumetric views of the skin voxel landmarks as the registration criterion and guidance for alignment. The software is built for up‐to‐4 concurrent imaging modality registration with real‐time volumetric manipulation and display (supported by a volume rendering board). Sixteen CT head phantom images are acquired with known spatial shifts, as well as fourteen patient cranial MR/MR (T1/T2/FLAIR) images from the same MR scanner and twenty‐five patient cranial PET/CT images from a hybrid scanner. Results: A sub‐voxel detection limit (0.1 degree/voxel) is achieved for CT/CT phantom image registration as the alignment is indicated by the color homogeneity of the aligned skin voxels, which represents a new dimension for monitoring the image registration. For the MR/MR image registration, it is found that 71% of the “co‐registered” images acquired from the same scanner within 5 minutes of each other exhibit a misalignment, caused by voluntary patient movement. The “distance” deviations between the co‐registered and voxel‐registered images are 0.2°±0.4° and 0.5±0.5 voxels. For the PET/CT image registration, 88% images have detectable misalignment due to higher probability of patient movement during longer scan time (<10 minutes) and the deviations are determined to be 0.4°±0.5° and 0.9±0.5 voxels. These movement‐induced misalignments can be corrected using the 3D volumetric image registration technique. Conclusion: The 3D volumetric image registration technique has sub‐degree/sub‐voxel accuracy in CT, MR and PET image registration. It can successfully detect misalignments in the co‐registered images visually and should be applied to correct the image misalignment caused by voluntary patient movement.
SU‐FF‐J‐77: Image Quality Assessment for An Investigational Megavoltage Cone‐Beam CT Device
Authors: Chen H , Simpson L , Morin O , Pouliot J , Sarkar A
Published: July 2006

Purpose: Megavoltage Cone‐Beam CT (MVCBCT) is an essential image guided radiation therapy (IGRT) device to acquire patient's daily treatment CT for accurate localization of treatment targets. The objective of this research was to assess its image qualities. Method and Materials: The image quality of MVCBCT was assessed by four indicators: noise, contrast, spatial resolution, and CT intensity stability. A CT electron density phantom and a Siemens calibration phantom were used. The images were acquired under various MU settings. The Siemens Syngo image processing software was used to sample and analysis the data. Results: The noise factor was used and found that the more MU to acquire the images produced less noise. 6 MU is the cut‐off value for noise factor of less than 5%. For contrast of the outer ring of the CT phantom, the electron density range of <0.952 and >0.976 were visible on all MUs. For the inner ring, we only see <0.952 and >1.052 on MU <9 and >1.043 for MU > 15. For the CT intensity stability, if the CT number differences has to be <50 to qualify as “stable”, then 6 MU is the border line. For spatial resolution, MTF was used to evaluate Siemens phantom images. The lp/mm at MTF = 0.5 was 0.07, a little less than the criteria of 0.08 and for MTF = 0.1, we had 0.28, which is >criteria of 0.25. Conclusion: The images from MVCBCT device were assessed for quality indicators; we conclude that the MU of 6 or above would have satisfactory results. For the future application of dose calculation on the MVCBCT images, the CT intensity stability is important, and we found that for 6 MU and above would have stable CT numbers.
SU‐FF‐J‐78: Implanted Marker Movements During Prostate IMRT
Authors: Watchman C , Hamilton R , Welsh J
Published: July 2006

Purpose: To quantify positional variations of implanted markers for prostate localization using the ExacTrac system. Method and Materials: Eleven patients were identified as having undergone IMRT boost for prostate cancer. The ExacTrac system was used to obtain implanted marker positions in the patient by means of two stereoscopic X‐ray tubes and DRRs from CT simulation. Marker positions were recorded by the ExacTrac software and subsequently read using the PTDreader software. Implanted marker positions were analyzed for variations in individual maker coordinate displacement, inter‐seed distances and area transcribed by the three markers. Comparisons of patient data was done for the initial boost phase (1980 cGy/11 fractions) and final boost phase (1080 cGy/6 fractions). Results: Implanted markers had maximum deviations of 3.3 mm (LAT) 6.4 mm (AP) and 3.4 mm (SI). In cases where the maximum deviation was observed for one coordinate the other coordinates were also approaching their maximums. No correlation between individual marker positions was observed. Inter‐seed distances were found to vary by +/− 2 mm over the treatment period. The area of a triangle contained by the three markers was shown to vary daily over the treatment period. In the 6 patients who were observed over both boost phases a trend was observed that indicated a progressive decrease in the area for the 4 patients not on hormone therapy and stable area size for the two on hormone therapy. Of the 11 patients followed over the final boost phase 8 were found to demonstrate a similar decrease but no correlation with hormone status was observed. Conclusions: Implanted markers in the prostate demonstrate daily variations in their positions. The observed variations exceeded the precision of the ExacTrac system. Further work is needed to address the movement of implanted markers in the prostate in positioning algorithms and correlation with prostate volume.
SU‐FF‐J‐80: Implementation of On‐Board Imager for Daily Image‐Guided Radiation Therapy in a Multi Vendor Environment
Authors: Kudchadker R , Zhu X , Zhang L , Lee A , Mohan R , Gillin M , Dong L
Published: July 2006

Purpose: To report our initial experience of using Varian On‐Board Imager (OBI) image‐guided radiation therapy (IGRT), on a prostate patient with implanted fiducials. Varian's OBI‐IGRT system enables clinicians to obtain high‐quality daily kV images of the patient in treatment position. An attempt has been made to implement and evaluate various possible workflow issues, when using the Varian OBI system in a multi vendor environment. Method and Materials: Daily shifts based on OBI images were made on an implanted fiducial prostate patient who underwent treatment on a Varian Trilogy Clinac. Using Varian's OBI 2D/2D matching software, orthogonal kV images were compared to Pinnacle3 (Philips) DRR's generated from the planning CT, to determine and correct for any daily target shifts. The kV images were transferred to our record and verification system IMPAC MultiAccess (Elekta), for physician review. The shifts predicted by the Varian software using manual matching, was compared daily, with the shifts predicted by the Acculoc software (Medtec), based on three implanted fiducials. In addition, cone beam CT (CBCT) scans were acquired once a week, to predict shifts using our in‐house developed 3D/3D‐ matching software. Results: Daily shifts predicted by the Varian OBI 2D/2D software, matched daily shifts predicted by the Acculoc fiducial alignment software to within 1 mm during the entire treatment course. The weekly CBCT scans also confirmed that these shifts were within 1 mm on the same treatment days. Various software issues were discovered and workarounds were proposed using in‐house solutions. For example, the electronic graticule could not be transferred to ACCESS from the OBI software, etc. Conclusions: In‐house validations and implementations were needed to make all pieces of equipment work together correctly. Once the workflow was established, we demonstrated that OBI based IGRT solutions was consistent and clinically acceptable across different vendor's solutions.
SU‐FF‐J‐83: Inaccuracy of Fixed Threshold Segmentation for PET
Authors: Kirov A , Danford C , Schmidtlein C , Yorke E , Yahalom J , Kalaigian H , Hu Y , Larson S , Humm J , Amols H
Published: July 2006

Purpose: Several automatic segmentation methods have been developed to aid physicians in drawing tumor contours from PET images. Our goal is to compare the consistency of current methods for delineating in vivo tumors and uniform objects in phantom. Method and Materials: We compare three published methods, each based on a single threshold value per scan, for segmenting objects in experimental or Monte Carlo simulated PET scans of cylinders and spheres with uniform activity concentration in a phantom, and for segmenting tumor volumes in the torso from 20 patient PET scans. Results: For uniform activity objects in phantom, segmented volumes generated by the 3 methods differ from true values by more than a factor of 2. The segmented volumes are within a factor of 3 and 4 of the true volume when the objects are larger than twice the PET resolution. Between the methods, the segmented volumes differ by up to 78% and 93% for objects in zero and non‐zero background respectively. These differences are close to the volume change caused by adding a single voxel layer to the surface of the object. The discrepancies between the different segmentation methods are even larger for segmenting in vivo tumors where volume differences larger than a factor of 10 were observed, far larger than the single voxel enlargement effect. Conclusion: Threshold value based segmentation methods can be used only as a rough guide for tumor delineation and then only after adapting to each clinics PET scanner and procedures. Among the probable sources of inaccuracy are various patient dependent factors including tracer uptake non‐uniformity. This suggests that effort in analyzing PET images should be shifted towards providing accurate quantitative information to the physician to improve confidence in target delineation amidst the various phenomena affecting the PET image. Supported in part from NCI Grant P01‐CA59017.
SU‐FF‐J‐84: Integration Of 3D Stereovision System In Image‐Guided Radiotherapy: A DICOM‐Based Method
Authors: Liu D , Li S
Published: July 2006

Purpose: To seamlessly integrate a 3D stereovision system (or 3D camera in short) with complex planning and treatment systems and implement a 3D image‐guided patient positioning for radiotherapy of breast cancer. Method and Materials: A high speed commercially available 3D camera is installed on the ceiling of treatment room to capture real‐time 3D surface images of patient. After transformed from camera coordinate system to treatment machine coordinate system, the real‐time 3D surface image is matched with a 3D reference image which is defined in planning systems. A stack of external contours with position information from DICOM RP and RS files are re‐sampled to generate a 3D reference image. Regions of interest (ROIs) of both images are defined by the PTV parameters from DICOM RP files. An Iterative Closest Points (ICP) algorithm is adopted to conduct the registration of ROIs. The parameters of patient positioning derived from the registration can be used to adjust the patient position. Results: Outliers and noise of captured 3D surface images are automatically and effectively removed when applying PTV parameters to define ROIs. Phantom test and An institutional IRB approved clinical trial indicates a typical case of image‐guided RT, including loading reference images, capturing real‐time images, and image matching can be done in less than 1 min. By double checking with online EPID, a millimeter positioning accurate can be achieved without considering deformation of images. Conclusions: This work shows the clinical potential for utilizing a 3D camera in image‐guided RT. The integration of 3D camera in image‐guided RT based on DICOM standard provides faster and more precise patient positioning than other image‐guided RT. How to deal with the deformation of ROIs will be our research direction in future.
SU‐FF‐J‐89: Intratumoral Pattern of FDG Uptake in Human Xenograft Models
Authors: Pugachev A , Carlin S , Ling C , Humm J
Published: July 2006

Purpose: To investigate the spatial association between intratumoral uptake of 18F‐fluorodeoxyglucose (18F‐FDG) and the following characteristics of the tumor microenvironment: hypoxia, cellular proliferation, and blood flow; and to evaluate the potential impact of this association on the use of 18F‐FDG images in radiation oncology treatment planning for target volume delineation. Method and Materials: Six nude rats were inoculated with the following human tumor cells: HT‐29 (colon adenocarcinoma), DU‐145 (prostate carcinoma), and A549 (NSCLC). When the tumors reached 20–25mm in diameter, the animals were injected with 18F‐FDG, pimonidazole, and bromodeoxyuridine. 1hr post injection animals were imaged on animal PET scanner. Upon completion of imaging, animals were injected with Hoechst33342 and sacrificed with CO2 5–10min later. Tumors were immediately dissected, frozen and cut into 8μm thick sections. One section from each tumor was placed onto a phosphor plate for autoradiography. The images of the fluorescence produced by Hoechst33342 and by fluorescent antibodies raised against pimonidazole and bromodeoxyuridine were acquired from the adjacent sections and co‐registered. The statistical analysis of association between PET tracer uptake and microenvironmental markers was then performed on a pixel‐by‐pixel basis. Results: In all the tumor models studied, we observed positive pixel‐by‐pixel correlation between FDG uptake and pimonidazole staining intensity (correlation coefficients ranged from 0.46 to 0.87, p<0.001). At the same time, correlation with bromodeoxyuridine was always negative (ranged from −0.50 to −0.78, p<0.001). Conclusions: This study further confirms association between foci of increased intratumoral FDG uptake and regions of hypoxia. In addition, it demonstrates reduced FDG uptake in actively growing parts of the tumor. Therefore, the use of FDG uptake iso‐contours to delineate lesions and, especially, to reduce target volume with respect to CT‐defined volume should be approached very carefully, since it might potentially result in exclusion of areas of active tumor growth from the target volume.
SU‐FF‐J‐90: Investigating Factors Affecting Weight Selection for Safe Delivery of Four Dimensional Weighted Radiotherapy (4D‐WRT)
Authors: Alasti H , Vandermeer A , Cho YB , Norrlinger B
Published: July 2006

Purpose: We have developed a 4D weighted radiotherapy (4D‐WRT) method based on 4D‐CT imaging to reduce respiratory motion margins. Here we discuss the issues related to weighting factor selection for this method to incorporate both patient breathing variability and machine performance limitations. Method and Materials: The Varian Real Time Position Management System (RPM) was used to measure patient respiratory motion. Free breathing was assessed based on extent of motion, breathing period, and drift of the mean breathing amplitude. Motion was also evaluated based on average marker trajectory as a function of phase for the entire breathing session. Measurements of machine limitations were conducted using a moving phantom to emulate tumor motion. The MLC motion was synchronized with the phantom motion to simulate 4D‐WRT. By examining log files and film dosimetry we assess MLC motion accuracy, dose rate fluctuations, synchronization errors, and tolerance selection. Results: The analysis of the average marker trajectory shows that the end‐inhale position is more variable than the end‐exhale position, and patients spend more time at end‐exhale. The average extent of motion, period, and drift for these patients was 1.01cm (0.14–1.15cm), 3.80sec (2.30–7.42sec), and 0.50cm (0.14–1.15cm), respectively. There was a correlation between the standard deviation of the period and that of the extent of motion. In the log file it was found that there is a phase delay between the actual and planned MLC position of 50msec, the period of motion was greater by 1% and the amplitude was greater by 0.05cm. It is estimated that a total phase difference of about 0.2sec exists due to delays in the communication, MLC motion, and dose rate stabilization. An appropriate tolerance should be selected to prevent beam hold‐off due to MLC velocity limitations. Conclusions: Quantification of patient breathing variability and machine limitations is necessary for safe delivery of 4D‐WRT.
SU‐FF‐J‐94: Metrics for Assessment of Reproducibility of Respiratory Motion for Four‐Dimensional Computed Tomography Imaging
Authors: Bivens Whitney , Starkschall George , Nelson Christopher
Published: July 2006

Purpose: Image data sets obtained via four‐dimensional (4D) computed tomography (CT) quantify motion of internal anatomy as a function of the patients' unique respiratory cycle. Crucial to the acquisition of high‐quality 4D CT data sets is the regularity of the respiratory cycle. The purpose of the present study is to develop metrics that can be used to assess the regularity of respiratory motion, as well as software that can readily extract these metrics from files containing respiratory information generated in a course of 4D CT image data acquisition. Method and Materials: 4D CT image data sets from 282 patients were acquired, along with files describing motion of an external fiducial that gathered respiratory patterns. Software was developed to calculate the mean amplitude and standard deviation of the amplitude, the mean period and standard deviation of the period of each respiratory data set, and the cross‐correlation of the fiducial amplitude in each respiratory cycle with the mean amplitude. Results: The range of mean amplitudes was 3.45 mm. to 29.26 mm., and the associated standard deviations ranged from 0.04 mm. to 10.64 mm. The mean breathing rates ranged from 2.39 to 10.88 sec., with a range of standard deviations of 0.02 to 10.92 sec. Histograms were generated that relate the various metrics assessed and the variability encountered. The cross‐correlation can be readily displayed for each respiratory cycle. Conclusion: The data obtained is thus available to correlate with the quality of the 4D CT images.
SU‐FF‐J‐96: Modeling Correlation Between External Surface Motion and Internal Organ Motion Based On 4DCT
Authors: Yan H , Yin F , Zhou S , Marks L , Willett C
Published: July 2006

Purpose: To develop a method for modeling the dynamic relationship between patient surface motion and internal target motion using 4DCT data. Then, to verify its capability to interpolate internal target motion with real‐time patient surface motion information. Methods and Materials: At discrete phases of a respiration cycle, the coordinates of points in a grid with specific interval on patient surface and internal target were automatically extracted from 4DCT data, then used for the experiments. A radial basis function (RBF) based neural network was employed for modeling purpose. The model was examined by eight sets of data, which consists of the coordinates of points on a patient surface and a region of interest (ROI) on either the left lung or right lung. Results: With fine sampling (grid interval < 5 voxels) of the patient surface and internal target, less than 5% interpolation error was found in seven experiments. one patient (right side of left lung) with error more than 8% and unexpected displacement more than 10 mm in 4DCT was observed. Coarse sampling (grid interval >10 voxels) will result in a larger interpolation error. In seven successful cases, the interpolation errors less than 2mm. The only failed one had interpolation error of 4 mm. Conclusion: A dynamic model is established for correlating patient surface motion with internal target motion based on 4DCT data. An acceptable interpolation accuracy is achieved for 7 of 8 cases examined in this study. The only failed one implied that for target motion with larger displacement and higher frequency, the model based on 4DCT data might be not sufficient because of its limited time resolution in acquiring those information in a respiration cycle.
SU‐FF‐J‐97: Modeling of Lung Tumor Response to Image‐Guided Radiation Therapy
Authors: Seibert R , Ramsey C , Hines J
Published: July 2006

Purpose: To employ locally weighted regression (LWR) as an empirical technique for modeling tumor response to radiation treatment. Methods and Materials: Daily Megavoltage CT (MVCT) images were acquired prior to the delivery of 660 lung treatment fractions for 20 lung cancer patients, with contoured tumor volumes in each image. This data was used to develop a LWR model of the tumor progression throughout the treatment. A leave one out cross validation (LOOCV) technique was used to evaluate the performance of the model with varied combinations of days of patient data. For each patient, the model was run using the remaining patient data as training data, but with different test observations for each trial. Multiple simulations using random sampling without replacement were carried out to ensure that all possible combinations were tried. The best combination of days was determined by finding the mean predictive performance. Since this technique will eventually be used to make decisions about a patient's treatment, each prediction was accompanied by a measure of its uncertainty. A large uncertainty value indicates that the prediction is unreliable, most likely due to poor coverage of the training data. Results: The LWR model did not always accurately predict the shape of the tumor response curve, but the model was accurate in its predictions made at the end of a patient's treatment. The average prediction error was 13%, and even tumor volume increases during the course of the treatment were correctly predicted. Conclusions: The LWR modeling technique proved fast and fairly accurate when predicting the tumor volume at the end of treatment, and improvement is expected as additional data is added to the memory matrix. Future research will use expert opinion and data analysis techniques to determine which additional patient information will improve the model.
SU‐FF‐J‐99: MR Imaging for Improving IMRT Targeting of Prostate Cancer
Authors: Miften M , Parda D
Published: July 2006

Purpose: To evaluate the role of magnetic resonance imaging (MR) in improving prostate and seminal‐vesicles (SV) target localization as well as penile bulb (PB) tissue avoidance in IMRT for prostate cancer. Method and Materials: CT and MR planning data for 16 patients treated with IMRT were analyzed retrospectively. The prostate, SV, and PB volumes were delineated on both the CT and MR images while rectum, bladder, femurs and other normal tissue volumes were delineated only on CT images. CT and MR scans were acquired over a 4 hour interval and, the MR scans were registered with the 3D planning CT scans, and consequently the IMRT dose distributions. Dosimeteric parameters and DVHs for the prostate, SV, and PB volumes based on CT‐outlined and MR‐outlined contours were compared. Furthermore, the areas for potential geometric target underdosing based on MR information were calculated to evaluate the quality of the CT‐based plans. Results: Analysis revealed that the prostate MR‐delineated volumes were smaller than the CT‐delineated volumes by 10%–40%. The SV‐MR‐delineated volumes were larger than the SV‐CT‐delineated volumes by 25%–100% and, PB‐volumes in CT and MR varied up to +/−50%. The prostate mean‐dose for CT‐ and MR‐based volumes was within 3%. However, in 4 patients, the minimum‐dose to 4–10% of the MR prostate‐volume was lower by up to 15% when compared to the CT plan. In 3 of these patients, there was an anterior movement of the prostate because of rectal expansion. The SV‐CT‐volumes mean‐ and minimum‐dose were lower than the MR‐volumes by a maximum of 5%. The PB‐CT‐volume mean‐dose was higher than the PB‐MR‐volume by a maximum of 10%. Conclusion: The quality of the dose delivery can be improved by identifying patient‐specific margins based on the CT and MR imaging data thereby potentially allowing further gain in the therapeutic ratio for prostate cancer.
SU‐FF‐J‐101: Optimized Field Shaping Strategy for Image Guided Radiation Therapy Treatments
Authors: Cormack R , Court L , D'Amico A
Published: July 2006

Purpose: Reducing normal tissue irradiation is one goal of image guided external beam radiation therapy treatments. One approach is to adjust the patient position based on daily imaging so that the target is localized and margins used for setup uncertainty can be reduced. The field shapes are generally determined at the time of simulation based on expansions of the clinical target volume. This work studies whether there are alternative field shaping strategies to reduce the volume of normal tissue in the field given that daily image guided position adjustments will be made. Method and Materials: Daily port films were acquired during prostate treatments that used an endorectal balloon for both immobilization and localization of the prostate. The contour of the balloon on lateral films was used to represent the shape of a sensitive structure to be shielded from treatment. The effect of static optimized, static conformal, and adaptive blocking strategies on the area of balloon in the treatment field was studied assuming that a daily image guided shift was allowed before treatment. Results: The daily port films of 9 patients were analyzed. The endorectal balloon allows localization of the anterior rectal wall and indicates a variation in shape from day to day. For all patients there are optimal field shapes that include less balloon area in the field than a conformal field shape. Relative to the optimal field shape, conformal, average, and running average field shapes involved 63%, 20% and 22% more balloon in the field. Conclusion: For targets of sensitive structures that have interfraction shape changes, image guidance enables non‐conformal field shapes to provide better shielding than conformal fields. This blocking strategy may improve treatments without daily field shape adjustments.
SU‐FF‐J‐103: Performance of the Nucletron Simulix Evolution Flat‐Plate Imaging System and CBCT
Authors: Goulet P , Béliveau‐Nadeau D
Published: July 2006

Purpose: To characterize the performance of the flat‐plate imaging system available on a commercial radiotherapy simulator, to describe some of it's clinical limitations and to present preliminary results when used in cone‐beam CT mode. Method and Materials: Measurements were performed on the Nucletron Simulix Evolution simulator installed at our institution. The Simulix Evolution is equipped with a 40 cm × 40 cm flat‐panel detector. Low contrast measurements were performed with a CDRAD contrast‐detail phantom at the center of varying‐thickness slabs of water‐equivalent material. Cone‐beam CT (CBCT) images were obtained with the AAPM CT and with a 20‐cm diameter water phantoms. Results: The Simulix Evolution performs digital fluoroscopy at a fixed rate of 3 frames/sec. There is no digital radiography mode available currently. In fluoroscopy, two automatic brightness settings are available (ABS1 and ABS2). Entrance skin exposure levels at time of acceptance exceeded 15 R/min. Contrast‐detail measurements with 10‐cm and 20‐cm water‐equivalent phantoms demonstrated that the grid has limited clinical utility since the large air gap present reduces the impact of scatter. Removing the grid reduces patient exposure by a factor of 2. Note that the grid is not used in CBCT. Evaluation of the CBCT mode demonstrated that the system does not currently meet the HU uniformity target of +/− 75 HU for water set by the manufacturer. Ring artifacts are also present and compromise clinical utility. Images and clinical examples will be presented. Conclusion: Lack of a digital radiography mode limits the clinical utility of the Simulix Evolution. With optimization, the system can be operated with clinically‐acceptable fluoroscopy images at exposures below 5 R/min. Cone‐beam CT image quality is currently compromised by the presence of ring artifacts and by poor HU uniformity and low‐contrast discrimination.
SU‐FF‐J‐105: Placement of Localization Wires for Breast Surgery Guided by the Positron Emission Mammography
Authors: Beylin D , Stepanov P , Narayanan D , Anashkin E , Yarnall S , Zavarzin V
Published: July 2006

Objectives: Positron Emission Mammography (PEM) with F18‐FDG has been shown to be useful in breast cancer diagnosis and characterization [1]. The ability of PEM to determine breast cancer extent makes it potentially useful for planning surgical excisions. Breast surgery is commonly directed with the use of the hook wires placed under radiological guidance. We sought to develop a method to perform PEM‐guided wire localization procedures with image confirmation, which could be used for directing breast conserving surgery. Materials and Methods: Five‐millimeter lesions with physiologic concentration (0.1uCi/cc) of F18‐FDG were implanted into a breast tissue phantom at various locations. Ten‐minute PEM scans were performed, and the lesions were located in three dimensions using the PEM tomographic images. 15cm long, 24Ga hollow hook wires filled with a small amount of the F18‐FDG (0.1uCi/cm) were guided toward the lesions based on the PEM spatial coordinates. Post‐placement scans were acquired to confirm correct placement of the wires relative to the lesion. Placement‐accuracy was determined by dissecting the phantom along the wire, and verifying the lesion position relative to the localization wire hook. Results: All ten attempts at lesion localization were successful. Conclusions: An accurate and practical method of wire localization of breast lesions identified on PEM images was developed and tested on phantoms. Disclosure: Authors are employees of the company sponsoring the study.
SU‐FF‐J‐106: Predicted Tumor Motion Ranges During Adaptive SBRT
Authors: Dieterich S
Published: July 2006

Purpose: The purpose of this study is to develop a method to estimate the patient‐specific clinical accuracy and safety of adaptive SBRT after treatment delivery. Respiratory motion phantoms rely on continuous knowledge of the in‐vivo tumor position, which does not exist for the time needed for adaptive SBRT delivery. A first approximation is to study the tumor motion range as predicted by the adaptive SBRT delivery system during treatment and compare the motion range to published literature. Method and Materials: The predicted tumor motion was analyzed for 48 patients with 52 tumors. The database contains 28 tumors in the lung, 4 hilar tumors, 9 pancreases, 2 each in liver and chestwall, and one each renal, retroperineal, and internal mammary nodes. The mean and variance of tumor motion amplitudes were calculated using MatLab. Normal distributions were fitted to a respiratory extrema amplitude histogram. Results: The motion ranges for most sites agreed with the literature. The largest motion amplitudes of 12 to 28 mm were observed in the lower lung. Fluctuations about the average motion ranges are mostly small. 50% of tumors in the upper and middle lung and hilum could have been treated without adaptive SRS using 2 mm PTV margin. This would have reduced the risk to patients caused by fiducial placement. The extent of motion in the pancreas is smaller than cited in the literature. Conclusion: We analyzed tumor respiratory motion ranges under free breathing during adaptive SBRT. Certain patient subgroups could have been treated with non‐adaptive SBRT and therefore with reduced risk from fiducial placement, if the extent of the tumor motion could have been determined before treatment by 4D imaging. Tracking problems due to sub‐optimal fiducial placement may lead to problems in tumor motion prediction, which can be discovered by our analysis method after the first treatment.
SU‐FF‐J‐107: Predicting Dosimetric Errors From Real‐Time Tumor Tracking Using a Treatment Couch During IMRT Delivery
Authors: D'Souza W , Nazareth D , Naqvi S , McAvoy T
Published: July 2006

Purpose: To determine the dosimetric errors resulting from real‐time tumor tracking using a treatment couch‐based feedback control system. Methods: The couch dynamics and controller were modeled as a closed‐loop feedback control system. A real‐time tumor trajectory was input into the control system and the resulting output residual tumor motion trajectory was determined as a function of varying parameters (time constants and dead times) used to describe the system dynamics. The residual motion trajectory served as the input for a Monte Carlo superposition dose calculation algorithm that sampled the position of the tumor according to the displacement distribution from the residual tumor trajectory. We considered tumor trajectory data from a lung tumor case and applied it to a 7‐field IMRT plan in which the prescribed dose was 66 Gy. The motion influenced dose distributions were compared with the “static” and tumor motion without feedback control cases. Results: The fraction of tumor displacements greater than 3 mm for uncontrolled motion was 0.72, 0.32 for the case in which the couch dynamics were described by two equal time constants of 0.135 s and a dead time of 0.27 s and the controller described by a time constant of 0.20 s. For even smaller time constants and dead times, this fraction reduced to < 0.05. The volume of the GTV receiving the prescription dose was 99% for the static case, 68.9% when uncontrolled tumor motion is present and 98.3% when feedback control is employed and the fraction of tumor displacements > 3 mm is 0.32. Conclusion: Our results show that real‐time tumor tracking may be achieved using a treatment couch with < 2% degradation in tumor volumes receiving the prescribed dose. The motion‐influenced dose distributions are more dependent on the distribution of residual tumor displacements than on the maximum instantaneous displacement.
SU‐FF‐J‐108: Predicting Respiratory Waveform of 4D CT Patients Using Moving Least Square Method
Authors: Zhang L , Bai E , Lu W , Parikh P , Hubenschmidt J , Bradley J , Low D
Published: July 2006

Purpose: The purpose of this study was to predict patient respiratory waveform precisely and quickly using a Moving Least Square method. The external respiratory waveform may have a strong correlation with tumor motion in lung and upper abdomen. Prediction is important because tumor may move significantly during the finite time between monitored position change and beam adjustment. Method and Materials: An adaptive linear filter based on the Moving Least Square (MLS) algorithm was used to predict the respiratory waveform signals for six 4D CT patients. The respiratory waveform was acquired with a bellows system at a sampling frequency of 100 Hz or a sampling period of 10 ms. To predict one datum in the future (10 samples or 100 ms away), 10 s history data (1000 samples) were fed to the linear filter to calculate its coefficients with the MLS algorithm. The output of the linear filter was the predicted value. The predicted values were compared to the real data recorded. We also calculated the Root Mean Square Error (RMSE) of the prediction for each patient and normalized it with the average waveform amplitude. The accuracy of MLS method was compared with that of Wiener‐Hopf method in the normalized RMSE values. Results: For a 100 ms prediction interval, the average prediction RMSE for the 6 patients was 2.1%, with a standard derivation of 0.7%. The computation time for predicting one datum with MLS method was 0.5 ms, which is far less than the sampling period (10 ms). This MLS method shows smaller normalized RMSEs than the Wiener‐Hopf method for all 6 patients. Conclusion: The prediction of patient respiratory waveform using MLS method is both accurate and fast. Real time prediction of respiratory waveform and tumor motion is possible.
SU‐FF‐J‐110: Quantification of Image Alignment Differences for Tomotherapy Prostate Patients
Authors: Rivest R , Riauka T , Murtha A , Fallone B
Published: July 2006

Purpose: Accurate registration of MVCT to planning kVCT images is required for daily positioning of patients treated on helical tomotherapy. Due to prostate motion, overlaying the prostate, overlaying the pelvic bones, and finding the best overall image overlap can result in three different alignments for prostate treatments. The objective is to quantify these alignment differences for research patients treated on the TomoTherapy Hi*Art system and determine the suitability of using the latter two alignments to correct for inter‐fraction prostate motion. Method and Materials: Daily MVCT images have been retrospectively registered to planning images using three different mutual information based algorithms. Each algorithm is designed to produce one of the alignments and does so by using selective planning CT pixels when calculating the mutual information parameter. Depending on the alignment, only prostate pixels (including small margin), bony pixels, or all image pixels are used. To reduce registration uncertainty and eliminate gross miss‐registrations, a multi‐start optimization procedure with random initial alignments was employed. Offsets between each alignment were calculated for ten prostate patients, each typically having twenty‐five fractions. Results: Maximum offsets of 6.7mm and 4.9° were observed between the pelvic bone and overall image alignments. Mean translational and rotational deviations were 2.3mm and 1.5° with standard deviations of 1.4mm and 0.7°. Registration resulting in pelvic bone overlap better accounts for prostate motion than using the entire image for alignment purposes. Maximum, mean and standard deviation translational offsets between overlaying the prostate and overlaying pelvic bones are 7.8mm, 2.8mm, and 1.6mm. The respective values for prostate and overall image overlap are 9.3mm, 4.4mm, and 2.1mm. Conclusion: Clinically significant offsets between the three image alignments have been observed for tomotherapy prostate patients. Mutual information registration using only bone pixels better accounts for inter‐fraction prostate motion than using all image pixels.
SU‐FF‐J‐111: Quantification of Patient Specific Target Motion in the Presence of Limited Data Through Population Models
Authors: Brock K , Moseley J , Jaffray D
Published: July 2006

Purpose: Integrate 2D cine imaging into 3D patient population models of physiological motion for patient specific quantification of deformation. Method and Materials: Inhale and exhale CT images of the liver were obtained for 5 patients under an REB approved protocol. A finite element model was constructed from the exhale contours of one patients liver and deformed into the exhale then inhale position of each patient, providing a common model of the 3D deformation of each patient's respiration, using Morfeus, a finite element‐based deformable registration algorithm. Average deformation maps were computed for all combinations of 4 patients. Simulated 2D coronal cine images were generated from the exhale and inhale data of each patient. Narrow 2D channels were used to obtain image intensity data at the dome of the liver and the inferior tip of the liver on the exhale image and at the same location on the inhale image. A least squares fit was performed to align the image intensities in the superior‐inferior direction, providing an estimate of the coronal motion of the liver. The motion from the channel was applied to the population deformation model, providing patient specific refinement by scaling the motion of the population model by the ratio of the motion at the navigator for the specific patient to the motion at the navigator for the population model. Results: The average superior‐inferior displacement difference over the liver volume between the 3D deformation model via Morfeus and the patient‐refined population model was −0.12 cm (SD 0.15 cm). The average standard deviation across the five patients was 0.38 cm. Conclusion: Population models of volumetric organ deformation due to physiological motion can be used to predict patient specific motion when limited data is available, such as with 2D cine data. Conflict of Interest: Research supported in part by Varian Medical Systems.
SU‐FF‐J‐112: Radiobiologically Motivated Margin Prescriptions
Authors: Deasy J
Published: July 2006

Purpose: The celebrated formula of Van Herck et al. prescribes a target margin of at least 2.5 times systematic standard error plus 0.7 times random standard setup error, in order “to ensure a minimum dose to the CTV of 95% for 90% of the patients” (van Herk et al., IJROBP (2000) 47:1121–1135). However, minimum dose is an imprecise and biased surrogate for local control. The question we address is whether this margin formula is consistent with radiobiological principles. Method and Materials: To address this issue we make the connection between outcomes and dose distributions using radiobiological modeling and computer simulations of tumor control probability under varying conditions of systematic and random (fraction to fraction) setup errors. We consider an idealized case of a circular cross‐section tumor which is shifted systematically and randomly with respect to a fixed dose distribution which falls off rapidly within the plane. Tumor control probability (TCP) is simulated using the Webb‐Nahum model. Cohorts of patient treatments were simulated with varying target margins for varying conditions of systematic and random setup shifts. The threshold for a clinically important improvement in TCP was set at 0.01 (absolute). Results: The radiobiological simulations consistently indicate that the dose‐based Van Herk formula produces margins which are unnecessarily large. In our simulations, margins equal to 1.2 times the setup error + 0.7 times the random error consistently produced TCP values which were within 0.01 of the large margin limit. Conclusion: Although the details of the margin prescription depend on the characteristics of the test cases, radiobiological, as opposed to purely dosimetric principles, support using tighter margins around gross tumor volumes than those indicated by the Van Herk equation. This research was partially supported by NIH grant CA85181.