bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐E‐J‐58: Lens Exposure During Percutaneous Trigeminal Nerve Radiofrequency Rhizotomy Guided by Computed Tomography with Three‐Dimensional Image Reconstruction
Authors: Yeh M , Tsai Y , Liu H , Yang J , Weng H
Published: July 2013

Purpose: The eye lens is one of the most radiosensitive tissues; it is an issue to estimate the lens dose during the percutaneous trigeminal nerve radiofrequency rhizotomy guided by computed tomography. The purpose of this abstract was to prove the CTDI(computed tomography dose index) peripheral (CTDIp) could be provided a reference value for the eye lens dose. Methods: There are two methods for estimate the eye lens dose in this study. The first is CTDIp calculated and second is ImPACT software which is common used in CT dose calculation. We Collected the CTDIv in dose report which create from CT scanner automatically. Ten patients who performed the trigeminal neuralgia underwent computed tomography (CT)‐guided percutaneous radiofrequency trigeminal rhizotomy was included. In order to find out the factor of CTDIv and CTDIp, we measured the CTDIv and CTDIp using ion‐chamber and 16‐cm diameter CTDI dose phantom. On the other hand, we also apply the ImPACT software to estimate the eye lens dose, and compare the two values, CTDIp and the eye lens dose from ImPACT. Results: The factor of CTDIv to CTDIp is 1.02. The average CTDIp in 10 patients is 116.65 mGy, and the average lens dose from ImPACT is 124 mGy. It is only 6% error. Conclusion: CTDIp can be provided a reference value for the eye lens dose. And the CTDIv can display in modern CT scanner in time, the radiologist can monitor the radiation dose during the exam. However,how to reduce the lens dose in this process would be next important issue foe us.
SU‐E‐J‐60: In Vivo Management of Suspectibility‐Induced Distortions for Intracranial MRI Guided Therapies at 3 Tesla
Authors: Hwang K , Stefan W , Stafford R
Published: July 2013

Purpose: To assess, in vivo, subject‐dependent susceptibility‐induced shifts along the frequency encode direction, and develop a strategy for correcting such distortions for applications in need of high spatial accuracy. Methods: Distortion maps of the brain were created for 5 volunteers using data from a 16‐echo multiple gradient echo (MFGRE) sequence, acquired on a 3 Tesla scanner for R2* mapping applications. Both an R2* map and a proton resonance frequency (PRF) map was calculated using a single peak autoregressive moving average (ARMA) model, and aliased frequencies were unwrapped with a compressed sensing type non‐convex optimization method, using the assumption that sharp changes in the field are sparse. A map of susceptibility induced shifts is then calculated with knowledge of the frequency gradient, which scales with field of view and receiver bandwidth. The need for distortion correction is then determined either visually or automatically based on a threshold. If distortion correction is necessary, a smoothed version of the distortion map can be corrected to subvoxel resolution using conventional image deformation techniques. Results: Due to the high frequency encoding gradient of MFGRE sequences (±125 kHz over 25.6 cm FOV in our protocol), susceptibility‐induced distortions within the brain were less than 0.7 mm. However, the distortion map and correction method was applied to sequences with lower bandwidth in the same slice locations. Most distortions occurred in high susceptibility areas near the sinuses, with the optic chiasm shifting by approximately one pixel on low bandwidth, high resolution images. Conclusion: The creation of field and distortion maps for assessment and correction of distortion was performed using data from an MFGRE sequence. The additional R2* information from the same sequence provides an added benefit over dedicated field mapping sequences. With proper design of imaging protocols, distortions in the brain can be adequately contained for therapy planning purposes. Ken‐Pin Hwang is an employee of General Electric Healthcare.
SU‐E‐J‐62: Theoretical Investigation of Ultra‐WideBand (UWB) Penetration Through Biological Tissues
Authors: Han‐Oh S , Oh E
Published: July 2013

Purpose: The UWB is a radio‐frequency electromagnetic pulse with width around nanosecond encompassing 3.1–10.6 GHz frequencies. The UWB is currently used in remote sensing as through‐the‐wall imaging technology. To develop this technology into through‐the‐body tumor‐detection technology, first, we theoretically investigate whether the UWB pulses can penetrate through human tissues such as skin, muscle, bone, fat, and lung. Specifically we want to calculate which frequencies can penetrate deeper through human tissues. Methods: We theoretically relate the complex permittivity with the complex index of refraction and obtain the reflection and transmission coefficients for skin, muscle, bone, fat, and lung. We use an experimentally‐measured complex permittivity from the published source1 as input to our numerical model. We calculate 1/e penetration depth, which is equivalent to 36.7% of initial radar power, for each frequency, from air to each tissue as an indicator of penetration capability of the UWB. Results: Our results show that lower UWB frequency (3 GHz) is better at penetrating through the human tissues than higher frequency (10 GHz). We find 1/e penetration depth for human skin, muscle, fat, bone, and lung to be 4.45, 4.67, 0.49, 2.61, and 2.38 cm for 3 GHz, respectively. At 10 GHz, the 1/e penetration depths are reduced significantly to 0.17, 0.13, 0.33, 0.21, 0.19 cm, respectively. This Result indicates that it is better to design a UWB radar system with higher power at lower frequencies. Conclusion: We find that lower frequencies of the UWB can penetrate deeper into human tissues. This Result will be used to design a future experiment for further investigation of the UWB radar as a new modality for lung‐tumor detection.1. Gabriel et al. The dielectric properties of biological tissues: I. Literature survey Phys. Med. Biol. 41(11) 2231‐2249
SU‐E‐J‐66: Effects of Noise in 4D‐CT On Deformable Image Registration and Derived Ventilation Data
Authors: Latifi K , Huang T , Feygelman V , Budzevich M , Stevens C , Dilling T , Moros E , Van Elmpt W , Dekker A , Zhang G
Published: July 2013

Purpose: Deformable image registration (DIR) and 4D‐CT have been proposed to generate ventilation images. Quantum noise is common in CT images. This study focuses on the effects of noise in 4D‐CT on DIR and the derived ventilation data. Methods: Diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B‐Spline were used to register the end‐inspiration phase to the end‐expiration phase of 6 4D‐CT sets with landmarks delineated on different phases, called point‐validated pixel‐based breathing thorax models (POPI). Landmarks at expiration were mapped to inspiration using DIR deformation matrices (DIRDM) for each POPI model. Target registration errors (TRE) were calculated as the distances between the delineated and the mapped landmarks. Gaussian noise with different standard deviations (SD) of the amplitude was added to the POPI models to simulate different levels of quantum noise. Ventilation estimations were performed by calculating the volume change geometrically, based on the DIRDM. Ventilation images with different CT noise levels were compared using Dice similarity coefficient (DSC). Results: The root mean square (RMS) values of the landmark TRE over the 6 POPI models for the 4 DIR algorithms were stable when the noise level was below SD=150 Hounsfield Units (HU), and increased with the noise level. The most accurate DIR was DD with mean RMS of 1.5±0.5 and 1.8+−0.5 mm at the added noise SD=0 and 200 HU respectively. The DSC values between the ventilation images with and without added noise decreased with the noise level. The most consistent DIR was DM with mean DSC=0.89+− 0.01 and 0.66+−0.02 for the top 50% ventilation volumes with the added noise of 0 and 30, 0 and 200 HU respectively. Conclusion: While the landmark TRE was stable with noise level for low noise, the differences between ventilation images increased, indicating that 4D‐CT based ventilation imaging is sensitive to image noise. This work was partially supported by a grant from the Varian Medical Systems, Inc.
SU‐E‐J‐77: A Comparison of Deformable Image Registration Algorithms as They Apply to 4DCT Lung Images
Authors: Loughery B , Song W
Published: July 2013

Purpose: To evaluate four intensity‐based deformable image registration (DIR) algorithms as they apply to 4DCT lung images. Methods: The four algorithms evaluated were one‐and two‐way optical flow, and one‐and two‐way Demons. All algorithms were coded in‐house using MATLAB. 4DCT datasets of ten patients were selected randomly from clinic for the study. Each code was used to register the end‐of‐exhalation (50% phase) image to all other nine phases. This was repeated for all ten patients. The relative performance of each algorithm was evaluated by calculating the sum of square difference (SSD) of all pixels between the moving and reference images, during each iteration of registration. Elapsed registration duration (in CPU time) was recorded as well. Results: In general, the Demons algorithm operated much quicker than the optical flow codes. The two‐way algorithms were often better than the one‐way algorithms, in terms of SSD. Two‐way algorithms produced the best final SSD in 83% of registrations. Thus, our results suggest that the optimal choice is the two‐way Demons algorithm. The two‐way optical flow algorithm also performed reasonably well for small deformations, but took at least twice the time compared to the two‐way Demons. In terms of the final registrations, all algorithms resulted in visually reasonable image registrations. Therefore, the differences were in the duration of registration and SSD. Conclusion: The two‐way Demons algorithm proved to be the best quantitative method for performing DIR on 4DCT of lung anatomy. All two‐way algorithms were generally slower than the one‐way algorithms, but their accuracy increased in terms of SSD. Therefore, we suggest using the two‐way Demons for 4D planning of lung cancer radiotherapy.
SU‐E‐J‐81: A Contour‐Guided Deformable Image Registration Scheme for Organ Surface Deformation
Authors: Gu X , Pompos A , Zhong Z , Wang J , Guo X , Jia X , Dong B , Jiang S , Solberg T
Published: July 2013

Purpose: Accurate deformation vector field (DVF) acting on organ surface is vital for surface dose summation of deformable cavitary organs in adaptive radiotherapy. The aim of this study is to develop a contour‐guided deformable image registration (DIR) scheme to establish accurate DVF on an organ surface. Methods: A critical cavitary organ, for example bladder, is delineated by a clinician on fraction 1 and fraction 2 CT images used for cervical high‐dose rate (HDR) brachytherapy planning. Both CT sets are preprocessed by creating binary images by setting one inside of the organ under evaluation and zero outside. The organ surface is subsequently extracted and discretized with a triangular mesh. The DVF on designed vertices is estimated through an inverse‐consistent demons‐DIR between the paired binary images. Due to shortcomings in DIR algorithms, the DVF derived through DIR always has limited accuracy. To increase the DVF accuracy, an iterative closest point (ICP) algorithm is adopted in this study to match corresponding vertices of the paired surface meshes through an affine registration. The accuracy of the resulting registration is then evaluated. Results: Using contours drawn by a clinician as ground truth we evaluated DVF accuracy through organ surface‐to‐surface distance (SSD), defined as the shortest Euclidean distance between the vertices on the target and the deformed surfaces. In our evaluation case, the maximum SSD is 27.8mm before registration, decreases to 15.0mm following demons registration, and 8.33mm after further affine alignment. The 95 percentile SSD decreases from 23.9mm to 10.8mm and to 3.8mm, respectively. Conclusion: The proposed contour‐guided DIR scheme substantially improves the accuracy of DVF on organ surfaces. Results from the evaluation case demonstrate that the proposed contour‐guided DIR scheme can provide accurate DVF making it a useful approach to be applied in adaptive radiotherapy.
SU‐E‐J‐83: Evaluation of Landmark‐Assisted Deformable Image Registration Technique On Inter‐and Intra‐Patient Head and Neck CT Imaging
Authors: Wang Y , Folkert M , Saleh Z , Apte A , Sharp G , Lee N , Deasy J
Published: July 2013

Purpose: Accurate image registration in patients with Head and Neck (HN) cancer is critical for assessment of treatment response, and must be rigorously validated. To evaluate deformable image registration (DIR) with our previously developed DIR techniques on HN PET‐CT scans, we incorporated distance discordance metric (DDM), a novel method for quantification of local registration uncertainty, as well as traditional quantitative and qualitative measurements. Methods: For each CT image series, six landmark (LM) points relevant for patients with HN cancer were placed, including the inferior‐most extent of the styloid processes, the lateral‐and inferior‐most aspects of the hyoid bone, and the superior central face of the sternum. These points were used either to assist or assess registration.Inter‐patient HN CT image registration for 10 patients was performed using standard intensity‐based B‐Spline and LM‐assisted B‐Spline DIR techniques. Intra‐patient DIR of the same patients was performed using pre‐and post‐radiation therapy image sets of the same patient.DDM metrics were obtained for inter‐patient DIR results and compared to traditional qualitative and quantitative methods including whole image mean square error (MSE), relative landmark distances between scans, and visual judgment by physicians. Results: Registration results were improved with the addition of landmarks on bony structures relevant to HN cancer. While MSE only provides a general estimate of the overall image registration error, the novel DDM metric shows the registration uncertainties for each pixel on the fixed image, allowing for localization of error. Based on DDM, DIR uncertainty was significantly reduced (>50% in region of interest) by LM‐assisted DIR techniques on inter‐patient registration. Relative LM distance was reduced as well for both intra‐and inter‐patient DIR (<1mm). Conclusion: Evaluation of DIR results with novel and traditional methods demonstrates the LM‐assisted DIR approach achieves better registration results on both inter‐and intra‐patient DIR.
SU‐E‐J‐87: Improvements to the Computational Environment for Radiotherapy Research
Authors: Apte Aditya P , Wang Ya , Deasy Joseph O
Published: July 2013

Purpose: To present new tools in CERR to analyze image registration, as well as other software updates and additions. Method: Two new features were added to CERR to evaluate the accuracy of image registration: (1) gamma‐based metric for comparing base and registered scans: All scan quantitative scan‐comparison methods rely on using image intensities. This means, these methods cannot quantify spatial misalignment between base and registered images. The gamma metric (similar to the ones used for dose comparisons) was modified for image‐comparisons. (2) a color‐coded mirror grid, based on mutual information or mean‐square‐error metrics: Color‐coding blocks within the grid help users focus on the well vs. poorly registered areas. In addition to these features, other noteworthy additions to CERR include (1) the ability to use colormaps for scans. This is an improvement over grayscale‐only scan display and is particularly useful for images produced in nuclear medicine. (2) the ability to change scan‐windowing by dragging the mouse along a view, similar to PACS viewers. Interactive windowing is particularly useful while delineating structures. (3) The ability to import Image‐annotations. The DICOM modality PR is used to store graphical and text annotations for images. These are widely used in diagnostic radiology and could be extracted from PACS. CERR can now imports these objects and store them under the planC{indexS.GSPS} element. Results: A 3‐d gamma metric was implemented in CERR for comparing image registration. It was evaluated on prostate cancer registration with criteria of 4mm and 50HU. The fused images were compared against the Gamma metric map. It was observed that the regions that matched up poorly had gamma value above 1. Conclusion: A novel gamma based approach to compare image registration allows for specification of acceptance criteria. Features added to CERR increase its utility in ART, registration, segmentation and collaboration.
SU‐E‐J‐89: GammaPlan MR to CT Image Registration Errors: Implications for Extend and Preplanned Treatments
Authors: Sudhyadhom A , Perez‐Andujar A , Ma L , Barani I
Published: July 2013

Purpose: To compare the spatial errors in coordinate localization created by MR‐based stereotaxia in GammaPlan for two different Methods: direct stereotactic MR localization and MR co‐registration with a stereotactic CT. Methods: Seven patients underwent Gamma Knife SRS and were scanned with our clinical stereotactic MR and stereotactic CT protocols. In GammaPlan, the stereotactic coordinate system for each MR and CT was defined using the fiducials of the localizer. Separately, each MR was co‐registered (by the patient's anatomy) to the stereotactic CT using GammaPlan's automated co‐registration algorithm. A rigid transformation relationship was determined between the direct stereotactic MR (stereoMR) and the MR co‐registered to the stereotactic CT (coregMR). Spatial errors in coordinate definition between these two methods were calculated at the center of stereotactic space and at the centroid of the target. A total of seventeen MR scans were analyzed by this method including both axial and coronal acquisitions. Results: Mean errors in fiducial definition were found to be 0.6±0.3mm and 0.7±0.3mm for CT and MR, respectively. At the center of stereotactic space, the mean magnitude error in coordinate localization between stereoMR and coregMR was found to be 1.6±0.6mm. At the centroid of the target, the mean magnitude error was found to be 1.5±0.7mm. These results were statistically significant compared to the errors in fiducial definition (p<0.002 for both). No statistically significant systematic errors along any axes were found in comparing stereoMR and coregMR coordinate localizations. Conclusion: Statistically significant differences were found in coordinate localizations between the two methods. These errors may primarily be due to sub‐optimal co‐registrations when using GammaPlan's automated registration algorithm. The magnitude of these errors may be clinically significant as they are on average greater than 1.5mm. Errors in co‐registration are important for preplanned SRS and Extend SRT treatments where direct stereotactic MR localization is not utilized.
SU‐E‐J‐90: MRI‐Guided Targeted Prostate Cancer Radiation Therapy: Imaging, Diagnosis, and Image Co‐Registration
Authors: Bao A , Zheng Y , Ware S , Colussi V , Ellis R , Wessels B
Published: July 2013

Purpose: This study investigates using MR imaging for prostate cancer targeted radiation therapy, including using MR imaging to localize tumor in prostate for focused radiation therapy, co‐registering MR images to CT or ultrasound images to guide or evaluate cancer radiation therapy, and determining tumor stage and its invasion to locoregional lymph nodes. Methods: Either endorectal (ER) or body RF coil has been used to acquire MR images. To determine the localization and volume of the prostate tumor from the MR images and to understand their correlation with the pathology slices, both pre‐surgery ER coil MR imaging and post‐surgery multi‐slice pathological analysis of the surgically excised tumor were studied (n=8). To avoid prostate dislocation caused by ER coil, multiple MR imaging protocols using the body coil were implemented to depict the structure of prostate and its surrounding tissues, pelvic lymph nodes, and obtain dynamics of contrast enhancement (n=37). MIM™ software was used to co‐register MR images to CT or ultrasound images for guiding treatment planning and evaluating prostate cancer brachytherapy. Results: The MR diagnosed tumor was proven by prostate pathology, which shows the rationale of using MRI guidance for focused treatment of prostate cancer to decrease toxicity and improve tumor control. It has been demonstrated that the capability of tumor localization and volume determination based on MR images are associated with tumor cell density. CT/MRI and ultrasound/MRI co‐registration provides excellent prostate micro‐structure, and surrounding tissue information for improved treatment planning, as well as post‐treatment evaluation. By using multi‐MR image synergistic co‐registration, T2‐weighted MR images can be accurately matched to CT image with <2 mm alignment error. Conclusion: MR image guidance is excellent for targeted prostate cancer radiation therapy, and it has been successfully used in our clinical practice.
SU‐E‐J‐91: Developing Platform for Online Registration Using Digital Tomosynthesis
Authors: Yan H , Yin F
Published: July 2013

Purpose: To meet the needs of fast registration of on‐board digital tomosynthesis (DTS), a platform was developed in assisting the task of intensive DTS reconstruction and analysis. Methods: this platform consists of four parts: pre‐processing, reconstruction, post‐processing, and registration. These four software parts were integrated into one platform and developed using Java programming language. The pre‐processing part handles the procedure of cone‐beam extracting and projection image filtering. The reconstruction part provides the function of FDK‐based cone‐beam reconstruction. Three types of reconstruction algorithms (CPU‐based, OpenGL‐based, and CUDA‐based) were provided to facilitate the processing of DTS in various application scenarios. The post‐processing part handles the procedure of image realignment, DICOM reformatting, and XML file setup for image registration. The image registration part provides both manual and automatic methods, many tools are provided to assist this procedure. Results: several clinical patient cases were tested on this platform. The need of current study which requires fast and intensive reconstruction of DTS from cone‐beam projection is met. Conclusion: this platform demonstrates a clinical available on‐board DTS registration software in assisting current image guided radiotherapy. In the future, additional functions will be added to this platform in order to handle more complex clinical cases.
SU‐E‐J‐97: Recursive Contour Propagation Using Feature‐Based Deformable Registration for Lung Cancer
Authors: Yang Y , Xie Y , Li R , Yu S , An M , Wu S
Published: July 2013

Purpose: Accurate target delineation of CT image is a critical step in treatment planning of radiotherapy.This paper describes a novel strategy of automatic recursive registration for lung segmentation in CT images. Methods: The proposed method started with a manual‐delineated contour in one slice, then by the means of feature‐based deformable registration, its correspondence in other slices could be recursively obtained.There are three algorithms would be implemented:the scale‐invariant feature transform (SIFT),used to automatically detect the feature control point pairs; thin‐plate spline (TPS),used to register a structure characterized by the control points;active contour(Snake), used to refine and correct the autocontour. 5 pulmonary cancer cases would be tested in,each comprising of three target delineation. Results: The use of the proposed recursive feature‐based registration method in the clinical cases leads to an improvement in the manual segmentation and intensity‐based contrast performance in the pulmonary CT planning images, obtaining a mean growth of overlap volume index=8% nearly.In addition,the time consumption decreased by nearly 98%,which largely improve the delineated efficiency of physicians. Conclusion: The application of the feature‐based deformable registration method in the recursive contour propagation during pulmonary cancer treatment planning, greatly improves the delineated efficiency,well agreed with a physician's manual contours. Furthermore, this method could be available in other cases, such as head‐neck, rectum, 3 dimension tumour tracking etc. This work is supported in part by grants from National Natural Science Foundation of China (NSFC: 81171402), NSFC Joint Research Fund for Overseas Research Chinese, Hong Kong and Macao Young Scholars (30928030), National Basic Research Program 973 (2010CB732606) from Ministry of Science and Technology of China, and Guangdong Innovative Research Team Program (No. 2011S013) of China.
SU‐E‐J‐98: Evaluating Commercial Orthopedic Metal Artifact Reduction Tool (OMAR) for CT Simulation in Radiotherapy for Head‐And‐Neck Cases
Authors: Kwon H , Kim J , Kim K , Chun Y , Park J , Ye S
Published: July 2013

Purpose: Many head‐and‐neck (H&N) patients have dental implants which can induce severe metal artifacts in CT images. This study assessed the performance and the effectiveness of the commercial orthopedic metal artifact reduction function (O‐MAR) for radiotherapy of H&N patients. Methods: 20 H&N images scanned by Philips Brilliance Big Bore CT were used for the assessment. After O‐MAR correction, the CT Hounsfield number and noise were measured and compared between the original and OMAR corrected images. Structures were contoured by a radiation oncologist on each of the two image sets. These contoured structure volumes on the original CT image were compared with those on O‐MAR corrected CT image by radiation therapy planning system. After volumetric‐modulated arc therapy (VMAT) planning was performed on the original CT image, this plan was translated and calculated on O‐MAR corrected images. Each DVH was compared and the gamma passing rate was calculated for comparing dose distributions. Results: CT Hounsfield numbers became closer to the reference values after O‐MAR correction. The standard deviations of the selected ROIs were reduced from 323.9 HU to 195.6 HU in air and from 43.5 HU to 24.7 HU in tissue. The volumes of contoured structure showed minute change. The difference between two DVH was less than 0.5%. The gamma pass rates of dose distributions calculated on the original and OMAR corrected images were higher than 99.9% in the case of closed‐mouth‐images but were about 80% in the case of opened‐mouth‐images when using 1mm and 1% distance‐to‐agreement criterion. Conclusion: The O‐MAR algorithm is suitable for the entire radiation treatment planning process by increasing the accuracy of CT Hounsfield numbers and reducing noises. Although there was no significant difference in dose distribution for most cases, the O‐MAR correction was shown to have an impact on opened‐mouth‐images.
SU‐E‐J‐101: Application of a Combination Method of Atlas‐Based and Model‐Based Auto‐Segmentation in Thorax‐And‐Abdomen Cancer
Authors: Jiang XQ , Jiang QF , Li T , Xiao JH , Zhong RM , Li GJ , Bai S
Published: July 2013

Purpose: Atlas‐based auto‐segmentation and model‐based auto‐segmentation were combined to improve accuracy of auto‐segmentation for thorax and abdomen cancer patient,herein referred to as A&M method. The purposes of this study were to evaluation the performance of A&M method and try to establish auto‐segmentation application protocol in clinical. Methods: Two major steps were performed in A&M method. Firstly, planning images and corresponding normal organ contours from 10 thorax‐and‐abdomen patients were selected as atlases input for ABAS. Auto‐segmentation was performed on planning images of 20 thorax‐and‐abdomen new patients using ABAS software. Secondly, results from ABAS were adjusted using MBS software based on characteristic values of each organ. All operations were automatic running by using pre‐edited scripting programs. Physicians reviewed the final A&M results and made necessary modifications. The Dice Similarity Coefficient (DSC), that between contours generated by A&M auto‐segmentation method and modified contours, was used to evaluate accuracy of A&M auto‐segmentation. It also was used to compare with the DSC between contours generated by ABAS only and physicians modified results based on A&M contours. Results: Compared to physicians modified results based on A&M contours, the mean DSC to right Lung, left Lung, Heart, Liver, right Kidney, left Kidney, Spleen, with respect to the A&M auto‐segmentation and the ABAS auto‐segmentation were 0.95 vs. 0.96 (p=0.11); 0.95 vs. 0.96 (p=0.42); 0.93 vs. 0.91 (p<0.01); 0.95 vs. 0.93 (p<0.001);and 0.93 vs. 0.88 (p<0.01);0.95 vs. 0.89 (p<0.01), 0.86 vs. 0.80 (p<0.001), respectively. Auto‐segmentation of all OARs using A&M method took about 12 minutes per patient in total, including ABAS 10 minutes and MBS 2 minutes. Conclusion: Our results demonstrated the AM method can significantly improve the results from ABAS only for most normal organs in thorax and abdomen cancer patients. And the AM method is more convention and accurate than MBS method only in clinical applications.
SU‐E‐J‐107: SEMAC MRI Metal Artifact Reduction Technique for Radiotherapy Planning Using CT/MRI Fusion
Authors: Panek R , Scurr E , Bernard J , Revell P , Koh D , Hansen V , Saran F , Leach M , Schmidt M
Published: July 2013

Purpose: To assess the utility of SEMAC MRI metal artifact reduction technique for radiotherapy (RT) planning using CT/MRI fusion in patients with spine stabilisation devices. Methods: Conventional MRI spin‐echo (SE) sequences used for spinal cord delineation and radiotherapy planning in the spine were compared with a prototype implementation of Slice Encoding for Metal Artifact Correction (SEMAC) (WARP works‐in‐progress software package, Siemens Healthcare, Erlangen, Germany). Sequences were first optimised at 1.5T (Siemens MAGNETOM Aera) using a phantom constructed of a spine stabilisation device suspended in gelatin and then applied in patient studies (n=2). Both patient and phantom MRI series were co‐registered with spine CT data to assess artifact extent and geometrical image distortion. Registered data sets were assessed in terms of artifact size, distortions, image quality, visualisation of the spinal cord, spinal fluid, disease site and soft tissues adjacent to metal implants. Results: The optimal parameters were found as a compromise between artifact reduction and MRI acquisition time, while maintaining image quality comparable to conventional sequences: TR/TE 560/15ms, res: 0.8×0.8×4mm, GRAPPA=3, BW=587Hz/Px, 30 slices, TA=3:57min, SEMAC parameters: Slice encoding steps=4, Excitation & Refocusing RF duration=1500us, slice correction factor=1.2. The metal artifact volumes (signal voids and pile‐ups) were reduced using 2D SEMAC sequence on average by 43±16 % from 15.6±2.5 cm3 to 8.9±1.4 cm3. The difference in spinal cord, fluid and lesion volumes outlined using conventional and SEMAC sequences were 7±0.15, 2±0.42 and 35±3.11% respectively in the 6cm long spine sections affected by metallic artifacts. Conclusion: The work shows the importance of MRI metal artifact reduction techniques for reliable RT planning in the vicinity of metallic implants. The use of SEMAC sequence improves geometrical accuracy of outlined volumes in the presence of spine stabilization devices which degrade geometrical information from both CT and conventional SE MRI.
SU‐E‐J‐108: Texture Segmentation in Magnetic Resonance Images Using Discrete Wavelet Transform Combined with Gabor Wavelets
Authors: Huang Z , Lo S , Mayr N , Yuh W
Published: July 2013

Purpose: Edge detection improves image readability and plays an important role in images preprocessing aimed to their segmentation and automatic recognition of their contents. The purpose of this study was to describe methods of edge detection in magnetic resonance images, with the emphasis on the use of discrete wavelet transform (DWT) combined with Gabor wavelets. Methods: Modulus maxima method by Mallat S (A Wavelet Tour of Signal Processing. Academic Press, 1998), provides the method for edge detection using wavelet transform. This method is based on finding local maxima of horizontal and vertical wavelet coefficients in the first level of wavelet decomposition because it is supposed that this level represents edges. This method was tested with various wavelet functions both on simulated and real medical images. A multiresolution approach using undecimated wavelet transform is also employed which allows the low‐low (LL), low‐high (LH), high‐low (HL), and high‐high (HH) sub‐bands to remain at full size. A simple peak finding algorithm is used to determine the peaks out of array of these texture features. Results: Using wavelet transform method, the decomposition was performed up to two levels. Gabor filters are then applied to the wavelet approximations at all levels to obtain the characteristic texture features such as entropy, second to fourth central moments and coefficient of variation. High values of the second central variance and fourth central variance signify images in which regions can be clearly differentiated. The corresponding filter outputs are compared to obtain an image containing minimum pixel values. Conclusion: A complex wavelet function could help to improve results of edge detection in real images. A comparison of basic edge detection methods including simple gradient operators and Gabor wavelets, and their combination with wavelet transform was presented. Mathematical principals were studied, as well as application of these methods.
SU‐E‐J‐109: Registration/Segmentation for Adaptive Radiotherapy Using the Jensen Renyi Divergence
Authors: Markel D , El Naqa I , Zaidi H
Published: July 2013

Purpose: For the purposes of adaptive radiotherapy, the consolidation of offline and online imaging modalities requires costly registration, resegmentation and re‐optimization. The Jensen Renyi (JR) divergence is a non‐parametric generalized statistical measure that can be applied as an energy function for all three of these objectives. Further efficiency and accuracy can be attained by coupling the objective functions such that they iteratively reinforce one another. Methods: The JR divergence was used as an energy function and with a finite difference scheme, the level set differential equation was solved for an active contour along with the energy gradient of control points placed using an adaptive mesh. The segmentation portion has been validated using three data sets; PET and CBCT images of an in‐house phantom for various image qualities, 7 PET scans of head and neck cases from the Louvain database and 22 PET/CT scans of patients with non‐small cell lung carcinoma from the MAASTRO database. Results: Segmentation of an in‐house phantom using the JR divergence showed a marked improvement in concordance index (CI) by almost a factor of 2 compared to the mutual information metric below SNR values of 35.3 and 24.0 for the CBCT and PET images. Average CI for the 7 Louvain cases was found to be 0.56. An average error in estimating the maximal tumor diameters of the 22 MAASTRO cases was found to be 63%, 19.5% and 14% using CT, PET and combined PET/ CT modalities. Conclusion: The JR divergence metric was applied to the task of segmentation using a level sets active contour. It was found to provide improved noise tolerance and competitive segmentation accuracy compared to 9 other PET segmentation methods. A coupled segmentation/registration scheme has been implemented using the JR divergence. Validation is currently being performed using plasticized pig lungs. Funding was provided by the Natural Sciences and Engineering Research Council of Canada. (NSERC‐RGPIN 397711‐11) and the Research Institute of the McGill University Health Centre. HZ is supported by the Swiss National Science Foundation under grant SNSF 31003A‐135576, Geneva Cancer League and the Indo‐Swiss Joint Research Programme ISJRP 138866.
SU‐E‐J‐111: Automatic Delineation of Gross Tumor Volume (GTV) for Non‐Small Cell Lung Cancer (NSCLC) On PET/CT Images Using Fuzzy Markov Random Field (MRF) Model
Authors: Guo Y , Sun J , Lin W , Wang P , Feng Y
Published: July 2013

Purpose: To develop a robust method for automatic delineation of GTV for the lung tumors on fused PET/CT images. Methods: The new method is based on fuzzy Markov random field model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. The parameters of the joint posterior distribution are obtained by fitting the histogram of the region obtained from C‐means clustering and the final membership degree of each voxel to tumor class is estimated with a gradient decent method.The PET and CT simulation images of 3 NSCLC patients were used. Fusion results using MIM 5.2 were exported to an in‐house‐developed software for the study. GTV delineation with the proposed new method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. The manually contoured GTVs were checked and confirmed by another experienced radiation oncologist. The robustness of the new method was evaluated by comparing the overlap of the two delineations using Dice similarity coefficient (DSC) expressed as 2x(Intersection of V1 and V2)/(V1+V2) where V1 is GTVmanual, V2 is GTVauto. Results: GTVs obtained with the two methods were similar and DSC was 0.85±0.013, which showed the advantage of the new method that utilized the information extracted effectively from both PET and CT images in the algorithm. Conclusion: It has been shown that effective and automatic GTV delineation can be achieved with this method for lung tumors located near other organs with similar intensities in PET and CT images (such as when the tumors extends into the chest wall or the mediastinum). The reliability of this method will be further tested and more data will be presented.corresponding:y_m_feng :y_m_feng@yahoo.com@yahoo.com :y_m_feng@yahoo.com; National Science Foundation of China (NSFC‐81201148,81171342,31000784)
SU‐E‐J‐120: A Phantom‐Based Comparison of Lung Tumor Planning Target Volumes and Organs a Risk Dose Reduction Between 4D‐CT and 3D‐CT
Authors: zehtabian M , Faghihi R , Mosleh‐Shirazi M , Shepherd J , Mohammadi M , Sarasanandarajah S , Higgs B
Published: July 2013

Purpose: Lung tumor motion due to respiration necessitates using large margins when creating a planning target volume (PTV). As tumor movement of up to 5 cm has been reported by several investigators, accurate investigation of the internal margin (IM) to 3D CTV is necessary. The purpose of this study was to compare the differences in volume between PTVs generated using 4D‐CT (4D PTV) and 3D‐CT (3D PTV) for varying degrees of tumor motion and to evaluate the differences in dose to normal lung and spinal cord. Methods: A movable phantom was used to simulate lung movement (Figure 1). Three blocks of rubber were attached to the phantom and a 3D‐CT and a 4D‐CT were taken. Then, PTVs were delineated and compared. After that, the PTVs were transferred to a Rando phantom for dose analysis. Also, investigation of varying displacements of lung tumor was done using four blocks of rubber attached to a movable phantom with different known displacements (Figure 2). Results: The average reduction in PTV volumes using 4D‐CT was 33.8%. This led to average decreases of 19.2%, 20.1% and 33.8% for lung V20, mean lung dose and maximum spinal cord dose, respectively (Table 1)., Investigation of varying displacements of lung tumor shows that if tumors move more than 4 cm, the 4D PTV may be equal to or larger than the 3D PTV(Table 2).Figures 3 shows overlapping of PTVs of tumors with 2 cm motion and 4 cm motion in 3D and 4D. Conclusion: It can be concluded that 4D‐CT reduces organ at risk doses. For lung tumors with large displacements, 4D‐CT is a promising method because the exact IM in the direction of tumor motion is achieved. In these cases, the use of 3D causes not only unnecessary dose to normal tissue but also missing part of the tumor (Figure 3).
SU‐E‐J‐122: Time‐Ordered Four Dimensional Cone‐Beam CT
Authors: Nakano M , Kida S , Masutani Y , Shiraki T , Shiraishi K , Yamamoto K , Nakagawa K , Haga A
Published: July 2013

Purpose: Four‐dimensional (4D) cone‐beam CT (CBCT) techniques have been started to use in clinical sites as a tool of image‐guided radiotherapy (IGRT), especially in the treatment of lung tumors. However, those techniques suppose periodic respiratory motion, and there are little approaches to visualize organs which present non‐periodic time‐ordered motion using CBCT imaging techniques. The present study proposes a method to visualize time‐ordered motion including peristaltic motion of gastrointestinal organs and adjacent area. Methods: Projection data sets of clinical patients and digital phantom were reconstructed in this study. Patients' data sets were acquired using the X‐ray Volume Imaging system (XVI, version 4.2) on Synergy linear accelerator system (Elekta, UK) as pre‐treatment CBCT imaging to setup prostate radiotherapy patients with 11.5 cm offset‐located flat panel detector (FPD) unit. An elliptic‐cylindrical digital phantom which contains a moving air sphere of 3 cm diameter was also reconstructed. These projection data sets were reconstructed using our in‐house reconstruction software based on Feldkamp, Davis and Kress (FDK) algorithm. In this study, 180 degrees or less angular ranges of projection data were used to reconstruct CBCT image set of each time phase, and the range moved as time progressed. Results: Reconstructed sagittal images of clinical patients visualized that flatus and stool inside rectum moved with the progress of time. Reconstructed sagittal images of digital phantom with several angular ranges of projection showed that the longitudinal length is getting shorter with shorter range of projection, though a shape of a sphere is getting blurred in vertical direction. Conclusion: The presented method of time‐ordered 4D CBCT reconstruction visualized deformation of intestine and rectum, and motion of flatus and stool, though the method has a trade‐off between temporal resolution improvement and image quality degradation.
SU‐E‐J‐123: Inter‐Fraction Rectum Motion for Patients with Rectal Cancer Detected by Daily Cone Beam CT
Authors: Lotey R , Yu N , Abdel‐Wahab M , Xia P
Published: July 2013

Purpose: The inter‐fraction motion of the rectum for patients with rectal cancer is not well studied. Using daily kilo‐voltage cone beam CTs, the purpose of this study is to quantify the interfractional rectal motion relative to the planning CT and to assess proper planning margins for these patients. Methods: A total of 211 daily CBCT from 9 patients, who received IMRT treatment, were selected for this study. On each CBCT, the treatment target volume of the rectum was retrospectively contoured. For each patient, the daily contoured rectum (25 CBCT/patient on average) was transferred to the planning CT with a rigid bone registration. The planning margins to compensate for the interfractional rectal motion were determined by expanding the planning rectum (clinical target volume) with a series of margins up to 2.0 cm. The daily rectal motion was measured in superior, middle, and inferior segments. For each segmented rectum, the lateral and anterior‐posterior motions were measured relative to the centroid of the corresponding segmented CTV. Results: For all patients, 1.7 cm uniform expansion of the CTV was required to achieve a geometric coverage in 96% fractions. Furthermore, 1.7cm uniform expansion resulted in geometric coverage in 99% fractions of the first half course of the treatment and in 92% fractions of the second half course. There is a direct correlation between the segment location and magnitude of the displacement/movement (r=0.99). For these patients, the average margin expansions required for 100% geometric coverage for the Inferior, Middle, and Superior segments of the rectum were 0.8 cm, 1.0 cm, and 1.2 cm respectively. Conclusion: We found that the superior segment of the rectal wall is more mobile than the other segments, followed by the middle and inferior segments. Non‐uniform margins may be considered to ensure adequate geometric coverage while sparing surrounding normal tissue.
SU‐E‐J‐132: Lung Dose Reduction From Patient Specific 4D Motion Based Non‐Uniform Dose Prescription in Lung IMRT Treatment
Authors: Zou W , Zhang M , Xu X , Chen T , Haffty B , Jabbour S , Yue N
Published: July 2013

Purpose: Motion of the tumor target in lung can be significant in patients. Traditionally lung dose is uniformly prescribed to the entire Internal Tumor Volume (ITV) with certain setup margin. Such uniform dose prescription may deliver quite high dose to the normal tissue surrounding the target volume due to tumor motion. This study explores patient specific motion based non‐uniform dose prescription that is to deliver prescription dose to the target while maximizing the sparing of the surrounding normal tissue. Methods: Patient 4D CT images were co llected during simulation. CT phases were deformably registered. The spatial tracks of the tumor and surrounding tissue voxels were extracted from the transform matrix. The voxel dose to the tumor target and the surrounding tissue was evaluated at each phase and then summed together. Simulated Annealing algorithm was used to search for the spatial dose prescription distribution that delivers sufficient tumor voxel dose while minimizing the surrounding normal tissue dose. Results: Simple phantom and patients studies were included. The optimization process generally co nverged within 60 seco nds to obtain a solution for non‐uniform dose prescription. The Result has shown a reduction in the adjacent lung tissue as great as 18% while maintaining the tumor dose. Co nclusion: This study proposed a methodology for a non‐uniform dose prescription that would deliver sufficient dose to the moving lung tumor target while maximizing the sparing of the surrounding lung tissue. The beam delivery and the interplay effect are being further studied.
SU‐E‐J‐136: Analysis of Residual Motion Artifact in 4DCT Using Simple 1D Theoretical Model
Authors: Kim T , Kang S , Kim D , Suh T
Published: July 2013

Purpose: We quantify the residual motion artifact in 4D‐CT scan using the dynamic lung phantom which could simulate respiratory target motion and suggest a simple one‐dimension theoretical model to explain and characterize the source of motion artifacts in 4DCT scanning. Methods: we set‐up regular 1D sine motion and adjusted three level of amplitude (10, 20, 30 mm) with fixed period (4s). The 4DCT scans are acquired in helical mode and phase information provided by the belt type respiratory monitoring system. The images were sorted into ten phase bins ranging from 0% to 90 %. The reconstructed images were subsequently imported into the Treatment Planning System for target delineation using a fixed contour window and dimensions of the three targets are measured along the direction of motion. Results: Target dimension of each phase image have same changing trend. The error is minimum at 50% phase in all case(10, 20, 30 mm) and we found that ΔS(target dimension change) of 10, 20 and 30 mm amplitude were 0(0%), 0.1(5%), 0.1(5%) cm respectively compare to the static image of target diameter (2 cm). while the error is maximum at 30% and 80% phase ΔS of 10, 20 and 30 mm amplitude were 0.2(10%), 0.7(35%), 0.9(45%) cm respectively. Based on these Result, we try to analysis the residual motion artifact in 4D‐CT scan using a simple one‐dimension theoretical model and also we developed a simulation program. Conclusion: In this study, we focus on provides a more intuitive understanding about the residual motion artifact and try to explain the relationship motion parameters of the scanner, treatment couch and tumor. In conclusion, our results could help to decide the appropriate reconstruction phase and CT parameters which reduce the residual motion artifact in 4DCT. This program was supported by the program of Basic Atomic Energy Research Institute (BAERI) which is a part of the Nuclear R and D programs (No. 20120004886) funded by the Ministry of Education, Science and Technology (MEST)