Results 1 - 10 of 101
Results 1 - 10 of 101. Search took: 0.018 seconds
|Sort by: date | relevance|
[en] Strategies for treating thoracic and liver tumors using pencil beam scanning proton therapy Thoracic and liver tumors have not been treated with pencil beam scanning (PBS) proton therapy until recently. This is because of concerns about the significant interplay effects between proton spot scanning and patient’s respiratory motion. However, not all tumors have unacceptable magnitude of motion for PBS proton therapy. Therefore it is important to analyze the motion and understand the significance of the interplay effect for each patient. The factors that affect interplay effect and its washout include magnitude of motion, spot size, spot scanning sequence and speed. Selection of beam angle, scanning direction, repainting and fractionation can all reduce the interplay effect. An overview of respiratory motion management in PBS proton therapy including assessment of tumor motion and WET evaluation will be first presented. As thoracic tumors have very different motion patterns from liver tumors, examples would be provided for both anatomic sites. As thoracic tumors are typically located within highly heterogeneous environments, dose calculation accuracy is a concern for both treatment target and surrounding organs such as spinal cord or esophagus. Strategies for mitigating the interplay effect in PBS will be presented and the pros and cons of various motion mitigation strategies will be discussed. Learning Objectives: Motion analysis for individual patients with respect to interplay effect Interplay effect and mitigation strategies for treating thoracic/liver tumors with PBS Treatment planning margins for PBS The impact of proton dose calculation engines over heterogeneous treatment target and surrounding organs I have a current research funding from Varian Medical System under the master agreement between University of Pennsylvania and Varian; L. Lin, I have a current funding from Varian Medical System under the master agreement between University of Pennsylvania and Varian.; H. Li, Na
[en] Despite widespread IMRT treatments at modern radiation therapy clinics, precise dosimetric commissioning of an IMRT system remains a challenge. In the most recent report from the Radiological Physics Center (RPC), nearly 20% of institutions failed an end-to-end test with an anthropomorphic head and neck phantom, a test that has rather lenient dose difference and distance-to-agreement criteria of 7% and 4 mm. The RPC report provides strong evidence that IMRT implementation is prone to error and that improved quality assurance tools are required. At the heart of radiation therapy dosimetry is the multidimensional dosimeter. However, due to the limited availability of water-equivalent dosimetry materials, research and development in this important field is challenging. In this session, we will review a few dosimeter developments that are either in the laboratory phase or in the pre-commercialization phase. 1) Radiochromic plastic. Novel formulations exhibit light absorbing optical contrast with very little scatter, enabling faster, broad beam optical CT design. 2) Storage phosphor. After irradiation, the dosimetry panels will be read out using a dedicated 2D scanning apparatus in a non-invasive, electro-optic manner and immediately restored for further use. 3) Liquid scintillator. Scintillators convert the energy from x-rays and proton beams into visible light, which can be recorded with a scientific camera (CCD or CMOS) from multiple angles. The 3D shape of the dose distribution can then be reconstructed. 4) Cherenkov emission imaging. Gated intensified imaging allows video-rate passive detection of Cherenkov emission during radiation therapy with the room lights on. Learning Objectives: To understand the physics of a variety of dosimetry techniques based upon optical imaging To investigate the strategies to overcome respective challenges and limitations To explore novel ideas of dosimeter design Supported in part by NIH Grants R01CA148853, R01CA182450, R01CA109558. Brian Pogue is founder and president of the company DoseOptics LLC, dedicated to developing and commercializing the first dedicated Cerenkov imaging camera and system for radiation dose imaging. Work reported in this talk does not involve the use of DoseOptics technology.; H. Li, this work was supported in part by NIH Grant No. R01CA148853; S. Beddar, NIH funding R01-CA182450
[en] Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on PENELOPE and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods: We first translated PENELOPE from FORTRAN to C++ and validated that the translation produced equivalent results. Then we adapted the C++ code to CUDA in a workflow optimized for GPU architecture. We expanded upon the original code to include voxelized transport boosted by Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gPENELOPE highly user-friendly. Moreover, we incorporated the vendor-provided MRIdian head model into the code. We performed a set of experimental measurements on MRIdian to examine the accuracy of both the head model and gPENELOPE, and then applied gPENELOPE toward independent validation of patient doses calculated by MRIdian’s KMC. Results: We achieve an average acceleration factor of 152 compared to the original single-thread FORTRAN implementation with the original accuracy preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen (1), mediastinum (1) and breast (1), the MRIdian dose calculation engine agrees with gPENELOPE with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: We developed a Monte Carlo simulation platform based on a GPU-accelerated version of PENELOPE. We validated that both the vendor provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.
[en] Purpose: To develop a novel and rapid, SIFT-based algorithm for assessing feature motion on cine MR images acquired during MRI-guided radiotherapy treatments. In particular, we apply SIFT descriptors toward both partitioning cine images into respiratory states and tracking regions across frames. Methods: Among a training set of images acquired during a fraction, we densely assign SIFT descriptors to pixels within the images. We cluster these descriptors across all frames in order to produce a dictionary of trackable features. Associating the best-matching descriptors at every frame among the training images to these features, we construct motion traces for the features. We use these traces to define respiratory bins for sorting images in order to facilitate robust pixel-by-pixel tracking. Instead of applying conventional methods for identifying pixel correspondences across frames we utilize a recently-developed algorithm that derives correspondences via a matching objective for SIFT descriptors. Results: We apply these methods to a collection of lung, abdominal, and breast patients. We evaluate the procedure for respiratory binning using target sites exhibiting high-amplitude motion among 20 lung and abdominal patients. In particular, we investigate whether these methods yield minimal variation between images within a bin by perturbing the resulting image distributions among bins. Moreover, we compare the motion between averaged images across respiratory states to 4DCT data for these patients. We evaluate the algorithm for obtaining pixel correspondences between frames by tracking contours among a set of breast patients. As an initial case, we track easily-identifiable edges of lumpectomy cavities that show minimal motion over treatment. Conclusions: These SIFT-based methods reliably extract motion information from cine MR images acquired during patient treatments. While we performed our analysis retrospectively, the algorithm lends itself to prospective motion assessment. Applications of these methods include motion assessment, identifying treatment windows for gating, and determining optimal margins for treatment
[en] Purpose: To study the feasibility of using the pRNA 3WJ nanoparticles to carry I-125 or Cs-131 to target and treat cancer. As the first step, we investigated the stabilities of pRNA 3WJ nanoparticles that are essential for cancer targeting and treatment in this study. Methods: The thermodynamic stability of assembled RNA 3WJ nanoparticles was studied using the TGGE system. The nanoparticles were irradiated with I-125 or Cs-131 radioactive sources that were immersed in the RNA nanoparticle/DNA structure sample liquid contained in a small vial. The irradiation of the RNA samples was performed for different time periods and doses. The purpose was to distinguish the effects of radiation on DNA and RNA structures. Unradiated samples were used as control. Results: RNA nanoparticles were formed by mixing three pieces of oligos, 3WJa, 3WJb, and 3WJc at 1:1:1 molar ratio. Figure 4 demonstrates that 2′-F modified 3WJ nanoparticles remained stable at temperatures as high as 66.8 ± 2°C, and exhibited melting temperatures of 71 ± 2°C. The radiation stability test was performed with I- 125 and Cs-131 irradiation. Several DNA structures including plasmids were included as control. The first test introduced I-125 and a low dose of 1 Gy to both RNA and DNA samples, but no change was observed. When the dose was increased to 30 Gy, DNA was damaged while RNA remained unchanged. Three tests were also conducted with Cs-131 with 7 Gy, 21 Gy, 30 Gy, and 89 Gy, and the results were similar to those with I-125. Conclusion: pRNA 3WJ nanoparticles are able to form efficiently by onepot self-assembly. They remained stable at high temperatures and high therapeutic doses over a long time. These unique features suggest that RNA 3WJ nanoparticles have the potential to be used for targeted radiation therapy for cancer treatment
[en] Purpose: To develop a tool enabling soft tissue based image guidance using dual energy radiographs for cases when on-board CT is not available. Method: Dual energy planar radiographs can be applied to image guidance for targeting lung lesions because the bone based alignment only may not be sufficient as the lesions move. We acquired images of an anthropomorphic thorax phantom at 120 and 60 KVp respectively. Using a weighted logarithmic subtraction of these dual energy images, a soft tissue enhanced and a bone enhanced image were generated and they could be used for the image guidance purpose. Similar processing was also applied to a dual energy image set acquired for a patient undergoing a proton therapy. Results: The soft tissue enhanced images suppressed bones (ribs and scapula) overlying on lung, thus enabling a better visualization of soft tissue and lesion, while the bone enhanced image suppressed the soft tissue. These enhanced effects were visually apparent without further processing for display enhancements, such as using histogram or edge enhancement technique. Conclusions: The phantom image processing was encouraging. The initial test on the patient image set showed that other post processing might still be able to add value in visualizing soft tissues in addition to the dual energy soft tissue enhancement. More evaluations are needed to determine the potential benefit of this technique in the clinic
[en] Purpose: To gain insight into the role of parenchyma stroma in the characterization of breast tumors by incorporating computerized mammographic parenchyma assessment into breast CADx in the task of distinguishing between malignant and benign lesions. Methods: This study was performed on 182 biopsy-proven breast mass lesions, including 76 benign and 106 malignant lesions. For each full-field digital mammogram (FFDM) case, our quantitative imaging analysis was performed on both the tumor and a region-of-interest (ROI) from the normal contralateral breast. The lesion characterization includes automatic lesion segmentation and feature extraction. Radiographic texture analysis (RTA) was applied on the normal ROIs to assess the mammographic parenchymal patterns of these contralateral normal breasts. Classification performance of both individual computer extracted features and the output from a Bayesian artificial neural network (BANN) were evaluated with a leave-one-lesion-out method using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the figure of merit. Results: Lesion characterization included computer-extracted phenotypes of spiculation, size, shape, and margin. For parenchymal pattern characterization, five texture features were selected, including power law beta, contrast, and edge gradient. Merging of these computer-selected features using BANN classifiers yielded AUC values of 0.79 (SE=0.03) and 0.67 (SE=0.04) in the task of distinguishing between malignant and benign lesions using only tumor phenotypes and texture features from the contralateral breasts, respectively. Incorporation of tumor phenotypes with parenchyma texture features into the BANN yielded improved classification performance with an AUC value of 0.83 (SE=0.03) in the task of differentiating malignant from benign lesions. Conclusion: Combining computerized tumor and parenchyma phenotyping was found to significantly improve breast cancer diagnostic accuracy highlighting the need to consider both tumor and stroma in decision making. Funding: University of Chicago Dean Bridge Fund, NCI U24-CA143848-05, P50-CA58223 Breast SPORE program, and Breast Cancer Research Foundation. COI: MLG is a stockholder in R2 technology/Hologic and receives royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi, and Toshiba. MLG is a cofounder and stockholder in Quantitative Insights
[en] Purpose: To investigate radiotherapy outcomes by incorporating 4DCT-based physiological and tumor elasticity functions for lung cancer patients. Methods: 4DCT images were acquired from 28 lung SBRT patients before radiation treatment. Deformable image registration (DIR) was performed from the end-inhale to the end-exhale using a B-Spline-based algorithm (Elastix, an open source software package). The resultant displacement vector fields (DVFs) were used to calculate a relative Jacobian function (RV) for each patient. The computed functions in the lung and tumor regions represent lung ventilation and tumor elasticity properties, respectively. The 28 patients were divided into two groups: 16 with two-year tumor local control (LC) and 12 with local failure (LF). The ventilation and elasticity related RV functions were calculated for each of these patients. Results: The LF patients have larger RV values than the LC patients. The mean RV value in the lung region was 1.15 (±0.67) for the LF patients, higher than 1.06 (±0.59) for the LC patients. In the tumor region, the elasticity-related RV values are 1.2 (±0.97) and 0.86 (±0.64) for the LF and LC patients, respectively. Among the 16 LC patients, 3 have the mean RV values greater than 1.0 in the tumors. These tumors were located near the diaphragm, where the displacements are relatively large.. RV functions calculated in the tumor were better correlated with treatment outcomes than those calculated in the lung. Conclusion: The ventilation and elasticity-related RV functions in the lung and tumor regions were calculated from 4DCT image and the resultant values showed differences between the LC and LF patients. Further investigation of the impact of the displacements on the computed RV is warranted. Results suggest that the RV images might be useful for evaluation of treatment outcome for lung cancer patients
[en] Purpose: The daily treatment MRIs acquired on MR-IGRT systems, like diagnostic MRIs, suffer from intensity inhomogeneity issue, associated with B1 and B0 inhomogeneities. An improved homomorphic unsharp mask (HUM) filtering method, automatic and robust body segmentation, and imaging field-of-view (FOV) detection methods were developed to compute the multiplicative slow-varying correction field and correct the intensity inhomogeneity. The goal is to improve and normalize the voxel intensity so that the images could be processed more accurately by quantitative methods (e.g., segmentation and registration) that require consistent image voxel intensity values. Methods: HUM methods have been widely used for years. A body mask is required, otherwise the body surface in the corrected image would be incorrectly bright due to the sudden intensity transition at the body surface. In this study, we developed an improved HUM-based correction method that includes three main components: 1) Robust body segmentation on the normalized image gradient map, 2) Robust FOV detection (needed for body segmentation) using region growing and morphologic filters, and 3) An effective implementation of HUM using repeated Gaussian convolution. Results: The proposed method was successfully tested on patient images of common anatomical sites (H/N, lung, abdomen and pelvis). Initial qualitative comparisons showed that this improved HUM method outperformed three recently published algorithms (FCM, LEMS, MICO) in both computation speed (by 50+ times) and robustness (in intermediate to severe inhomogeneity situations). Currently implemented in MATLAB, it takes 20 to 25 seconds to process a 3D MRI volume. Conclusion: Compared to more sophisticated MRI inhomogeneity correction algorithms, the improved HUM method is simple and effective. The inhomogeneity correction, body mask, and FOV detection methods developed in this study would be useful as preprocessing tools for many MRI-related research and clinical applications in radiotherapy. Authors have received research grants from ViewRay and Varian.
[en] Purpose: To ensure patient safety and treatment quality in RT departments that use Varian ARIA and Eclipse, we developed a computer software system and interface functions that allow previously developed electron chart checking (EcCk) methodologies to support these Varian systems. Methods: ARIA and Eclipse store most patient information in its MSSQL database. We studied the contents in the hundreds database tables and identified the data elements used for patient treatment management and treatment planning. Interface functions were developed in both c-sharp and MATLAB to support data access from ARIA and Eclipse servers using SQL queries. These functions and additional data processing functions allowed the existing rules and logics from EcCk to support ARIA and Eclipse. Dose and structure information are important for plan quality check, however they are not stored in the MSSQL database but as files in Varian private formats, and cannot be processed by external programs. We have therefore implemented a service program, which uses the DB Daemon and File Daemon services on ARIA server to automatically and seamlessly retrieve dose and structure data as DICOM files. This service was designed to 1) consistently monitor the data access requests from EcCk programs, 2) translate the requests for ARIA daemon services to obtain dose and structure DICOM files, and 3) monitor the process and return the obtained DICOM files back to EcCk programs for plan quality check purposes. Results: EcCk, which was previously designed to only support MOSAIQ TMS and Pinnacle TPS, can now support Varian ARIA and Eclipse. The new EcCk software has been tested and worked well in physics new start plan check, IMRT plan integrity and plan quality checks. Conclusion: Methods and computer programs have been implemented to allow EcCk to support Varian ARIA and Eclipse systems. This project was supported by a research grant from Varian Medical System