Zero-point entropies of spin-jam and spin-glass states in a frustrated magnet
Not provided.
Materials Science↗
Engineering topics
Sheng, K.
Publications and source records attributed to Sheng, K..
Secondary structure drives self-assembly in weakly segregated globular protein-rod block copolymers
Explore the source record for details and available documents.
99 GENERAL AND MISCELLANEOUS↗
TH-AB-BRB-03: 4n Radiotherapy
Current state-of-the art digital C-arm medical linear accelerators are capable of delivering radiation treatments with high level of automation, which affords coordinated motions of gantry, couch, and multileaf collimator (MLC) with dose rate modulations. The new machine capacity has shown the potential to bring substantially improved radiation dosimetry and/or delivery efficiency to many challenging diseases. Combining an integrated beam orientation optimization algorithm with automated machine navigation, markedly improved dose conformity has been achieved using 4ρ therapy. Trajectory modulated radiation therapy (TMAT) can be used to deliver highly conformal dose to partial breast or to carve complex dose distribution for therapy involving extended volumes such as total marrow and total lymph node treatment. Dynamic electron arc radiotherapy (DEAR) not only overcomes the deficiencies of conventional electron therapy in dose conformity and homogeneity but also achieves so without patient-specific shields. The combination of MLC and couch tracking provides improved motion management of thoracic and abdominal tumors. A substantial body of work has been done in these technological advances for clinical translation. The proposed symposium will provide a timely review of these exciting opportunities. Learning Objectives: Recognize the potential of using digitally controlled linacs for clinically significant improvements in delivered dose distributions for various treatment sites. Identify existing approaches to treatment planning, optimization and delivery for treatment techniques utilizing the advanced functions of digital linacs and venues for further development and improvement. Understand methods for testing and validating delivery system performance. Identify tools available on current delivery systems for implementation and control for such treatments. Obtain the update in clinical applications, trials and regulatory approval. K. Sheng, NIH U19AI067769, NIH R43CA183390, NIH R01CA188300, Varian Medical Systems V. Yu, Varian Medical Systems, AAPM Summer Undergraduate Fellowship, NSF graduate fellowship S. Nill, Elekta AB. Cancer Research UK under Programme C33589/A19727, NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.
60 APPLIED LIFE SCIENCES↗
WE-B-BRD-02: MR Simulation for Radiation Therapy
The use of MRI in radiation therapy is rapidly increasing. Applications vary from the MRI simulator, to the MRI fused with CT, and to the integrated MRI+RT system. Compared with the standard MRI QA, a broader scope of QA features has to be defined in order to maximize the benefits of using MRI in radiation therapy. These QA features include geometric fidelity, image registration, motion management, cross-system alignment, and hardware interference. Advanced MRI techniques require a specific type of QA, as they are being widely used in radiation therapy planning, dose calculations, post-implant dosimetry, and prognoses. A vigorous and adaptive QA program is crucial to defining the responsibility of the entire radiation therapy group and detecting deviations from the performance of high-quality treatment. As a drastic departure from CT simulation, MRI simulation requires changes in the work flow of treatment planning and image guidance. MRI guided radiotherapy platforms are being developed and commercialized to take the advantage of the advance in knowledge, technology and clinical experience. This symposium will from an educational perspective discuss the scope and specific issues related to MRI guided radiotherapy. Learning Objectives: Understand the difference between a standard and a radiotherapy-specific MRI QA program. Understand the effects of MRI artifacts (geometric distortion and motion) on radiotherapy. Understand advanced MRI techniques (ultrashort echo, fast MRI including dynamic MRI and 4DMRI, diffusion, perfusion, and MRS) and related QA. Understand the methods to prepare MRI for treatment planning (electron density assignment, multimodality image registration, segmentation and motion management). Current status of MRI guided treatment platforms. Dr. Jihong Wang has a research grant with Elekta-MRL project. Dr. Ke Sheng receives research grants from Varian Medical systems.
60 APPLIED LIFE SCIENCES↗
A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image registration
Purpose: Validating the usage of deformable image registration (DIR) for daily patient positioning is critical for adaptive radiotherapy (RT) applications pertaining to head and neck (HN) radiotherapy. The authors present a methodology for generating biomechanically realistic ground-truth data for validating DIR algorithms for HN anatomy by (a) developing a high-resolution deformable biomechanical HN model from a planning CT, (b) simulating deformations for a range of interfraction posture changes and physiological regression, and (c) generating subsequent CT images representing the deformed anatomy. Methods: The biomechanical model was developed using HN kVCT datasets and the corresponding structure contours. The voxels inside a given 3D contour boundary were clustered using a graphics processing unit (GPU) based algorithm that accounted for inconsistencies and gaps in the boundary to form a volumetric structure. While the bony anatomy was modeled as rigid body, the muscle and soft tissue structures were modeled as mass–spring-damper models with elastic material properties that corresponded to the underlying contoured anatomies. Within a given muscle structure, the voxels were classified using a uniform grid and a normalized mass was assigned to each voxel based on its Hounsfield number. The soft tissue deformation for a given skeletal actuation was performed using an implicit Euler integration with each iteration split into two substeps: one for the muscle structures and the other for the remaining soft tissues. Posture changes were simulated by articulating the skeletal structure and enabling the soft structures to deform accordingly. Physiological changes representing tumor regression were simulated by reducing the target volume and enabling the surrounding soft structures to deform accordingly. Finally, the authors also discuss a new approach to generate kVCT images representing the deformed anatomy that accounts for gaps and antialiasing artifacts that may be caused by the biomechanical deformation process. Accuracy and stability of the model response were validated using ground-truth simulations representing soft tissue behavior under local and global deformations. Numerical accuracy of the HN deformations was analyzed by applying nonrigid skeletal transformations acquired from interfraction kVCT images to the model’s skeletal structures and comparing the subsequent soft tissue deformations of the model with the clinical anatomy. Results: The GPU based framework enabled the model deformation to be performed at 60 frames/s, facilitating simulations of posture changes and physiological regressions at interactive speeds. The soft tissue response was accurate with a R{sup 2} value of >0.98 when compared to ground-truth global and local force deformation analysis. The deformation of the HN anatomy by the model agreed with the clinically observed deformations with an average correlation coefficient of 0.956. For a clinically relevant range of posture and physiological changes, the model deformations stabilized with an uncertainty of less than 0.01 mm. Conclusions: Documenting dose delivery for HN radiotherapy is essential accounting for posture and physiological changes. The biomechanical model discussed in this paper was able to deform in real-time, allowing interactive simulations and visualization of such changes. The model would allow patient specific validations of the DIR method and has the potential to be a significant aid in adaptive radiotherapy techniques.
60 APPLIED LIFE SCIENCES↗
A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures
Purpose: Real-time adaptive planning and treatment has been infeasible due in part to its high computational complexity. There have been many recent efforts to utilize graphics processing units (GPUs) to accelerate the computational performance and dose accuracy in radiation therapy. Data structure and memory access patterns are the key GPU factors that determine the computational performance and accuracy. In this paper, the authors present a nonvoxel-based (NVB) approach to maximize computational and memory access efficiency and throughput on the GPU. Methods: The proposed algorithm employs a ray-tracing mechanism to restructure the 3D data sets computed from the CT anatomy into a nonvoxel-based framework. In a process that takes only a few milliseconds of computing time, the algorithm restructured the data sets by ray-tracing through precalculated CT volumes to realign the coordinate system along the convolution direction, as defined by zenithal and azimuthal angles. During the ray-tracing step, the data were resampled according to radial sampling and parallel ray-spacing parameters making the algorithm independent of the original CT resolution. The nonvoxel-based algorithm presented in this paper also demonstrated a trade-off in computational performance and dose accuracy for different coordinate system configurations. In order to find the best balance between the computed speedup and the accuracy, the authors employed an exhaustive parameter search on all sampling parameters that defined the coordinate system configuration: zenithal, azimuthal, and radial sampling of the convolution algorithm, as well as the parallel ray spacing during ray tracing. The angular sampling parameters were varied between 4 and 48 discrete angles, while both radial sampling and parallel ray spacing were varied from 0.5 to 10 mm. The gamma distribution analysis method (γ) was used to compare the dose distributions using 2% and 2 mm dose difference and distance-to-agreement criteria, respectively. Accuracy was investigated using three distinct phantoms with varied geometries and heterogeneities and on a series of 14 segmented lung CT data sets. Performance gains were calculated using three 256 mm cube homogenous water phantoms, with isotropic voxel dimensions of 1, 2, and 4 mm. Results: The nonvoxel-based GPU algorithm was independent of the data size and provided significant computational gains over the CPU algorithm for large CT data sizes. The parameter search analysis also showed that the ray combination of 8 zenithal and 8 azimuthal angles along with 1 mm radial sampling and 2 mm parallel ray spacing maintained dose accuracy with greater than 99% of voxels passing the γ test. Combining the acceleration obtained from GPU parallelization with the sampling optimization, the authors achieved a total performance improvement factor of >175 000 when compared to our voxel-based ground truth CPU benchmark and a factor of 20 compared with a voxel-based GPU dose convolution method. Conclusions: The nonvoxel-based convolution method yielded substantial performance improvements over a generic GPU implementation, while maintaining accuracy as compared to a CPU computed ground truth dose distribution. Such an algorithm can be a key contribution toward developing tools for adaptive radiation therapy systems.
60 APPLIED LIFE SCIENCES↗