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Register a new userRelationship between mass density, electron density, and elemental composition of body tissues for Monte Carlo simulation in radiation treatment planning
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Nobuyuki Kanematsu Ph.D.
Publication date
2015
fields
Physics
and research's language is
English
Authors
Nobuyuki Kanematsu
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Purpose: For Monte Carlo simulation of radiotherapy, x-ray CT number of every system needs to be calibrated and converted to mass density and elemental composition. This study aims to formulate material properties of body tissues for practical two-step conversion from CT number. Methods: We used the latest compilation on body tissues that constitute reference adult male and female. We formulated the relations among mass, electron, and elemental densities into polylines to connect representative tissues, for which we took mass-weighted mean for the tissues in limited density regions. We compared the polyline functions of mass density with a bi-line for electron density and broken lines for elemental densities, which were derived from preceding studies. Results: There was generally high correlation between mass density and the other densities except of C, N, and O for light spongiosa tissues occupying 1% of body mass. The polylines fitted to the dominant tissues and were generally consistent with the bi-line and the broken lines. Conclusions: We have formulated the invariant relations between mass and electron densities and from mass to elemental densities for body tissues. The formulation enables Monte Carlo simulation in treatment planning practice without additional burden with CT-number calibration.
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Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distribution maps in patient need to be calculated and evaluated for the purpose of killing tumor and protecting healthy tissue. Monte Carlo (MC) radiation transport calculation is able to account for all aspects of radiological physics within 3D heterogeneous media such as the human body and generate the dose distribution maps accurately. However, an MC calculation for doses in radiotherapy usually takes a great mass of time to achieve acceptable statistical uncertainty, impeding the MC methods from wider clinic applications. Here we introduce a convolutional neural network (CNN), termed as Monte Carlo Denoising Net (MCDNet), to achieve the acceleration of the MC dose calculations in radiotherapy, which is trained to directly predict the high-photon (noise-free) dose maps from the low-photon (noise-much) dose maps. Thirty patients with postoperative rectal cancer who accepted intensity-modulated radiation therapy (IMRT) were enrolled in this study. 3D Gamma Index Passing Rate (GIPR) is used to evaluate the performance of predicted dose maps. The experimental results demonstrate that the MCDNet can improve the GIPR of dose maps of 1x107 photons over that of 1x108 photons, yielding over 10x speed-up in terms of photon numbers used in the MC simulations of IMRT. It is of great potential to investigate the performance of this method on the other tumor sites and treatment modalities.
We have previously described RapidBrachyMCTPS, a brachytherapy treatment planning toolkit consisting of a graphical user interface (GUI) and a Geant4-based Monte Carlo (MC) dose calculation engine. This work describes the tools that have recently bee
n added to RapidBrachyMCTPS, such that it now serves as the first stand-alone application for MC-based brachytherapy treatment planning. Notable changes include updated applicator import and positioning, three-plane contouring tools, and updated dose optimization algorithms that, in addition to optimizing dwell position and dwell time, also optimize the rotating shield angles in intensity modulated brachytherapy. The main modules of RapidBrachyMCTPS were validated including DICOM import, applicator import and positioning, contouring, material assignment, source specification, catheter reconstruction, EGSphant generation, interface with the MC code, and dose optimization and analysis tools. Two patient cases were simulated to demonstrate these principles, illustrating the control and flexibility offered by RapidBrachyMCTPS for all steps of the treatment planning pathway. RapidBrachyMCTPS is now a stand-alone application for brachytherapy treatment planning, and offers a user-friendly interface to access powerful MC calculations. It can be used to validate dose distributions from clinical treatment planning systems or model-based dose calculation algorithms, and is also well suited to testing novel combinations of radiation sources and applicators, especially those shielded with high-Z materials.
Noncoplanar radiation therapy treatment planning has the potential to improve dosimetric quality as compared to traditional coplanar techniques. Likewise, automated treatment planning algorithms can reduce a planners active treatment planning time and remove inter-planner variability. To address the limitations of traditional treatment planning, we have been developing a suite of algorithms called station parameter optimized radiation therapy (SPORT). Within the SPORT suite of algorithms, we propose a method called NC-POPS to produce noncoplanar (NC) plans using the fully automated Pareto Optimal Projection Search (POPS) algorithm. Our NC-POPS algorithm extends the original POPS algorithm to the noncoplanar setting with potential applications to both IMRT and VMAT. The proposed algorithm consists of two main parts: 1) noncoplanar beam angle optimization (BAO) and 2) fully automated inverse planning using the POPS algorithm. We evaluate the performance of NC-POPS by comparing between various noncoplanar and coplanar configurations. To evaluate plan quality, we compute the homogeneity index (HI), conformity index (CI), and dose-volume histogram (DVH) statistics for various organs-at-risk (OARs). As compared to the evaluated coplanar baseline methods, the proposed NC-POPS method achieves significantly better OAR sparing, comparable or better dose conformity, and similar dose homogeneity. Our proposed NC-POPS algorithm provides a modular approach for fully automated treatment planning of noncoplanar IMRT cases with the potential to substantially improve treatment planning workflow and plan quality.
Pancreas stereotactic body radiotherapy treatment planning requires planners to make sequential, time consuming interactions with the treatment planning system (TPS) to reach the optimal dose distribution. We seek to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. The focus of pancreas SBRT planning is finding a balance between organs-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to OAR dose constraints. We have formulated such interactions between the planner and the TPS into a finite-horizon RL model. First, planning status features are evaluated based on human planner experience and defined as planning states. Second, planning actions are defined to represent steps that planners would commonly implement to address different planning needs. Finally, we have derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients and generated plans for 24 cases in a separate validation set. All 24 bot-generated plans achieve similar PTV coverages compared to clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot can be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions are consistent, indicating reproducibility of the learning process.
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