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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.
Purpose: In treatment planning of charged-particle radiotherapy, patient heterogeneity is conventionally modeled as variable-density water converted from CT images to best reproduce the stopping power, which may lead to inaccuracies in the handling o
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
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
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 an
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