The recent development of model-based dose calculation algorithms (MBDCA) have allowed treatment planning software to more accurately model patient specific heterogeneities. These algorithms model radiation transport in the appropriate tissue type, as opposed to water per TG 43, and give a better picture to the actual doses being delivered to patient. Multipledifferent MBDCA’s have been put forward including monte carlo methods, grid-based Boltzmann solvers (GBBS) and collapsed-cone convolution. Elekta Inc has developed the advanced collapse cone engine (ACE) and implemented this method into their Oncentra® Brachy software. A more robust description of these methods is discussed in. Rising in popularity in recent years, 3d printers allow the fabrication of objects through a process of additive manufacturing (AM). In this process, an object is built up by layers that are deposited sequentially, allowing for the construction of complex geometries . Two of the most common materials used in 3d construction is polylactic acid (PLA) and Acrylonitrile Butadiene Styrene (ABS). These two materials are thermoplastics that are extruded by the 3d printer in set path layer by layer and then cool and harden. The wide availability and low cost of 3d printers and materials has led to an interest in potential medical applications for this technology. In radiation oncology, this technology has been used in the fabrication of patient specific phantoms. This has been accomplished by varying the pattern of deposited material as well as filling any gaps created with material of varying densities and compositions. With proper care, these phantoms have been found to accurately represent patient specific properties that may allow them to become a regular part of a radiation oncology clinic. The goals of this project are multi-factorial. First, we will show how a patient specific phantom was created using FDM 3d printing. This phantom was then filled with different materials to approximate the composition of varying tissues and to create a heterogeneous phantom.
Secondly, we recreated the treatment plan and conditions of our patient and delivered an identical HDR treatment to our phantom and measured the resultant doses with radiochromic film. Lastly, we compared the measurements of our phantom to the TPS plans using both the TG-43 formalism and the newer ACE algorithm.
|School:||The University of Oklahoma Health Sciences Center|
|School Location:||United States -- Oklahoma|
|Source:||MAI 81/2(E), Masters Abstracts International|
|Keywords:||MBDCA, Grid-based Boltzmann solvers, Polylactic acid|
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