Improvement of flattenability using particle swarm optimizer for surface unfolding in bolus shaping

Autor: Rui Li, Qingjin Peng, Harry Ingleby, David Sasaki
Rok vydání: 2020
Předmět:
Zdroj: SN Applied Sciences. 2
ISSN: 2523-3971
2523-3963
DOI: 10.1007/s42452-020-03330-9
Popis: Bolus, formed by a sheet of material, covers on tumors of patient skins to generate the desired dose distribution in a process of the high-energy radiotherapy. The existing methods of bolus shaping use a manual process to cut the commercial material into the shape, which can cause air gaps between the patient skin surface and bolus in the reduced effect of cancer care treatment. A method of automatic bolus shaping can improve the accuracy and reduce air gaps of bolus shaping, which uses a process of first segmenting 3D patient skin surface models into several patches and then unfolding the patches into 2D patterns for material cutting before folding the cut sheet into the bolus. However, air gaps between the bolus and patient skin surface cannot be directly evaluated to find the bolus accuracy. This paper proposes a method to improve model flattenability and evaluate the air gaps using a particle swarm optimizer (PSO). A 3D surface shape is unfolded only if the air gap and surface flattenability meet the requirement of accuracy. Otherwise, the surface will be segmented into several patches to improve the flattenability and reduce air gaps. The objective and constraint are identified to search for an optimal solution for the 3D surface with a high flattenability. A strategy of the dimension reduction is proposed for the selection of local nodes on a meshed surface to increase the searching efficiency of surface flattenability. A local node selection based PSO (L-PSO) method is developed to search for the optimal solution. The proposed method is verified in two case studies of bolus shaping.
Databáze: OpenAIRE
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