An adaptive spot placement method on Cartesian grid for pencil beam scanning proton therapy.

Autor:	Lin B; School of Mathematics, Shandong University, Jinan, People's Republic of China., Fu S; School of Mathematics, Shandong University, Jinan, People's Republic of China., Lin Y; Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, United States of America., Rotondo RL; Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, United States of America., Huang W; Department of Mathematics, University of Kansas, Lawrence, United States of America., Li HH; Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, United States of America., Chen RC; Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, United States of America., Gao H; Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, United States of America.
Jazyk:	angličtina
Zdroj:	Physics in medicine and biology [Phys Med Biol] 2021 Dec 02; Vol. 66 (23). Date of Electronic Publication: 2021 Dec 02.
DOI:	10.1088/1361-6560/ac3b65
Abstrakt:	Pencil beam scanning proton radiotherapy (RT) offers flexible proton spot placement near treatment targets for delivering tumoricidal radiation dose to tumor targets while sparing organs-at-risk. Currently the spot placement is mostly based on a non-adaptive sampling (NS) strategy on a Cartesian grid. However, the spot density or spacing during NS is a constant for the Cartesian grid that is independent of the geometry of tumor targets, and thus can be suboptimal in terms of plan quality (e.g. target dose conformality) and delivery efficiency (e.g. number of spots). This work develops an adaptive sampling (AS) spot placement method on the Cartesian grid that fully accounts for the geometry of tumor targets. Compared with NS, AS places (1) a relatively fine grid of spots at the boundary of tumor targets to account for the geometry of tumor targets and treatment uncertainties (setup and range uncertainty) for improving dose conformality, and (2) a relatively coarse grid of spots in the interior of tumor targets to reduce the number of spots for improving delivery efficiency and robustness to the minimum-minitor-unit (MMU) constraint. The results demonstrate that (1) AS achieved comparable plan quality with NS for regular MMU and substantially improved plan quality from NS for large MMU, using merely about 10% of spots from NS, where AS was derived from the same Cartesian grid as NS; (2) on the other hand, with similar number of spots, AS had better plan quality than NS consistently for regular and large MMU. (© 2021 Institute of Physics and Engineering in Medicine.)
Databáze:	MEDLINE
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