TEAM: Triangular‐mEsh Adaptive and Multiscale proton spot generation method

• Published in: Medical physics (Lancaster) Vol. 51; no. 10; pp. 7067 - 7079
• Main Authors: Wang, Chao, Lin, Bowen, Lin, Yuting, Shontz, Suzanne M., Huang, Weizhang, Chen, Ronald C., Gao, Hao
• Format: Journal Article
• Language: English
• Published: United States 01.10.2024
• Subjects: adaptive; Humans; multiscale; proton therapy; Proton Therapy - methods; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; spot generation; triangular mesh; spot generation; adaptive; triangular mesh; proton therapy; multiscale
• ISSN: 0094-2405; 2473-4209; 2473-4209
• DOI: 10.1002/mp.17352

Background Proton therapy is preferred for its dose conformality to spare normal tissues and organs‐at‐risk (OAR) via Bragg peaks with negligible exit dose. However, proton dose conformality can be further optimized: (1) the spot placement is based on the structured (e.g., Cartesian) grid, which may not offer conformal shaping to complex tumor targets; (2) the spot sampling pattern is uniform, which may be insufficient at the tumor boundary to provide the sharp dose falloff, and at the same time may be redundant at the tumor interior to provide the uniform dose coverage, for example, due to multiple Coulomb scattering (MCS); and (3) the lateral spot penumbra increases with respect to the depth due to MCS, which blurs the lateral dose falloff. On the other hand, while (1) the deliverable spots are subject to the minimum‐monitor‐unit (MMU) constraint, and (2) the dose rate is proportional to the MMU threshold, the current spot sampling method is sensitive to the MMU threshold and can fail to provide satisfactory plan quality for a large MMU threshold (i.e., high‐dose‐rate delivery). Purpose This work will develop a novel Triangular‐mEsh‐based Adaptive and Multiscale (TEAM) proton spot generation method to address these issues for optimizing proton dose conformality and plan delivery efficiency. Methods Compared to the standard clinically‐used spot placement method, three key elements of TEAM are as follows: (1) a triangular mesh instead of a structured grid: the triangular mesh is geometrically more conformal to complex target shapes and therefore more efficient and accurate for dose shaping inside and around the target; (2) adaptive sampling instead of uniform sampling: the adaptive sampling consists of relatively dense sampling at the tumor boundary to create the sharp dose falloff, which is more accurate, and coarse sampling at the tumor interior to uniformly cover the target, which is more efficient; and (3) depth‐dependent sampling instead of depth‐independent sampling: the depth‐dependent sampling is used to compensate for MCS, that is, with increasingly dense sampling at the tumor boundary to improve dose shaping accuracy, and increasingly coarse sampling at the tumor interior to improve dose shaping efficiency, as the depth increases. In the TEAM method the spot locations are generated for each energy layer and layer‐by‐layer in the multiscale fashion; and then the spot weights are derived by solving the IMPT problem of dose‐volume planning objectives, MMU constraints, and robustness optimization with respect to range and setup uncertainties. Results Compared to the standard clinically‐used spot placement method UNIFORM, TEAM achieved (1) better plan quality using <60% number of spots of UNIFORM; (2) better robustness to the number of spots; (3) better robustness to a large MMU threshold. Furthermore, TEAM provided better plan quality with fewer spots than other adaptive methods (Cartesian‐grid or triangular‐mesh). Conclusions A novel triangular‐mesh‐based proton spot placement method called TEAM is proposed, and it is demonstrated to improve plan quality, robustness to the number of spots, and robustness to the MMU threshold, compared to the clinically‐used spot placement method and other adaptive methods.