arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.04375physics.med-ph

角度依赖剂量变换算法用于快速质子治疗剂量计算

Angle dependent dose transformer algorithm for fast proton therapy dose calculations

Mikołaj Stryja, Danny Lathouwers, Zoltán Perkó

AI总结:

ADoTA是一种基于深度学习的质子剂量计算算法,通过角度依赖性投影消除网格旋转,提升计算效率并保持高精度。

摘要:

对于质子笔扫质子治疗(PBSPT)的准确三维剂量计算通常使用蒙特卡洛(MC)引擎,但其运行时间限制了自适应工作流程和重复评估。当前的深度学习质子剂量引擎通常需要质子射线与CT网格的正交性,迫使计算成本高昂的束元级三维重新插值。我们提出了角度依赖剂量变换算法(ADoTA),该算法通过在模型输入中增加快速分析束元形状投影来消除网格旋转,该投影明确编码了束方向。模型在108名患者的CT数据上进行训练,以预测初始能量为70-270 MeV的束元剂量分布,覆盖80×110 mm²的区域,并在独立的50名患者队列上进行测试。在测试集上,γ通过率(1%,3 mm)为胸腔99.40±0.86%和腹部/盆腔99.87±0.23%。单束元推断时间约为1.72±0.8 ms。通过避免重新插值,端到端的三维剂量计算减少了约86%,相对于最快的已发表重新插值方法。对于完整的治疗计划,γ通过率(2%,2 mm)在10%的剂量截止下达到肺部98.4%和前列腺98.9%。ADoTA提供了一个具有角度意识的深度学习质子剂量引擎,能够在异质解剖结构中保持MC级的精度,同时显著减少计算开销。

英文摘要:

Accurate 3D dose calculation for Pencil Beam Scanning Proton Therapy (PBSPT) is typically performed with Monte Carlo (MC) engines, but their runtimes limit adaptive workflows and repeated evaluations. Current deep-learning proton dose engines often require orthogonality between proton rays and the CT grid, forcing computationally expensive beamlet-wise 3D reinterpolation. We propose the Angle-dependent Dose Transformer Algorithm (ADoTA), which eliminates grid rotation by augmenting the model input with a fast analytical beamlet-shape projection that explicitly encodes beam direction. The model was trained on CT data from 108 patients to predict beamlet dose distributions for initial energies of $70$--$270\,\mathrm{MeV}$ over an $80\times110\,\mathrm{mm}^2$ field, and tested on an independent cohort of 50 patients. On the test set, gamma pass rates $(1\%,3\,\mathrm{mm})$ were $99.40\pm0.86\%$ (thorax) and $99.87\pm0.23\%$ (abdomen/pelvis). Single-beamlet inference took $1.72\pm0.8\,\mathrm{ms}$. By avoiding reinterpolation, end-to-end 3D dose computation was reduced by $\approx86\%$ relative to the fastest published reinterpolation-based methods. For full treatment plans, gamma pass rates $Γ(2\%,2\,\mathrm{mm})$ with a 10\% dose cut-off reached $98.4\%$ (lung) and $98.9\%$ (prostate). ADoTA provides an angle-aware deep-learning proton dose engine that preserves MC-level accuracy across heterogeneous anatomies while substantially reducing computational overhead.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑