Gerlach Stefan, Fürweger Christoph, Hofmann Theresa, Schlaefer Alexander
Institute of Medical Technology, Hamburg University of Technology, Hamburg, 21073, Germany.
Europäisches Cyberknife Zentrum München-Großhadern, Munich, 81377, Germany.
Med Phys. 2020 Sep;47(9):3806-3815. doi: 10.1002/mp.14331. Epub 2020 Jul 8.
Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, treatment planning becomes a computationally challenging task as the search space for potential beam directions for dose delivery is arbitrarily large. We propose an approach based on deep learning to improve the search for treatment beams.
In clinical practice, a set of candidate beams generated by a randomized heuristic forms the basis for treatment planning. We use a convolutional neural network to identify promising candidate beams. Using radiological features of the patient, we predict the influence of a candidate beam on the delivered dose individually and let this prediction guide the selection of candidate beams. Features are represented as projections of the organ structures which are relevant during planning. Solutions to the inverse planning problem are generated for random and CNN-predicted candidate beams.
The coverage increases from 95.35% to 97.67% for 6000 heuristically and CNN-generated candidate beams, respectively. Conversely, a similar coverage can be achieved for treatment plans with half the number of candidate beams. This results in a patient-dependent reduced averaged computation time of 20.28%-45.69%. The number of active treatment beams can be reduced by 11.35% on average, which reduces treatment time. Constraining the maximum number of candidate beams per beam node can further improve the average coverage by 0.75 percentage points for 6000 candidate beams.
We show that deep learning based on radiological features can substantially improve treatment plan quality, reduce computation runtime, and treatment time compared to the heuristic approach used in clinics.
机器人放射外科手术提供了机械臂的灵活性，以实现对目标的高度适形和陡峭的剂量梯度。然而，由于用于剂量输送的潜在射束方向的搜索空间任意大，治疗计划成为一项计算上具有挑战性的任务。我们提出一种基于深度学习的方法来改进治疗射束的搜索。
在临床实践中，由随机启发式方法生成的一组候选射束构成治疗计划的基础。我们使用卷积神经网络来识别有前景的候选射束。利用患者的放射学特征，我们分别预测候选射束对所输送剂量的影响，并让这种预测指导候选射束的选择。特征表示为规划期间相关器官结构的投影。针对随机和经卷积神经网络预测的候选射束生成逆向规划问题的解决方案。
对于分别由启发式方法和卷积神经网络生成的6000个候选射束，覆盖率分别从95.35%提高到97.67%。相反，对于候选射束数量减半的治疗计划，可以实现类似的覆盖率。这导致患者依赖的平均计算时间减少20.28% - 45.69%。有效治疗射束的数量平均可减少11.35%，从而减少治疗时间。对每个射束节点的候选射束最大数量进行限制，对于6000个候选射束，可进一步将平均覆盖率提高0.75个百分点。
我们表明，与临床中使用的启发式方法相比，基于放射学特征的深度学习可以显著提高治疗计划质量、减少计算运行时间和治疗时间。
