Abstract
To automatically identify optimal beam angles for proton therapy configured with the double-scattering delivery technique, a beam angle optimization method based on a convolutional neural network (BAODS-Net) is proposed. Fifty liver plans were used for training in BAODS-Net. To generate a sequence of input data, 25 rays on the eye view of the beam were determined per angle. Each ray collects nine features, including the normalized Hounsfield unit and the position information of eight structures per 2° of gantry angle. The outputs are a set of beam angle ranking scores (S beam) ranging from 0° to 359°, with a step size of 1°. Based on these input and output designs, BAODS-Net consists of eight convolution layers and four fully connected layers. To evaluate the plan qualities of deep-learning, equi-spaced, and clinical plans, we compared the performances of three types of loss functions and performed K-fold cross-validation (K = 5). For statistical analysis, the volumes V27Gy and V30Gy as well as the mean, minimum, and maximum doses were calculated for organs-at-risk by using a paired-samples t-test. As a result, smooth-L1 loss showed the best optimization performance. At the end of the training procedure, the mean squared errors between the reference and predicted S beam were 0.031, 0.011, and 0.004 for L1, L2, and smooth-L1 loss, respectively. In terms of the plan quality, statistically, PlanBAO has no significant difference from PlanClinic (P >.05). In our test, a deep-learning based beam angle optimization method for proton double-scattering treatments was developed and verified. Using Eclipse API and BAODS-Net, a plan with clinically acceptable quality was created within 5 min.
Highlights
Interest in beam angle optimization (BAO) research has been on the rise recently again
The conventional BAO process for the intensity-modulated radiotherapy (IMRT) plan is based on trial-and-error searching by a planner; the optimal beam angle is affected by the experience and understanding of the treatment planning system (TPS) of the planner [1]
We developed a deep-learning based BAO method for the three-ports proton double-scattering (DS) technique using the geometric information of the patient computed tomography (CT) anatomy and Hounsfield unit (HU) data as well as a convolutional neural network (CNN)
Summary
Interest in beam angle optimization (BAO) research has been on the rise recently again. The conventional BAO process for the IMRT plan is based on trial-and-error searching by a planner; the optimal beam angle is affected by the experience and understanding of the treatment planning system (TPS) of the planner [1]. Various studies for BAO have been conducted to reduce the workload of treatment planning and decrease the planning time. These BAO studies incorporated techniques such as simulated annealing [2,3,4,5,6,7,8,9], geometric information scoring [10,11,12,13,14,15,16,17,18,19], gradient descent [20,21,22,23,24,25], genetic algorithms [26,27,28,29], and neural networks [30,31,32,33,34]. The advent of volumetric-modulated arc therapy and the templatization of the radiation treatment plan, including dose prescription and gantry angles, have reduced interest in BAO research for X-ray therapy
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