AUTHOR=Cheon Wonjoong , Ahn Sang Hee , Jeong Seonghoon , Lee Se Byeong , Shin Dongho , Lim Young Kyung , Jeong Jong Hwi , Youn Sang Hee , Lee Sung Uk , Moon Sung Ho , Kim Tae Hyun , Kim Haksoo 

TITLE=Beam Angle Optimization for Double-Scattering Proton Delivery Technique Using an Eclipse Application Programming Interface and Convolutional Neural Network

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.707464

DOI=10.3389/fonc.2021.707464

ISSN=2234-943X

ABSTRACT=To automatically identify optimal beam angles for proton therapy configured with the double-scattering delivery technique, a beam angle optimisation network based on a convolutional neural network (BAODS-Net) is proposed. Fifty liver plans were used for training BAODS-Net. To generate a sequence of input data, twenty-five rays on the beam’s eye view were determined per angle. Each ray collects nine features, including the normalised Hounsfield unit and the position information of eight structures per two degrees of gantry angle. The outputs are a set of beam angle ranking scores (Sbeam) ranging from 0° to 359° with a step size of one degree. Based on these input and output designs, BAODS-Net consists of eight convolution layers and three 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 V27 Gy and V30 Gy 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 optimisation performance. At the end of the training procedure, the mean squared errors between the reference and predicted Sbeam 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 BAO 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.