Medical Physics Graduate Program, Duke University, Durham, NC 27705, United States of America.
Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27710, United States of America.
Phys Med Biol. 2022 Oct 27;67(21). doi: 10.1088/1361-6560/ac9881.
Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-time3D dose verification. However, its dosimetry accuracy is limited due to the limited-angle view of the ultrasound transducer. In this study, we developed a deep learning-based method to address the limited-view issue in the PA reconstruction. A deep cascaded convolutional neural network (DC-CNN) was proposed to reconstruct 3D high-quality radiation-induced pressures using PA signals detected by a matrix array, and then derive precise 3D dosimetry from pressures for dose verification in proton therapy. To validate its performance, we collected 81 prostate cancer patients' proton therapy treatment plans. Dose was calculated using the commercial software RayStation and was normalized to the maximum dose. The PA simulation was performed using the open-source k-wave package. A matrix ultrasound array with 64 × 64 sensors and 500 kHz central frequency was simulated near the perineum to acquire radiofrequency (RF) signals during dose delivery. For realistic acoustic simulations, tissue heterogeneity and attenuation were considered, and Gaussian white noise was added to the acquired RF signals. The proposed DC-CNN was trained on 204 samples from 69 patients and tested on 26 samples from 12 other patients. Predicted 3D pressures and dose maps were compared against the ground truth qualitatively and quantitatively using root-mean-squared-error (RMSE), gamma-index (GI), and dice coefficient of isodose lines. Results demonstrated that the proposed method considerably improved the limited-view PA image quality, reconstructing pressures with clear and accurate structures and deriving doses with a high agreement with the ground truth. Quantitatively, the pressure accuracy achieved an RMSE of 0.061, and the dose accuracy achieved an RMSE of 0.044, GI (3%/3 mm) of 93.71%, and 90%-isodose line dice of 0.922. The proposed method demonstrates the feasibility of achieving high-quality quantitative 3D dosimetry in PA imaging using a matrix array, which potentially enables the online 3D dose verification for prostate proton therapy.
剂量传递不确定性是质子治疗中的一个主要关注点，会对治疗精度和结果产生不利影响。最近，一种有前途的技术——质子声（PA）成象技术已经被开发出来，用于提供实时 3D 剂量验证。然而，由于超声换能器的有限视角，其剂量测量精度受到限制。在这项研究中，我们开发了一种基于深度学习的方法来解决 PA 重建中的有限视角问题。提出了一种深度级联卷积神经网络（DC-CNN），使用矩阵阵列检测到的 PA 信号来重建 3D 高质量辐射诱导压力，并从压力中得出精确的 3D 剂量，用于质子治疗中的剂量验证。为了验证其性能，我们收集了 81 例前列腺癌患者的质子治疗计划。使用商业软件 RayStation 计算剂量，并将剂量归一化为最大剂量。使用开源 k-wave 包进行 PA 模拟。在会阴部附近模拟了一个具有 64×64 个传感器和 500 kHz 中心频率的矩阵超声阵列，以在剂量输送过程中获取射频（RF）信号。为了进行现实的声学模拟，考虑了组织异质性和衰减，并在获得的 RF 信号中添加了高斯白噪声。在 69 名患者的 204 个样本和 12 名其他患者的 26 个样本上对提出的 DC-CNN 进行了训练和测试。定性和定量地使用均方根误差（RMSE）、伽马指数（GI）和等剂量线的骰子系数比较了预测的 3D 压力和剂量图与真实值。结果表明，该方法显著提高了有限视角 PA 图像的质量，重建了具有清晰准确结构的压力，并得出了与真实值高度一致的剂量。定量地，压力精度达到 RMSE 为 0.061，剂量精度达到 RMSE 为 0.044，GI（3%/3mm）为 93.71%，90%-等剂量线骰子为 0.922。该方法证明了使用矩阵阵列在 PA 成象中实现高质量定量 3D 剂量测量的可行性，这可能使前列腺质子治疗的在线 3D 剂量验证成为可能。
