KU Leuven, Department of Cardiovascular Sciences, Leuven, Belgium.
KU Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, Leuven, Belgium.
Phys Med Biol. 2022 May 27;67(11). doi: 10.1088/1361-6560/ac6cc3.
External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool fordosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts.. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset.The median DSC in the 5-fold cross validation was equal to ∼0.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by ∼30% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a -0.8 mm accuracy.BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.
外束放射治疗旨在精确地将高剂量辐射递送到恶性肿瘤，同时最大限度地保护周围健康组织。随着日益复杂的治疗计划的出现，最好通过质量保证方法对其进行验证。最近，已提出对汽化的放射敏纳米液滴进行在线超声成像，作为放射治疗剂量测定的一种很有前途的工具。此前，通过应用差分超声 (US) 成像并使用主观参数调整进行强度阈值处理来实现稀疏汽化事件的检测，这对图像伪影很敏感。提出了一种广义深度学习解决方案（即 BubbleNet），用于在差分 US 帧上定位汽化纳米液滴，同时克服了上述限制。通过手动分割对由不同成分组成的 5747 帧训练/验证数据集进行了 5 倍交叉验证。然后将 BubbleNet 应用于 1536 个差分 US 帧的测试数据集以评估剂量学特征。通过对两次分割的 150 个帧进行评分来确定观察者内可变性，以确定 Dice 相似系数 (DSC)。此外，还在使用相控阵换能器以低得多的超声频率获取的 432 个外部测试数据集上测试了 BubbleNet 的泛化能力，并且相对于训练数据集重建了非常规的像素尺寸。5 倍交叉验证的中位数 DSC 等于约 0.88，与观察者内可变性（=0.86）一致。接下来，在使用 154 MeV 质子束或 6 MV 光子束对体模进行照射期间，将 BubbleNet 用于检测差分 US 帧中的汽化。与以前建立的强度加权阈值相比，BubbleNet 将气泡计数统计提高了约 30%。BubbleNet 可以在实验获取的 US 图像上灵活地定位各个汽化的纳米液滴，与以前的加权阈值方法相比，提高了灵敏度。
