Abstract：
Objective.The aim of this work was to develop a novel artificial intelligence-assistedin vivodosimetry method using time-resolved (TR) dose verification data to improve quality of external beam radiotherapy.Approach. Although threshold classification methods are commonly used in error classification, they may lead to missing errors due to the loss of information resulting from the compression of multi-dimensional electronic portal imaging device (EPID) data into one or a few numbers. Recent research has investigated the classification of errors on time-integrated (TI)in vivoEPID images, with convolutional neural networks showing promise. However, it has been observed previously that TI approaches may cancel out the error presence onγ-maps during dynamic treatments. To address this limitation, simulated TRγ-maps for each volumetric modulated arc radiotherapy angle were used to detect treatment errors caused by complex patient geometries and beam arrangements. Typically, such images can be interpreted as a set of segments where only set class labels are provided. Inspired by recent weakly supervised approaches on histopathology images, we implemented a transformer based multiple instance learning approach and utilized transfer learning from TI to TRγ-maps.Main results. The proposed algorithm performed well on classification of error type and error magnitude. The accuracy in the test set was up to 0.94 and 0.81 for 11 (error type) and 22 (error magnitude) classes of treatment errors, respectively.Significance. TR dose distributions can enhance treatment delivery decision-making, however manual data analysis is nearly impossible due to the complexity and quantity of this data. Our proposed model efficiently handles data complexity, substantially improving treatment error classification compared to models that leverage TI data.
摘要：
目的：这项工作的目的是开发一种新颖的AI辅助体内剂量测定（IVD）方法，该方法使用时间分辨的剂量验证数据来提高外部束放射治疗的质量。
方法。尽管阈值分类方法通常用于错误分类，由于将多维电子射野成像设备（EPID）数据压缩为一个或几个数字而导致信息丢失，因此它们可能会导致丢失错误。最近的研究已经调查了活体EPID图像中时间积分（TI）的错误分类，卷积神经网络显示出希望。然而,以前已经观察到，TI方法可以抵消在动态治疗期间γ-图上的误差存在。为了解决这个限制，每个VMAT角度的模拟时间分辨（TR）γ图用于检测由复杂的患者几何结构和束布置引起的治疗误差。通常,这样的图像可以被解释为仅提供集合类标签的一组段。受最近对组织病理学图像的弱监督方法的启发，我们实现了基于变压器的多实例学习（MIL）方法，并利用了从TI到TRγ图的迁移学习。
主要结果。该算法在误差类型和误差大小的分类上表现良好。对于11类（错误类型）和22类（错误幅度）的治疗错误，测试集的准确性分别达到0.94和0.81，分别。
意义。TR剂量分布可以增强治疗交付决策，然而，由于这些数据的复杂性和数量，手动数据分析几乎是不可能的。我们提出的模型有效地处理数据复杂性，与利用TI数据的模型相比，大幅改进了处理错误分类。
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