DataSheet1_Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy.PDF

We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded “bad” PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of “good” data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify “good” and “bad” PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (∼1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ∼5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification.

本研究探究了深度全连接神经网络（Neural Network, NN）在处理临床质子放疗束递送过程中，康普顿相机（Compton Camera, CC）所测得的瞬发伽马（prompt gamma, PG）数据中的应用场景。该网络可实现两大功能：其一，识别测量过程中由背景噪声引发的无效PG事件；其二，确定PG相互作用在康普顿相机内的正确顺序，以提升用于图像重建的有效PG数据的保真度。研究采用原型康普顿相机，对以临床剂量率递送的150 MeV质子束辐照组织等效靶材时产生的瞬发伽马信号进行了测量。分别基于原始实测数据，以及经训练用于区分有效与无效PG事件、并预测有效PG事件内各相互作用顺序的神经网络处理后的实测数据，完成了图像重建。本研究旨在验证：对康普顿相机采集的数据进行神经网络预处理，能否将重建得到的PG图像提升至可提供临床有效信息的水平，从而实现临床全剂量率递送质子束的体内射程及射程偏移检测。实验结果表明，深度全连接神经网络可将图像的对比度噪声比（contrast to noise ratio, CNR）提升8倍以上。这使得即便在临床治疗所用的最高剂量率150 MeV质子束条件下，组织等效靶材内临床质子束的路径、射程及横向宽度，仍可通过PG图像轻松识别。平均而言，最小可检测的光束射程偏移可达3 mm。但当受限于单次质子笔形束（约1×10^9个质子）递送过程中，原型康普顿相机所采集的PG数据量时，重建PG图像的不确定性将射程偏移的可识别精度限制至约5 mm。通过对实测PG数据进行神经网络预处理，临床束流递送过程中的康普顿相机图像质量得到了显著提升。我们认为，该结果证实了神经网络可助力优化基于康普顿相机的瞬发伽马成像，并推动其最终应用于质子放疗治疗递送验证的临床场景。