Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy
Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy
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使用深度学习对质子放射治疗中基于康普顿相机的瞬发伽玛成像进行分类
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发表时间：
2020
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通讯作者：
J. Polf
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作者：
Jonathan N. Basalyga;Gerson C. Kroiz;C. Barajas;M. Gobbert;P. Maggi;J. Polf
实时成像有可能大大提高质子束治疗癌症的有效性。一种有前途的实时成像方法是使用康普顿照相机来探测瞬发伽马射线，该伽马射线沿光束的路径沿着发射，以便重建它们的起源。然而，由于康普顿照相机探测瞬发伽马射线的能力有限，数据往往是模糊的，使得基于它们的重建无法用于实际目的。深度学习能够检测到传统模型无法使用的数据中的细微之处，这使其成为改进康普顿相机数据分类的可能候选者之一。我们表明，一个适当设计的神经网络可以减少错误的检测和误序的相互作用，从而提高重建质量。
Real-time imaging has potential to greatly increase the effectiveness of proton beam therapy for cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted along the path of the beam, in order to reconstruct their origin. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the data are often ambiguous, making reconstructions based on them unusable for practical purposes. Deep learning’s ability to detect subtleties in data that traditional models do not use make it one possible candidate for the improvement of classification of Compton camera data. We show that a suitably designed neural network can reduce false detections and misorderings of interactions, thereby improving reconstruction quality.