Developing Enabling PET-CT Image Analysis Tools for Predicting Response in Radiation Cancer Therapy

摘要 集成的正电子发射断层扫描和计算机断层扫描（PET-CT）已成为 现代癌症治疗中不可或缺的工具。准确的目标划定是不可避免的第一步 充分发挥PET-CT的潜力。然而，在目前的临床实践中， 重要任务通常是在非常有限的 自动分割工具。最先进的PET-CT分割技术依赖于 单一模态或融合PET-CT数据，可能无法充分利用两种模态，因此 损害了分割精度。此外，最先进的治疗反应 预测方法高度依赖于手工制作的图像特征和参数， 限制了它们在临床上的广泛应用。本研究旨在开发快速、客观的PET-CT 分析方法，以促进利用双模态成像的大规模临床 试验研究和日常临床护理。所提出的方法的新功能是第一次， 为PET-CT肿瘤描绘引入共分割，其识别 PET和CT中的肿瘤将探索新的PET-CT特异性先验并纳入 分割框架，进一步提高了分割的准确性。拟议 反应预测方法是建立在我们的PET-CT联合诊断的准确肿瘤定义基础上的， 分割方法，采用卷积神经网络的创新设计，自动 直接从PET-CT扫描中学习分层特征，从而高度准确地预测 反应所开发的方法将进行测试，比较国家的最先进的方法利用 今天将在足够的数据样本中对方法的性能进行统计学评估。 尺寸.

ABSTRACT The integrated Positron Emission Tomography and Computed Tomography (PET-CT) has become an indispensable tool in modern cancer therapy. Accurate target delineation is an inevitable first step towards fully making use of the potentials of PET-CT. However, in current clinical practice, this important task is typically performed visually on a slice-by-slice basis with very limited support of automated segmentation tools. The state-of-the-art PET-CT segmentation techniques rely on either a single modality or the fused PET-CT data, which may not fully take advantage of both modalities, thus compromising the segmentation accuracy. In addition, the state-of-the-art therapeutic response prediction methods highly rely on the handcrafted image features and parameters, which poses a limiting factor for their wide use in clinic. This research proposes to develop fast and objective PET-CT analysis methods to facilitate the utilization of the dual modality imaging for both large-scale clinical trial research and daily clinical care. The novel feature of the proposed methods is the first time to introduce co-segmentation for PET-CT tumor delineation, which recognizes the contour difference of tumors in PET from those in CT. New PET-CT specific priors will be explored and incorporated into the segmentation framework, further improving the accuracy of segmentation. The proposed response prediction method is built on the accurate tumor definition from our PET-CT co- segmentation approach, with an innovative design of a convolutional neural network for automatically learning hierarchical features directly from the PET-CT scans, leading to highly accurate prediction of response. The developed methods will be tested in comparison with state-of-the-art methods utilized today. The performance of the methods will be statistically assessed in data samples of sufficient sizes.