Next Generation Brain PET Imaging

摘要 黄金标准的定量成像研究往往难以实施， 受财务和后勤问题的限制，或使患者暴露于不必要的 风险近年来，深度学习在许多医疗领域显示出巨大的潜力。 应用;一个用途是合成改进的图像。这样的形象trans- 形成方法提供了提高质量、价值和 医学影像的可及性。 该项目的目标是开发深度卷积神经网络 FDG PET成像的方法，最常见的临床脑- 在美国进行PET研究。使用同步PET/MRI，我们将训练 从超低剂量PET和MR生成诊断PET图像的网络 图像.我们将探讨这三个报销的临床适应症 成像模式（肿瘤复发、痴呆和癫痫）， 定量指标和重复阅片人研究，以评估等效性， 评价与重复性、扫描仪类型 年龄、性别和患病率。 接下来，我们将评估我们是否可以超越超低剂量， 放射剂量，从MR合成FDG脑PET图像 仅输入，依赖于多模态功能MRI中的信息。最后， 我们将评估我们是否可以使用深度网络来结合联合收割机成像和非成像， 成像数据，如临床和遗传信息，以进一步改善图像 转换和预测未来的图像和基于图像的生物标志物。 大大减少甚至消除了辐射的需要， 脑FDG PET图像将是真正的变革，而能力， 预测未来将实现个性化的放射学，并提高我们的能力， 进行临床试验。

Abstract Gold-standard quantitative imaging studies are often difficult to implement, limited by financial and logistical issues, or expose the patient to unnecessary risks. Deep learning has shown great promise in recent years for many medical applications; one use is to synthesize improved images. Such image trans- formation methods offer the potential to improve the quality, value, and accessibility of medical imaging. The goal of this project is to develop deep convolutional neural network approaches to FDG PET imaging, the most commonly performed clinical brain- focused PET study in the USA. Using simultaneous PET/MRI, we will train networks to produce diagnostic PET images from ultra-low dose PET and MR images. We will explore the three reimbursed clinical indications for this imaging modality (tumor recurrence, dementia, and epilepsy) using both quantitative metrics and repeated reader studies to assess equivalence and evaluate possible AI generalization bias related to simultaneity, scanner type, age, gender, and disease prevalence. Next, we will evaluate whether we can move beyond ultra-low dose and remove the radiation dose altogether, synthesizing FDG brain PET images from MR inputs only, relying on the information in multi-modal functional MRI. Finally, we will assess whether we can use deep networks to combine imaging and non- imaging data such as clinical and genetic information to further improve image transformation and predict future images and image-based biomarkers. Significantly reducing or even eliminating the need for radiation to produce brain FDG PET images would be truly transformative while the ability to predict the future will enable personalized radiology and enhance our ability to perform clinical trials.