Next Generation Brain PET Imaging

下一代脑 PET 成像

• 批准号: 10279862
• 负责人: Gregory George Zaharchuk
• 金额: $ 56.46万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10279862
• 关键词: Age; Air; Architecture; Artificial Intelligence; Brain; Brain imaging; Cerebrovascular Circulation; Characteristics; Clinical; Clinical Data; Clinical Trials; Cyclotrons; Data; Data Set; Dementia; Diagnosis; Diagnostic; Diagnostic Imaging; Discipline of Nuclear Medicine; Disease; Disease Progression; Dose; Epilepsy; FDA approved; Functional Magnetic Resonance Imaging; Future; Gender; Genetic; Genetic Markers; Glioblastoma; Goals; Gold; Human; Image; Link; Logistics; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Medical; Medical Genetics; Medical Imaging; Metabolic; Metabolism; Methods; Modality; Modeling; Patients; Performance; Phenotype; Physicians; Population; Positron-Emission Tomography; Prevalence; Protocols documentation; Provider; Radiation; Radiation Dose Unit; Radiology Specialty; Reader; Recording of previous events; Recurrence; Risk; Risk Factors; Running; Rural Community; Site; Specific qualifier value; Spin Labels; Structure; Technology; Tracer; Training; Travel; Water; acute stroke; base; chronic stroke; convolutional neural network; cost; deep learning; disease natural history; fluorodeoxyglucose; fluorodeoxyglucose positron emission tomography; genetic information; imaging biomarker; imaging modality; imaging study; improved; metabolic imaging; multimodal data; multimodality; neural network; next generation; patient population; patient safety; prospective; quantitative imaging; radiologist; radiotracer; sex; stroke patient; targeted imaging; tumor; underserved community

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.

摘要 黄金标准的定量成像研究通常很难实施， 受限于财务和后勤问题，或使患者面临不必要的风险 风险。近年来，深度学习在许多医学领域显示出了巨大的前景 应用程序；其中一个用途是合成改进的图像。这样的形象跨越了 形成方法提供了提高质量、价值和 医学成像的可获得性。 本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- 专注于美国的PET研究。使用同步的PET/MRI，我们将训练 利用超低剂量PET和MR生成诊断PET图像的网络 图像。我们将探讨这三种报销的临床适应症。 使用两者的成像方式(肿瘤复发、痴呆和癫痫) 量化指标和重复的读者研究，以评估等价性和 评估与同时性、扫描仪类型 年龄、性别和疾病流行率。 接下来，我们将评估我们是否可以超越超低剂量并移除 从MR合成FDG脑PET图像 仅输入，依赖于多模式功能磁共振中的信息。最后， 我们将评估是否可以使用深度网络将成像和非 临床和遗传信息等影像数据，进一步改善影像 转换和预测未来的图像和基于图像的生物标记物。 显著减少甚至消除产生辐射的需要 脑FDG PET图像将真正实现变革，同时能够 预测未来将实现个性化放射学并增强我们的能力 进行临床试验。