Next Generation Brain PET Imaging

下一代脑 PET 成像

基本信息

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
项目类别：
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

项目摘要

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.

抽象的金标准的定量成像研究通常很难实施，受财务和后勤问题的限制，或暴露于不必要的风险。近年来，深度学习对许多医学表现出了很大的希望申请；一种用途是合成改进的图像。这样的图像trans- 形成方法提供了提高质量，价值和医学成像的可访问性。该项目的目的是开发深层卷积神经网络 FDG PET成像的方法，是最常见的临床大脑在美国的重点研究。使用同时的宠物/MRI，我们将训练网络从超低剂量PET和MR产生诊断PET图像图像。我们将探讨这三个报销的临床指示使用两者都使用成像方式（肿瘤复发，痴呆和癫痫）定量指标和重复的读者研究，以评估等效性和评估可能与同时，扫描仪类型相关的AI概括偏差，年龄，性别和疾病患病率。接下来，我们将评估我们是否可以超越超低剂量并去除辐射剂量完全合成MR的FDG脑PET图像仅输入，依靠多模式功能MRI中的信息。最后，我们将评估我们是否可以使用深层网络结合成像和非 - 成像数据，例如临床和遗传信息，以进一步改善图像转换并预测未来图像和基于图像的生物标志物。显着减少甚至消除了辐射产生的需求大脑FDG PET图像将真正具有变革性预测未来将使个性化放射学并增强我们的能力进行临床试验。