Synergistic integration of deep learning and regularized image reconstruction for positron emission tomography

深度学习与正电子发射断层扫描正则化图像重建的协同集成

基本信息

批准号：
9752639
负责人：
JINYI QI
金额：
$ 19.63万
依托单位：
UNIVERSITY OF CALIFORNIA AT DAVIS
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2021-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9752639
关键词：
Advanced Development Anatomy Applications Grants Cancer Detection Cardiology Cardiovascular Diseases Clinic Clinical Complex Core Facility Data Data Set Detection Disease Funding Genomics Grant Image Imaging Techniques Injections Learning Lesion Machine Learning Magnetic Resonance Imaging Malignant Neoplasms Medical Imaging Methods Molecular Morphologic artifacts Mus Network-based Neurology Noise Output Pathway interactions Patients Play Positron-Emission Tomography Radiation Dose Unit Radioactive Tracers Rattus Role Solid Time Tracer Training Use Effectiveness Validation Work X-Ray Computed Tomography anatomic imaging animal data base cost deep learning deep neural network fluorodeoxyglucose human data image reconstruction imaging modality improved innovation learning strategy molecular imaging nervous system disorder neural network nonhuman primate novel strategies oncology success tool

项目摘要

Project Summary/Abstract Positron emission tomography (PET) is a high-sensitivity molecular imaging modality widely used in oncology, neurology, and cardiology, with the ability to observe molecular-level activities inside a living body through the injection of specific radioactive tracers. In addition to the commonly used F-18-FDG, new tracers are being constantly developed and investigated to pinpoint specific pathways in various diseases. New PET scanners are also being proposed by exploiting time of flight (TOF) information, enabling depth of interaction capability, and extending the solid angle coverage. To realize the full potential of the new PET tracers and scanners, there is an increasing need for the development of advanced image reconstruction methods. This grant application proposes a new framework for regularized image reconstruction that synergistically integrates deep learning and regularized image reconstruction. The new framework is enabled by the recent advances in machine learning, which provide a tool to digest vast amount information embedded in existing medical images. The proposed method embeds a pre-trained deep neural network in an iterative image reconstruction framework and uses the deep neural network to regularize PET image directly. By training the deep neural network with a large amount of high-quality low-noise PET images, the proposed method can capture complex prior information from existing inter-subject and intra-subject data and thus is expected to substantially outperform the current state-of-the-art regularized image reconstruction method. The two specific aims of this exploratory proposal are (1) to develop the theoretical framework to synergistically integrate deep learning in regularized image reconstruction for PET and (2) to implement the proposed method and validate its effectiveness using existing animal data. Once the proposed method is validated using existing animal data, we will seek funding to acquire necessary human data for the implementation of the proposed method on clinical PET scanners.

项目摘要/摘要正电子发射断层扫描（PET）是一种高灵敏的分子成像方式，广泛用于肿瘤学，神经病学和心脏病学，能够通过观察活体内的分子级活动。注入特定的放射性示踪剂。除了常用的F-18-FDG外，还在不断开发和研究以查明各种疾病中的特定途径。新的宠物扫描仪还通过利用飞行时间（TOF）信息提出，使互动能力深度并扩大实体角度覆盖范围。为了实现新的宠物示踪剂和扫描仪的全部潜力，对高级图像重建方法的开发越来越需要。这笔赠款应用程序为正规图像重建提供了一个新的框架，该框架协同整合了深度学习和正规图像重建。新框架是由最近的进步来实现的机器学习，它提供了一种工具来消化现有医疗中嵌入的大量信息图像。所提出的方法将预先训练的深神网络嵌入了迭代图像重建中框架并使用深神网络直接使PET图像正常。通过训练深层神经具有大量高质量低噪声宠物图像的网络，该方法可以捕获复杂现有受试者间和受试者内数据的先前信息，因此有望实质上优于当前最新的正规图像重建方法。这两个具体目标探索性建议是（1）开发理论框架以协同整合深度学习 PET的正则图像重建和（2）实施该方法并验证其使用现有动物数据的有效性。一旦使用现有动物数据验证了提出的方法，我们将寻求资金以获取必要的人类数据，以实施拟议的方法临床宠物扫描仪。