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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)是一种广泛应用于肿瘤学的高灵敏度分子成像手段，神经学和心脏病学，有能力通过观察活体内分子水平的活动注射特定的放射性示踪剂。除了常用的F-18-FDG外，正在开发新的示踪剂不断发展和研究，以确定各种疾病的具体途径。新型PET扫描仪还通过利用飞行时间(TOF)信息，实现交互能力的深度，扩大了立体角度覆盖范围。为了充分发挥新的PET示踪剂和扫描仪的潜力，对发展先进的图像重建方法的需求越来越大。这笔赠款应用提出了一种协同集成深度的正则化图像重建新框架学习和正则化图像重建。新的框架是由以下方面的最新进展促成的机器学习，它提供了一种工具来消化嵌入在现有医学中的大量信息图像。该方法将预先训练好的深度神经网络嵌入到迭代图像重建中框架，并直接使用深度神经网络对PET图像进行正则化。通过训练深层神经网络中含有大量高质量的低噪声PET图像，该方法可以捕捉到复杂的图像来自现有的受试者间和受试者内数据的先验信息，因此预计基本上优于目前最先进的正则化图像重建方法。这样做的两个具体目标探索性建议是(1)开发理论框架，以协同方式整合深度学习在正则化正则化PET图像重建算法的实现与验证使用现有动物数据的有效性。一旦使用现有的动物数据验证了所提出的方法，我们会寻求拨款，以取得所需的人体数据，以便在临床PET扫描仪。