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:9586688
- 负责人:
- 金额:$ 22.13万
- 依托单位:
- 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 2020-05-31
- 项目状态:已结题
- 来源:
- 关键词:Advanced DevelopmentAnatomyApplications GrantsBiological Neural NetworksCancer DetectionCardiologyCardiovascular DiseasesClinicClinicalComplexCore FacilityDataData SetDetectionDiseaseDoseFundingGenomicsGrantImageImaging TechniquesInjectionsLearningLesionMachine LearningMagnetic Resonance ImagingMalignant NeoplasmsMedical ImagingMethodsMolecularMorphologic artifactsMusNetwork-basedNeurologyNoiseOutputPathway interactionsPatientsPlayPositron-Emission TomographyRadiationRadioactive TracersRattusRoleSolidTimeTracerTrainingUse EffectivenessValidationWorkX-Ray Computed Tomographyanatomic imaginganimal databasecostdeep learningdeep neural networkfluorodeoxyglucosehuman dataimage reconstructionimaging modalityimprovedinnovationlearning strategymolecular imagingnervous system disordernonhuman primatenovel strategiesoncologysuccesstool
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.
项目总结/摘要
正电子发射断层扫描(PET)是一种广泛用于肿瘤学的高灵敏度分子成像模式,
神经病学和心脏病学,能够通过观察活体内的分子水平活动,
注射特定的放射性示踪剂。除了常用的F-18-FDG外,
不断开发和研究,以查明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,
以及扩展立体角覆盖。为了充分发挥新型PET示踪剂和扫描仪的潜力,
对先进的图像重建方法的开发的需求日益增加。这笔赠款
应用程序提出了一个新的框架,正则化图像重建,协同集成深
学习和正则化图像重建。新的框架是由最近的进展,
机器学习,它提供了一种工具来消化嵌入在现有医疗系统中的大量信息。
图像.所提出的方法将预训练的深度神经网络嵌入迭代图像重建中
框架,并使用深度神经网络直接正则化PET图像。通过训练深层神经
网络具有大量高质量低噪声PET图像,所提出的方法可以捕获复杂的
来自现有受试者间和受试者内数据的先验信息,因此预计
优于当前最先进的正则化图像重建方法。这两个具体目标
探索性的建议是(1)开发理论框架,以协同整合深度学习,
正则化PET图像重建和(2)实现所提出的方法,并验证其
使用现有的动物数据。一旦使用现有的动物数据验证了所提出的方法,
我们会申请拨款,以搜集所需的人类数据,
临床PET扫描仪
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('JINYI QI', 18)}}的其他基金
TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)
TRD3:数据分析和智能系统(AI-ML-DL-可视化)
- 批准号:
10649478 - 财政年份:2022
- 资助金额:
$ 22.13万 - 项目类别:
TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)
TRD3:数据分析和智能系统(AI-ML-DL-可视化)
- 批准号:
10424949 - 财政年份:2022
- 资助金额:
$ 22.13万 - 项目类别:
Positronium lifetime imaging using TOF PET
使用 TOF PET 进行正电子寿命成像
- 批准号:
10288242 - 财政年份:2021
- 资助金额:
$ 22.13万 - 项目类别:
Positronium lifetime imaging using TOF PET
使用 TOF PET 进行正电子寿命成像
- 批准号:
10443873 - 财政年份:2021
- 资助金额:
$ 22.13万 - 项目类别:
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
- 批准号:
9752639 - 财政年份:2018
- 资助金额:
$ 22.13万 - 项目类别:
Iterative Image reconstruction for high-resolution PET imaging
高分辨率 PET 成像的迭代图像重建
- 批准号:
7383846 - 财政年份:2007
- 资助金额:
$ 22.13万 - 项目类别:
Iterative Image reconstruction for high-resolution PET imaging
高分辨率 PET 成像的迭代图像重建
- 批准号:
7265565 - 财政年份:2007
- 资助金额:
$ 22.13万 - 项目类别:
Iterative Image reconstruction for high-resolution PET imaging
高分辨率 PET 成像的迭代图像重建
- 批准号:
7586255 - 财政年份:2007
- 资助金额:
$ 22.13万 - 项目类别:
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