Development of a Rapid Method for Imaging Regional Ventilation in Small Animals w/o Contrast Agents
开发一种无需造影剂的小动物局部通气成像快速方法
基本信息
- 批准号:9888370
- 负责人:
- 金额:$ 41.98万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-08-28 至 2022-02-28
- 项目状态:已结题
- 来源:
- 关键词:AddressAirAnimal Disease ModelsAnimal ModelAnimalsBreathingCommunitiesContrast MediaDevelopmentDiagnosticEvaluationFunctional ImagingImageImaging technologyLongitudinal StudiesLow Dose RadiationLungLung ComplianceLung diseasesMagnetic Resonance ImagingMapsMeasuresMethodsMonitorMotionMusPathologyPerformancePhasePhysicsPhysiologyPlethysmographyProcessPulmonary EmphysemaPulmonary function testsRadiationRadiation Dose UnitResearchResolutionResource SharingRespiratory physiologyRoentgen RaysScientistSourceStructureSystemTechnical DegreeTechniquesTextureThinnessTimeTissuesTranslatinganimal imagingbasecomputer studiescontrast imagingcostdetectordrug discoverydrug efficacyefficacy studyexperiencefallsimaging modalityimaging systemimprovedin vivoin vivo monitoringinnovationlung imaginglung injurylung pressurelung volumemachine learning methodmicroCTmouse modelnovelparametric imagingpre-clinicalpressurerapid techniquerespiratorysupervised learningventilation
项目摘要
The objective of this R01 application is to develop a rapid method for imaging regional ventilation and
lung compliance in small animals without contrast agents. Much of our current understanding of the
normal functioning of the lung and mechanisms of lung disease comes from small animal studies. However,
lung function imaging in small animal models is technically challenging due to motion and the relatively small
size of the lungs. Pulmonary function testing using plethysmography has been employed to assess lung
function and injury with limited validity and utility, particularly in small animals. Additionally, only aggregate
measures of functional performance are produced and no regional lung changes can be assessed. An
improved imaging method that could provide spatially- and temporally-resolved information regarding
ventilation would be of great value to those studying basic pulmonary physiology and the onset and
progression of a large range of respiratory diseases. It would also facilitate drug discovery and efficacy studies
aimed to mitigate respiratory pathology. The ideal method would provide quantitative regional functional
information, be applicable to longitudinal studies (low radiation dose), and have a simple and affordable
implementation that permits widespread use. Currently available imaging methods including micro-CT or MRI
fall short in one or more of these requirements.
To address this need, we will establish and evaluate a novel, easy to implement, and highly effective X-
ray phase-contrast (XPC) method for ventilation imaging in small animal models. The lung is ideally suited to
XPC imaging because it is comprised mainly of air spaces separated by thin tissue structures. The air-tissue
interfaces cause the X-ray beam to experience numerous and strong refractions that produce a distinctive
texture in the intensity measured over the lungs known as speckle. Detailed information regarding the regional
lung air volume (RLAV) distribution is encoded in the speckle. The benefits of exploiting lung speckle for
detecting and monitoring lung function are numerous but remain entirely unexplored for benchtop imaging.
Our approach involves a high degree of technical innovation regarding image formation methods and
will significantly extend the current boundaries of functional lung imaging in small animals. The proposed
method, referred to as parametric XPC (P-XPC) imaging, will produce 2D parametric images that depict the
projected RLAV distribution. When differential images are computed for any given two points in the breathing
cycle, ventilation or lung compliance imaging will be achieved. Preliminary in vivo and computational studies
have been conducted in support of the proposed research. The specific aims of the project are as follows.
Aim 1: Develop P-XPC image formation methods for estimating the projected RLAV distribution; Aim 2:
Optimize an XPC imaging system for P-XPC imaging. Aim 3: Evaluate the diagnostic capability of P-XPC
imaging in two pre-clinical animal models of disease in vivo.
该 R01 应用的目标是开发一种快速方法,用于对区域通气和
无需造影剂的小动物的肺顺应性。我们目前的大部分理解
肺部的正常功能和肺部疾病的机制来自小动物研究。然而,
由于运动和相对较小,小动物模型的肺功能成像在技术上具有挑战性
肺部的大小。使用体积描记法进行肺功能测试已用于评估肺功能
功能和损伤的有效性和实用性有限,特别是在小动物中。此外,仅聚合
产生了功能表现的测量结果,并且无法评估局部肺部变化。一个
改进的成像方法,可以提供空间和时间分辨的信息
通气对于研究基础肺生理学以及肺通气的发生和发展具有重要价值。
多种呼吸系统疾病的进展。它还将促进药物发现和功效研究
旨在减轻呼吸道病理。理想的方法是提供定量的区域功能
信息,适用于纵向研究(低辐射剂量),并且具有简单且负担得起的
允许广泛使用的实施。目前可用的成像方法包括显微 CT 或 MRI
不符合其中一项或多项要求。
为了满足这一需求,我们将建立并评估一种新颖、易于实施且高效的 X-
用于小动物模型通气成像的射线相衬(XPC)方法。肺非常适合
XPC 成像,因为它主要由薄组织结构分隔开的空气空间组成。空气组织
界面使 X 射线束经历多次强烈的折射,从而产生独特的
在肺部测量的强度纹理称为散斑。有关区域的详细信息
肺气量 (RLAV) 分布被编码在散斑中。利用肺斑点的好处
检测和监测肺功能的方法有很多,但台式成像尚未完全探索。
我们的方法涉及图像形成方法和技术方面的高度技术创新
将显着扩展目前小动物功能性肺成像的界限。拟议的
称为参数 XPC (P-XPC) 成像的方法将生成描绘
预计 RLAV 分布。当计算呼吸中任意给定两点的差分图像时
将实现周期、通气或肺顺应性成像。初步体内和计算研究
已进行以支持拟议的研究。该项目的具体目标如下。
目标 1:开发用于估计投影 RLAV 分布的 P-XPC 图像形成方法;目标 2:
优化 P-XPC 成像的 XPC 成像系统。目标 3:评估 P-XPC 的诊断能力
两种临床前疾病动物模型的体内成像。
项目成果
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Properties of a Joint Reconstruction Method for Edge-Illumination X-Ray Phase-Contrast Tomography.
边缘照明 X 射线相衬断层扫描联合重建方法的特性。
- DOI:
- 发表时间:2018
- 期刊:
- 影响因子:0
- 作者:Chen, Yujia;Anastasio, Mark A
- 通讯作者:Anastasio, Mark A
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction.
用于生成模型约束图像重建的可压缩潜在空间可逆网络。
- DOI:
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Kelkar, Varun A;Bhadra, Sayantan;Anastasio, Mark A
- 通讯作者:Anastasio, Mark A
Quantification of image texture in X-ray phase-contrast-enhanced projection images of in vivo mouse lungs observed at varied inflation pressures.
在不同充气压力下观察到的体内小鼠肺部的 X 射线相差增强投影图像中图像纹理的量化。
- DOI:
- 发表时间:2019
- 期刊:
- 影响因子:2.5
- 作者:Brooks, Frank J;Gunsten, Sean P;Vasireddi, Sunil K;Brody, Steven L;Anastasio, Mark A
- 通讯作者:Anastasio, Mark A
On Hallucinations in Tomographic Image Reconstruction.
关于断层扫描图像重建中的幻觉。
- DOI:
- 发表时间:2021-11
- 期刊:
- 影响因子:10.6
- 作者:Bhadra, Sayantan;Kelkar, Varun A;Brooks, Frank J;Anastasio, Mark A
- 通讯作者:Anastasio, Mark A
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
使用渐进式增长的 AmbientGAN 从医学成像测量中学习随机对象模型
- DOI:
- 发表时间:2020-05-29
- 期刊:
- 影响因子:0
- 作者:Weimin Zhou;Sayantan Bhadra;F. Brooks;Hua Li;M. Anastasio
- 通讯作者:M. Anastasio
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Mark A Anastasio其他文献
Mark A Anastasio的其他文献
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{{ truncateString('Mark A Anastasio', 18)}}的其他基金
Deep learning technologies for estimating the optimal task performance of medical imaging systems
用于评估医学成像系统最佳任务性能的深度学习技术
- 批准号:
10635347 - 财政年份:2023
- 资助金额:
$ 41.98万 - 项目类别:
A Computational Framework Enabling Virtual Imaging Trials of 3D Quantitative Optoacoustic Tomography Breast Imaging
支持 3D 定量光声断层扫描乳腺成像虚拟成像试验的计算框架
- 批准号:
10367731 - 财政年份:2022
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Computational imaging and intelligent specificity (Anastasio)
计算成像和智能特异性(Anastasio)
- 批准号:
10705173 - 财政年份:2022
- 资助金额:
$ 41.98万 - 项目类别:
A Computational Framework Enabling Virtual Imaging Trials of 3D Quantitative Optoacoustic Tomography Breast Imaging
支持 3D 定量光声断层扫描乳腺成像虚拟成像试验的计算框架
- 批准号:
10665540 - 财政年份:2022
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$ 41.98万 - 项目类别:
Advanced image reconstruction for accurate and high-resolution breast ultrasound tomography
先进的图像重建,可实现精确、高分辨率的乳腺超声断层扫描
- 批准号:
10017970 - 财政年份:2019
- 资助金额:
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Quantitative histopathology for cancer prognosis using quantitative phase imaging on stained tissues
使用染色组织的定量相位成像进行癌症预后的定量组织病理学
- 批准号:
10443772 - 财政年份:2019
- 资助金额:
$ 41.98万 - 项目类别:
Quantitative histopathology for cancer prognosis using quantitative phase imaging on stained tissues
使用染色组织的定量相位成像进行癌症预后的定量组织病理学
- 批准号:
10703212 - 财政年份:2019
- 资助金额:
$ 41.98万 - 项目类别:
Advanced image reconstruction for accurate and high-resolution breast ultrasound tomography
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- 批准号:
10252852 - 财政年份:2019
- 资助金额:
$ 41.98万 - 项目类别:
Advanced image reconstruction for accurate and high-resolution breast ultrasound tomography
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- 批准号:
10442593 - 财政年份:2019
- 资助金额:
$ 41.98万 - 项目类别:
Development of a Rapid Method for Imaging Regional Ventilation in Small Animals w/o Contrast Agents
开发一种无需造影剂的小动物局部通气成像快速方法
- 批准号:
9927856 - 财政年份:2019
- 资助金额:
$ 41.98万 - 项目类别:
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