Quantitative Multiparametric MRI to Assess the Effect of Stem Cell Therapy on Chronic Low Back Pain
定量多参数 MRI 评估干细胞疗法对慢性腰痛的效果
基本信息
- 批准号:10454354
- 负责人:
- 金额:$ 71.58万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2014
- 资助国家:美国
- 起止时间:2014-08-01 至 2025-07-31
- 项目状态:未结题
- 来源:
- 关键词:AgingAmericanAnatomyBack PainBiologyChemicalsChronic low back painClinicalConsumptionDataDehydrationDetectionDevelopmentDiagnosisDiagnostic ProcedureDiffusionDimensionsFamily suidaeGoalsGrantHeightHumanImageImaging TechniquesImmobilizationIndividualInjectionsIntervertebral disc structureLongitudinal StudiesLow Back PainMagnetic Resonance ImagingMapsMeasuresMedicalMesenchymal Stem CellsMetabolismMethodsMiniature SwineModelingMolecularMonitorMorbidity - disease rateMotionNatural regenerationNatureOccupationsOperative Surgical ProceduresPainPathologicPatientsPhysiciansProcessPropertyProtonsPublishingReproducibilityScanningSourceSpinal FusionSystemT2 weighted imagingTechniquesTherapeutic EffectTimeTissuesUnited StatesVertebral columnWaterWorkaccurate diagnosisbasebone marrow mesenchymal stem cellcardiovascular imagingdata acquisitiondisabilityhealthy volunteerimaging modalityimprovedintervertebral disk degenerationmolecular markermultitasknew therapeutic targetnucleus pulposuspain patientpain reliefparametric imagingporcine modelreconstructionsolutestem cell therapystem cellstargeted treatmenttooltreatment response
项目摘要
PROJECT SUMMARY
The goal of the proposed project is to develop multiparametric mapping magnetic resonance imaging (MRI)
techniques to assess the effect of stem cell therapy on Intervertebral Disc (IVD) degeneration, a major source
of chronic low back pain (LBP). LBP is a common cause of morbidity and disability, and the most common
cause of job-related disability and a leading contributor to missed work in the United States. If a patient with
LBP has several degenerate discs, further examination is required to determine which disc is the source of
pain, prior to a decision of medical treatment or surgical intervention. However, there are no diagnostic
methods in clinical use that help to differentiate between a pathologically painful and an aging disc. On the
other hand, there are no good surgical solutions for patients suffering from chronic LBP as they failed to show
long term pain relief compared to conservative treatment. A potential alternative approach to surgical
procedures is the use of injected stem cells as a potential treatment to regenerate the discs, which have shown
promising therapeutic effects in several recent studies. In this proposal, we hypothesize that a combination of
multiple MR parameters T1, T2, T1ρ, ADC, and qCEST can detect painful discs and quantitatively measure the
effect of mesenchymal stem cells (MSCs) injection to degenerate discs on LBP better than any single MR
parameter. In Aim 1, we will develop a highly efficient and simultaneous multiparametric mapping approach for
T1, T2, T1ρ, ADC, and CEST quantification. The centerpiece of the technical development is our newly
developed Multitasking framework, published in Nature BME in 2018, which allows motion compensated,
highly efficient, simultaneous, multiparametric mapping using low rank tensor reconstruction, taking advantage
of the vast data redundancy among multiple time dimensions. We have applied the technique to quantitative
cardiovascular and body imaging with excellent results. In Aim 2, we will validate that multiparametric
mappings are better associated with molecular pain markers than any individual parameters alone and can
detect the therapeutic effect of MSC injection in a minipig model of disc degeneration. Successful completion
of this project has the potential to make major impact on the way we diagnose and treat chronic LBP.
Multiparametric quantification MRI will provide a reliable and noninvasive tool for LBP detection and guide
physicians which discs to treat. It could also be used to monitor the therapeutic effect of various stem cell
injections, or other disc-targeted therapies that are currently in development.
项目摘要
拟议项目的目的是开发多参数映射磁共振成像(MRI)
评估干细胞疗法对椎间盘(IVD)变性的影响的技术,这是主要来源
慢性下腰痛(LBP)。 LBP是发病率和残疾的常见原因,也是最常见的
与工作有关的残疾原因,是美国错过工作的主要贡献者。如果患者患有
LBP具有几个退化的椎间盘,需要进一步检查以确定哪个光盘是
疼痛,在做出治疗或手术干预的决定之前。但是,没有诊断
临床用途的方法有助于区分病理疼痛和衰老的椎间盘。在
另一方面,对于患有慢性LBP的患者,没有出色的手术解决方案,因为他们未能显示
与保守治疗相比,长期缓解疼痛。手术的潜在替代方法
程序是使用注射干细胞作为再生椎间盘的潜在治疗方法,这已显示
在最近的几项研究中有希望的治疗作用。在此提案中,我们假设
多个MR参数T1,T2,T1ρ,ADC和QCEST可以检测到疼痛的椎间盘并定量测量
间充质干细胞(MSC)注射对LBP的归化盘的影响比任何单个MR都要好
范围。在AIM 1中,我们将开发一种高效且简单的多参数映射方法
T1,T2,T1ρ,ADC和CEST定量。技术发展的核心是我们新的
开发的多任务框架,于2018年发表于自然BME,允许完成运动,
利用低级张量重建,高效,同时的多参数映射,利用优势
多个时间维度之间的巨大数据冗余。我们已经将该技术应用于定量
心血管和身体成像,效果很好。在AIM 2中,我们将验证该多参数
与单独的任何参数相比,映射与分子疼痛标记相关,可以
检测MSC注射的治疗作用在椎间盘退化模型中。成功完成
这个项目有可能对我们诊断和治疗慢性LBP的方式产生重大影响。
多参数定量MRI将为LBP检测和指南提供可靠且无创的工具
光盘治疗的医师。它也可用于监测各种干细胞的治疗作用
注射剂或其他目前正在开发的椎间盘靶向疗法。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Debiao Li其他文献
Debiao Li的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Debiao Li', 18)}}的其他基金
Predicting Pancreatic Ductal Adenocarcinoma (PDAC) Through Artificial Intelligence Analysis of Pre-Diagnostic CT Images
通过诊断前 CT 图像的人工智能分析预测胰腺导管腺癌 (PDAC)
- 批准号:
10475648 - 财政年份:2021
- 资助金额:
$ 71.58万 - 项目类别:
Predicting Pancreatic Ductal Adenocarcinoma (PDAC) Through Artificial Intelligence Analysis of Pre-Diagnostic CT Images
通过诊断前 CT 图像的人工智能分析预测胰腺导管腺癌 (PDAC)
- 批准号:
10693185 - 财政年份:2021
- 资助金额:
$ 71.58万 - 项目类别:
An Accurate Non-Contrast-Enhanced Cardiac MRI Method for Imaging Chronic Myocardial Infarctions: Technical Developments to Rapid Clinical Validation
用于慢性心肌梗塞成像的准确非增强心脏 MRI 方法:快速临床验证的技术发展
- 批准号:
9899302 - 财政年份:2017
- 资助金额:
$ 71.58万 - 项目类别:
4Dx Small Animal Scanner for Functional Lung Imaging
用于功能性肺部成像的 4Dx 小动物扫描仪
- 批准号:
9075865 - 财政年份:2016
- 资助金额:
$ 71.58万 - 项目类别:
Whole-Heart Myocardial Blood Flow Quantification Using MRI
使用 MRI 定量全心心肌血流量
- 批准号:
9226051 - 财政年份:2015
- 资助金额:
$ 71.58万 - 项目类别:
Quantitative Multiparametric MRI to Assess the Effect of Stem Cell Therapy on Chronic Low Back Pain
定量多参数 MRI 评估干细胞疗法对慢性腰痛的效果
- 批准号:
10689204 - 财政年份:2014
- 资助金额:
$ 71.58万 - 项目类别:
3D MRI Characterization of High-Risk Carotid Artery Plaques without Contrast Media
无需造影剂的高风险颈动脉斑块的 3D MRI 表征
- 批准号:
8973293 - 财政年份:2009
- 资助金额:
$ 71.58万 - 项目类别:
Flow Sensitive SSFP for Non-Contrast MRA and Vessel Wall Imaging
用于非对比 MRA 和血管壁成像的流量敏感 SSFP
- 批准号:
7644221 - 财政年份:2009
- 资助金额:
$ 71.58万 - 项目类别:
3D MRI Characterization of High-Risk Carotid Artery Plaques without Contrast Media
无需造影剂的高风险颈动脉斑块的 3D MRI 表征
- 批准号:
9300995 - 财政年份:2009
- 资助金额:
$ 71.58万 - 项目类别:
相似海外基金
Fluency from Flesh to Filament: Collation, Representation, and Analysis of Multi-Scale Neuroimaging data to Characterize and Diagnose Alzheimer's Disease
从肉体到细丝的流畅性:多尺度神经影像数据的整理、表示和分析,以表征和诊断阿尔茨海默病
- 批准号:
10462257 - 财政年份:2023
- 资助金额:
$ 71.58万 - 项目类别:
Preservation of brain NAD+ as a novel non-amyloid based therapeutic strategy for Alzheimer’s disease
保留大脑 NAD 作为阿尔茨海默病的一种新型非淀粉样蛋白治疗策略
- 批准号:
10588414 - 财政年份:2023
- 资助金额:
$ 71.58万 - 项目类别:
Fecal Microbiota Transfer Attenuates Aged Gut Dysbiosis and Functional Deficits after Traumatic Brain Injury
粪便微生物群转移可减轻老年肠道菌群失调和脑外伤后的功能缺陷
- 批准号:
10573109 - 财政年份:2023
- 资助金额:
$ 71.58万 - 项目类别:
The Role of Viral Exposure and Age in Alzheimer's Disease Progression
病毒暴露和年龄在阿尔茨海默病进展中的作用
- 批准号:
10717223 - 财政年份:2023
- 资助金额:
$ 71.58万 - 项目类别:
Designing novel therapeutics for Alzheimer’s disease using structural studies of tau
利用 tau 蛋白结构研究设计治疗阿尔茨海默病的新疗法
- 批准号:
10678341 - 财政年份:2023
- 资助金额:
$ 71.58万 - 项目类别: