Analyses of gradient echo MRI data
梯度回波 MRI 数据分析
基本信息
- 批准号:8448217
- 负责人:
- 金额:$ 37.92万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-04-15 至 2015-03-31
- 项目状态:已结题
- 来源:
- 关键词:AcuteAlgorithmsBloodCalibrationCerebral hemisphere hemorrhageDataData AnalysesDevelopmentDiagnosisDiffusion Magnetic Resonance ImagingDiseaseDrug FormulationsEquationFerritinFoundationsFunctional Magnetic Resonance ImagingGrowthHematomaHemorrhageHemosiderinImageImageryIn VitroIronLinkMagnetic Resonance ImagingMapsMeasurementMeasuresMethemoglobinMethodsMorbidity - disease rateMorphologic artifactsMorphologyNeurodegenerative DisordersPatientsPhasePredispositionPropertyResearchSeveritiesSignal TransductionSolutionsSourceStructureTestingTissue ExtractsTissuesTraumatic Brain InjuryValidationVascular DiseasesWeightacute strokebaseblood productclinical practicedeoxyhemoglobinin vivomagnetic fieldmethod developmentmortalitynovelobject shapepublic health relevancesimulation
项目摘要
DESCRIPTION (provided by applicant): The objective of this research is to develop novel analyses of the gradient echo (GRE) MRI data for quantitative characterization of intrinsic tissue property. Gradient echo MRI has been routinely used in clinical practice. A major aspect of its image contrast is based on its unique signal sensitivity to tissue susceptibility, which is particularly useful for studying blood deoxyhemoglobin (foundation of fMRI) and blood breakdown products, methemoglobin, hemosiderin and ferritin (various bleeding disorders including traumatic brain injury, hemorrhage and microbleed, vascular disorders, neurodegenerative diseases, et al) that have strong susceptibilities. For example, GRE MRI is becoming a method replacing CT for measuring acute intracerebral hemorrhage (ICH). However, GRE MRI is well known to have blooming susceptibility artifacts that make it difficult to identify the true boundary of hematoma and overestimate hematoma volume, a critical parameter in managing ICH patients. We hypothesize that rigorous analysis of GRE MRI data can allow accurate mapping of susceptibility source, enabling robust identification of hematoma volume. Mapping tissue susceptibility requires solving the field-to-source inverse problem, which is ill-posed using the phase data alone. We propose to develop a novel morphology enabled dipole inversion (MEDI) approach for analyzing both phase and magnitude data gradient echo MRI to extract tissue susceptibility quantity. The phase image contains the magnetic field information for fitting susceptibility via Maxwell's Equation. The magnitude image contains tissue structure information for matching with susceptibility interfaces via least discordance. We have proved mathematically that these phase and magnitude information are sufficient to determine susceptibility. We have obtained very encouraging preliminary data indicating that our MEDI inverse approach is sufficiently accurate in solving the field to source inverse problem. Accordingly, our proposed research consists of the following specific aims. 1) Develop the MEDI approach for analyzing phase and magnitude data in gradient echo MRI. 2) Apply MEDI to analyze gradient echo MRI of patients with primary ICH for measuring hematoma by comparing with CT.
描述(由申请人提供):本研究的目的是开发梯度回波(GRE)MRI数据的新分析,用于内在组织特性的定量表征。梯度回波MRI已常规用于临床实践。其图像对比度的一个主要方面是基于其对组织敏感性的独特信号敏感性,这对于研究血液脱氧血红蛋白(fMRI的基础)和具有强敏感性的血液分解产物、高铁血红蛋白、含铁血黄素和铁蛋白(各种出血性疾病,包括创伤性脑损伤、出血和微出血、血管疾病、神经退行性疾病等)特别有用。例如,GRE MRI正在成为测量急性脑出血(ICH)的替代CT的方法。然而,众所周知,GRE MRI具有模糊的敏感性伪影,这使得难以识别血肿的真实边界并高估血肿体积,这是管理ICH患者的关键参数。 我们假设GRE MRI数据的严格分析可以准确定位敏感性来源,从而能够可靠地识别血肿体积。映射组织磁化率需要解决场源逆问题,这是不适定的相位数据单独使用。我们提出了一种新的形态学使能偶极反演(MEDI)方法,用于分析相位和幅度数据梯度回波MRI提取组织磁化率量。相位图像包含用于通过麦克斯韦方程拟合磁化率的磁场信息。幅值图像包含组织结构信息,用于通过最小不一致性与磁化率界面匹配。我们已经从数学上证明了这些相位和幅度信息足以确定磁化率。我们已经获得了非常令人鼓舞的初步数据表明,我们的MEDI逆方法是足够准确地解决场源反问题。因此,我们提出的研究包括以下具体目标。1)开发用于分析梯度回波MRI中相位和幅度数据的MEDI方法。2)应用MEDI分析梯度回波MRI对原发性脑出血患者血肿的测量,并与CT进行比较。
项目成果
期刊论文数量(0)
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bspan style=font-family:Times New Roman,serif;font-size:18pt;Detecting Chaos from Time Series of/spanspan style=font-family:宋体;font-size:18pt; /spanspan style=
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Xin Su;Yi Wang;Shengseng Duan;Junhai Ma - 通讯作者:
Junhai Ma
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