Quantitative Magnetization Transfer Imaging for Early Detection of Alzheimer's Disease
Quantitative Magnetization Transfer Imaging for Early Detection of Alzheimer's Disease
批准号:
10681225
负责人:
Andrew Mao
金额:
$5.27万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-01 至 2026-07-31
关键词:
AddressAlzheimer disease detectionAlzheimer&aposs DiseaseAlzheimer&aposs disease pathologyAlzheimer&aposs disease therapeuticAmericanAmyloidAmyloid beta-ProteinBindingBiological MarkersBiophysicsBrainCessation of lifeClinicalClinical ManagementClinical ResearchCognitiveDementiaDemyelinationsDiagnosticDiseaseDisease ProgressionEarly DiagnosisFellowshipHIVHealth Care CostsHeart DiseasesHybridsImageImaging TechniquesImpaired cognitionIndividualLipidsLiteratureMagnetic Resonance ImagingMapsMeasuresMethodsModelingMonitorMyelinNerve DegenerationNetwork-basedNeurobehavioral ManifestationsNeurodegenerative DisordersNoiseOutcomePathologyPatient MonitoringPatientsPharmacotherapyPhysiologic pulsePilot ProjectsPopulationPositron-Emission TomographyProcessProteinsProtonsRelaxationReportingReproducibilityResearchResolutionScanningSignal TransductionStrokeStructureSurrogate MarkersTechnical ExpertiseTechniquesTechnologyTestingTimeTissuesTrainingUnited StatesWaterWorkabeta accumulationaging populationamyloid pathologybeta amyloid pathologybiophysical propertiesburden of illnesscareercerebral atrophyclinical developmentclinical practicedata reductiondata spacedeep learningdetection methodhigh resolution imagingimage reconstructionimage translationimaging modalityimprovedin silicoin vivoinnovationinterestinterstitialmacromoleculemagnetic fieldmild cognitive impairmentmouse modelneural networknovel therapeuticspatient populationpatient responsepre-clinicalprismareconstructiontau Proteinstheoriestooltreatment response
中文摘要
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英文摘要
Project Summary
Alzheimer's disease (AD) is the most common cause of dementia and, at present, an irreversible neurode-
generative disease with an ever-increasing disease burden in the United States. Clinical management and de-
velopment of novel therapeutics for AD will benefit from reproducible, robust and non-invasive markers of early in
vivo AD pathology, but no such tools are well-established in routine clinical practice. Quantitative magnetization
transfer (qMT) is an MRI based technique for detecting microstructural tissue changes such as demyelination and
the presence of macromolecules, including amyloid beta protein. This sensitivity to multiple aspects of the amy-
loid/tau/neurodegeneration framework offers a promising diagnostic alternative to amyloid PET in a single, rapid
and high-resolution imaging exam. My preliminary findings suggest that, using the theory of hybrid state free
precession developed in my research group, qMT images can be acquired in vivo in 12 minutes at 1mm isotropic
resolution with good SNR. Additionally, by direct comparison to PET in a cognitively normal but amyloid positive
subject, qMT appears to be sensitive to the early accumulation of amyloid beta and confirms previous literature
reports of increased qMT exchange rates in AD. My proposal aims to solve the remaining technical challenges
surrounding qMT by optimizing the acquisition for improved sensitivity to amyloid beta (Aim 1) and developing
a neural network based reconstruction pipeline to substantially reduce the post-processing time and improve its
robustness to magnetic field inhomogeneities (Aim 2), which create biases in the quantitative parameters. These
biases are particularly important to curtail in the deep brain, where the accumulation of amyloid beta is purported
to begin in AD. In Aim 3, I will perform a pilot study to compare the proposed qMT method's sensitivity for in
vivo amyloid beta in a cognitively normal population directly to amyloid PET. Together, this will establish qMT as a
surrogate marker for in vivo amyloid and motivate further clinical studies on its utility in monitoring AD progression
and response to therapy.
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Quantitative Magnetization Transfer Imaging for Early Detection of Alzheimer's Disease
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批准号:10464315
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项目类别:
-
资助金额:$5.18万
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财政年份:2022
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负责人:Andrew Mao
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依托单位: