Fluid mechanics approach to tissue perfusion quantification in MRI
Fluid mechanics approach to tissue perfusion quantification in MRI
批准号:
10720485
负责人:
Yi Wang
金额:
$66.0万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2027-07-31
关键词:
AddressAnatomyAngiographyArteriesBiophysicsBlood VesselsBlood flowBrainBreastClinical TrialsCollectionComplexComputing MethodologiesDataData SetDefectEquationFeasibility StudiesFingerprintGoalsImageIschemic StrokeKidneyKidney TransplantationKnowledgeLawsLiquid substanceMagnetic Resonance ImagingMalignant NeoplasmsMammary NeoplasmsMapsMechanicsMedical ImagingMethodsOrganOutcomeOutputPatientsPerfusionPermeabilityPositron-Emission TomographyPredispositionProcessPropertyQuantitative MicroscopyResearch Project GrantsResolutionSpecific qualifier valueStrokeStructureTechnologyTimeTissuesTracerTrainingarterial spin labelingbreast imagingclinical practicecontrast enhanceddeep learningdeep neural networkimprovedinterestkidney imaginglarge datasetsmillisecondpreservationsimulationtumor
中文摘要
点击翻译按钮获取中文摘要
英文摘要
PROJECT SUMMARY/ABSTRACT
Our overall goal is to develop a fluid mechanics approach to studying tracer transport through tissue for
perfusion quantification in magnetic resonance imaging (MRI), which is termed as quantitative transport
mapping (QTM). Current/traditional perfusion quantification in MRI and medical imaging in general is based on
Kety's method that assumes the same arterial input globally into all voxels in an imaging volume. This global
arterial input function (AIF) transgresses the local mass conservation at a voxel and requires the user to
choose an arterial region of interest (ROI) with the consequent perfusion value highly dependent on the ROI
choice, which is known as the AIF problem.
been
The AIF problem in Kety's method for perfusion quantification has
a major unmet challenge impeding perfusion quantification in MRI.The tracer concentration at the artery
entering the voxel is needed to address the AIF problem and can be estimated by following tracer transport
through the vascular space according to fluid mechanics, which is the proposed QTM. Accordingly, we propose
to develop QTM technology for MRI perfusion quantification, capable of processing all 3 major types of images:
dynamic susceptibility contrast (DSC) as in imaging ischemic stroke, multidelay arterial spin labeling (ASL) as
in imaging kidney transplant, and dynamic contrast enhanced (DCE) as in imaging breast tumor. We plan to
achieve this objective through the following three specific aims:
Aim
comprising
Aim
Simverse
tracer
Aim
processing
In
perfusion
1 Develop vascular Simverses For the brain, kidney and breast, we will develop vascular Simverse
datasets of vasculature, flow and permeability distribution, and tracer propagation.
2 Develop compartmentalized quantitative transport mapping. The datasets in the vascular
of an organ are used to train DNNs for QTM determination of vasculature, flow and permeability, and
propagation from tracer spacetime images.
3 Evaluate quantitative perfusion mapping in patients. The eveloped QTM is evaluated for
three major perfusion MRI acquisitions: DSC, multidelay ASL and DCE.
summary, the successful outcome of this project will establish the fluid mechanics based QTM for
quantification
.
d
as a more effective alternative to Kety's method with preservation of local mass
conservation and without the AIF problem.
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