Advancing and calibrating anisotropic diffusion MR imaging brain connectome with Taxon brain network diffusion phantoms
Advancing and calibrating anisotropic diffusion MR imaging brain connectome with Taxon brain network diffusion phantoms
批准号:
9893037
负责人:
Anthony P Zuccolotto
金额:
$62.15万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2022-08-31
关键词:
3D PrintAlgorithmsAnatomyAnimalsAxonBiophysicsBrainBrain imagingCaliberCalibrationClinicalCommunitiesComputer softwareCorpus CallosumDataData SetDepartment of DefenseDiffuseDiffusionDiffusion Magnetic Resonance ImagingDimensionsDiseaseElectron MicroscopeEquipmentEyeFiberGeometryGoalsGovernmentHeatingHistologyHumanImageImaging PhantomsIndustrializationLaboratoriesLight MicroscopeLiquid substanceMRI ScansMagnetic Resonance ImagingMapsMeasurementMeasuresMethodsModelingMonitorNanotubesNeurodegenerative DisordersOpticsOxygenPathologyPhasePhysiologic pulsePolymersProductionPublicationsPublishingQuality ControlRadiology SpecialtyReportingReproducibilityResearchResearch PersonnelRouteRunningSamplingScanningScoring MethodShipsSiteSpeedSpinal CordStructureSystemTaxonTechnologyTemperatureTestingTextilesTimeTime Series AnalysisTissuesTracerTraumatic Brain InjuryTubeUnited States National Institutes of HealthVariantVendorWaterbasebrain tractclinically significantconnectomecostdensitydevelopmental diseaseeconomic costhuman tissueimprovedinstrumentmicroCTmillimeternanometernanoscalenetwork modelsopen datapathology imagingprogramsquality assurancesuccesstractographytumorwhite matter
中文摘要
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英文摘要
There is a critical gap in the reliability of anisotropic diffusion magnetic resonance imaging (AdMRI). This gap can be
filled by using a ground truth measurement capability that allows for the necessary parametric control of water filled
geometries of tubes at the micron scale that can produce paths representative of the millions of axons across centimeters in
brain tract trajectories. Diffusion Tensor Imaging (DTI) publications report clinically significant systematic error that
confounds accurate quantitative assessments across instruments and time. Reference phantoms that provide exact error
metrics will advance MRI biophysics science and clinical quantitative accuracy. Correction algorithms using reference data
can reduce systematic measurement error, enabling accurate reproducible measurement and provide cross scanner norms
for AdMRI pathology. This project will deliver the first viable AdMRI phantom “ground truth” using ‘Taxons™’ (textile
axon shaped nanotubes), invented by this team, and apply advanced bi-component polymer nanoscale production methods
to create structures matched to human tissue histology. In doing this we will deliver axon scale taxons at 800 nanometer
diameter, with a packing density of one million taxons per mm2, matched to actual human corpus callosum axon
measurements. In Phase I we proposed and delivered taxons with 12 micron inner diameter tubes with a packing density of
1241 per mm2 that could be filled with water and produce FA measurement in the human tissue range. We actually “over-
delivered”, exceeding a packing density of 1,000,000 per mm2 covering the human axonal tissue range. We can now
precisely parametrically control the diameters, packing density, restricted/hindered, and isotropic water fractions to test and
improve leading compartmental models of diffusion. We created a fasciculus routing machine that can, at viable cost, create
human scale fasciculus routes matched to human tissue, such as the optic system eye to LGN, of 20 million routed taxons.
The 1 to 1 scale taxonal network phantoms quantify dMRI measurement accuracy for each taxon path with 100 micron path
precision along the trajectory. We scanned the phase I phantoms at ten sites. We established in empirical studies that there
is substantial systematic, cross instrument and measurement error (e.g., 5x the TBI effect size), that the error is stable, and
can be corrected for (removed 94% of systematic error). Phase II of this project will: 1) provide the first AdMRI phantom
for ground truth measurement to quantify dMRI biophysics, spatial homogeneity, and routing precision; 2) provide fully
automated quantification of accuracy and repeatability of measurement; 3) assess AdMRI precision of 20+ sites, quantifying
measurement error at 1.5, 3, 7, 9.4 and 14T field strength; and4) develop a set of routing phantoms (Eye>LGN> V1, spinal
cord and cortical tracts). These phantoms and/or subcomponents will be measured with non-MRI methods (confocal &
electron microscope) using NIST traceable measurements. Researchers and center directors involved in Phase I scanning
and reviewing of the results were very positive, with 30+ sites offering free scanning time to use the phantom, and to utilize
the resulting quality assurance reports. Radiology has had phantom based pivotal successes (i.e., CT Hounsfield phantoms
in the 1990s). This project will deliver a quantitative AdMRI phantom, enabling MRI metrics to become accurate across
vendors and time implementing quantitative quality assurance (QQA).
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Advancing and calibrating anisotropic diffusion MR imaging brain connectome with Taxon brain network diffusion phantoms
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批准号:9410059
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项目类别:
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资助金额:$18.91万
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财政年份:2017
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负责人:Anthony P Zuccolotto
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依托单位:
Show-N-Tell: Computerized Assessment of Pain in Children
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批准号:7612475
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资助金额:$14.17万
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财政年份:2009
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负责人:Anthony P Zuccolotto
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依托单位:
Screening for Medication IQ and Managing Medication
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批准号:7054172
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资助金额:$12.98万
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财政年份:2006
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负责人:Anthony P Zuccolotto
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依托单位:
Managing Complexity in Psychology Experiment Generation
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批准号:7230534
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项目类别:
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资助金额:$35.07万
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财政年份:2004
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负责人:Anthony P Zuccolotto
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依托单位:
Managing Complexity in Psychology Experiment Generation
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批准号:7055089
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项目类别:
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资助金额:$39.61万
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财政年份:2004
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负责人:Anthony P Zuccolotto
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依托单位:
Managing Complexity in Psychology Experiment Generation
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批准号:6833699
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项目类别:
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资助金额:$12.45万
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财政年份:2004
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负责人:Anthony P Zuccolotto
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依托单位:
Virtual Reality and Functional MRI to Study Drug Craving
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批准号:7407663
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项目类别:
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资助金额:$10.69万
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财政年份:2002
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负责人:Anthony P Zuccolotto
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依托单位:
BEHAVIORAL SOFTWARE LABORATORY--FROM PEARL TO E-PRIME
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批准号:2714278
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项目类别:
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资助金额:$37.21万
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财政年份:1997
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负责人:Anthony P Zuccolotto
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依托单位:
BEHAVIORAL SOFTWARE LABORATORY--FROM PEARL TO E-PRIME
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批准号:2890904
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项目类别:
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资助金额:$37.7万
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财政年份:1997
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负责人:Anthony P Zuccolotto
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依托单位:
BEHAVIORAL SOFTWARE LABORATORY--PEARL
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批准号:2332823
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项目类别:
-
资助金额:$10.0万
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财政年份:1997
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负责人:Anthony P Zuccolotto
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依托单位:
海外基金