grant
Development and clinical validation of domain-aware anthropomorphic model observers for task-based image quality assessment
Organization ILLINOIS INSTITUTE OF TECHNOLOGYLocation CHICAGO, UNITED STATESPosted 1 Jul 2025Deadline 31 Mar 2029
NIHUS FederalResearch GrantFY20253-D print3-D printer3D Print3D printer3D printingAdoptedAgreementAlgorithm DesignAlgorithmic DesignAlgorithmic EngineeringAlgorithmsAwarenessBlack BoxCAT scanCT X RayCT XrayCT imagingCT scanCardiacClinicalClinical DataCognitive DiscriminationCollaborationsComputed TomographyConsumptionDataData SetData SourcesDecision MakingDetectionDevelopmentDevice or Instrument DevelopmentDiagnosisDiagnosticDiscriminationDoseEngineeringEnvironmentEvaluationFeedbackHistoryHumanHybridsImageImage AnalysesImage AnalysisImaging DeviceImaging InstrumentImaging PhantomsImaging ToolKnowledgeLesionLettersLiverLocalized LesionLungLung Respiratory SystemMR ImagingMR TomographyMRIMRIsMachine LearningMagnetic Resonance ImagingMeasuresMedical ImagingMedical Imaging, Magnetic Resonance / Nuclear Magnetic ResonanceMethodologyMethodsModelingModern ManMotionNMR ImagingNMR TomographyNational Institutes of HealthNuclear Magnetic Resonance ImagingOrganPatientsPerformanceProceduresProcessReaderRecording of previous eventsResearchResolutionSeveritiesStandardizationTechnologyTestingTimeTomodensitometryTrainingUnited States National Institutes of HealthValidationVisual PsychophysicsWorkX-Ray CAT ScanX-Ray Computed TomographyX-Ray Computerized TomographyXray CAT scanXray Computed TomographyXray computerized tomographyZeugmatographyadaptive learningalgorithm engineeringalgorithmic compositioncatscanclinical diagnosticsclinical relevanceclinical validationclinically relevantcomputed axial tomographycomputer tomographycomputerized axial tomographycomputerized tomographycostdeep learningdeep learning methoddeep learning strategydesigndesigningdevelopmentaldevice developmentdigitaldiscrimination taskengineering designexperiencehepatic body systemhepatic organ systemhistoriesimage constructionimage evaluationimage generationimage interpretationimage reconstructionimagingimaging systemimprovedinsightinstrument developmentliver imagingliver scanningmachine based learningmanufacturemedical diagnosticnon-contrast CTnoncontrast CTnoncontrast computed tomographyreceptive fieldreconstructionresolutionsresponsesimulationthree dimensional printingtooltransfer learningvalidationsvirtual
Description preview
In this project we aim to develop deep-learning anthropomorphic model observers (AMO) as
a substitute for human observers (HO) in studies aimed to assess image quality, defined in a task-
based fashion based on clinical diagnostic performance. An accurate AMO would allow fast and
clinically relevant procedure for optimization of imaging system and…
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