grant

Quantitative in-vivo and clinical imaging (Boppart)

Organization UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGNLocation CHAMPAIGN, UNITED STATESPosted 30 Sept 2022Deadline 20 Jun 2027
NIHUS FederalResearch GrantFY2025AI systemAlgorithmsApoptosisApoptosis PathwayApplication procedureArtificial IntelligenceAutophagocytosisBindingBiologicalBiological MarkersBiophotonicsBiopsy SampleBiopsy SpecimenBody TissuesCancer DetectionCathetersCell BodyCell Communication and SignalingCell Death ProcessCell SignalingCellsChemicalsClinicalClinical Medical SciencesClinical MedicineClinical TrialsColoring AgentsComputer ReasoningContrast AgentContrast DrugsContrast MediaDecision MakingDetectionDevelopmentDiagnosisDiagnosticDiseaseDisorderDyesEndoscopesEngineeringFDA approvedFiberFinancial costHistopathologyHumanHuman Subject ResearchImageImaging DeviceImaging InstrumentImaging ToolImaging technologyIntermediary MetabolismIntracellular Communication and SignalingInvestigatorsLab FindingsLabelLaboratoriesLaboratory FindingLightLipidsMachine IntelligenceMachine LearningMapsMeasuresMedicalMedicineMetabolicMetabolic ProcessesMetabolismMethodsMicroscopicMicroscopyMitochondriaModalityModern ManMolecularMolecular InteractionMonitorNamesNecrosisNecroticNeedlesOptical BiopsyOpticsOrganellesOrganismPathologicPathologyPatientsPerformancePhasePhotoradiationPhotoreceptor CellPhotoreceptorsPhotosensitive CellProceduresProcessProgrammed Cell DeathRadiopaque MediaRegulatory approvalResearch PersonnelResearchersRetinaRiskSafetyScienceSensitivity and SpecificitySignal TransductionSignal Transduction SystemsSignalingSiteSlideSpectroscopySpectrum AnalysesSpectrum AnalysisStaining methodStainsStructureSystemSystemic diseaseTechnologyThickThicknessTimeTissue ExpansionTissue StainsTissue imagingTissuesVariantVariationVisual ReceptorVisualizationadaptive opticsautophagybasebasesbio-markersbiologicbiologic markerbiological signal transductionbiomarkerbiomedical imagingbioprocessclinical imagingclinical relevanceclinically relevantdeep learningdeep learning methoddeep learning strategydesigndesigningdevelopmentaldigital pathologyimage-based methodimagingimaging capabilitiesimaging in vivoimaging methodimaging modalityimaging probeimaging systemimprovedin vivoin vivo imaginginstrumentliving systemmachine based learningmitochondrialmonetary costmulti-modalitymultimodalitynamenamednamingnano particlenano-sized particlenanoparticlenanosized particlenew technologynovelnovel technologiesoptic imagingopticaloptical imagingpoint of careregulatory authorizationregulatory certificationregulatory clearanceresearch studysingle moleculespatial and temporalspatial temporalspatiotemporaltech developmenttechnology developmenttranslational opportunitiestranslational potentialuptake
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Full Description

SUMMARY
It is essential that technological advances in imaging developed in the laboratory find direct translational paths

to rapidly demonstrate their clinical utility in patients, and establish the potential for improving detection,

diagnosis, and monitoring of disease. While label-based optical imaging modalities have demonstrated potential

in intraoperative cancer detection, mapping the microvasculature, and site-specifically targeting of altered

metabolism and pathology, to name a few, these approaches often come at a significant time and financial cost

due to the associated safety risks and lengthy review processes required for FDA approval of any new contrast

agent or probe. Importantly, any new targeted contrast agent or probe inevitably will have some degree of non-

specific binding or off-target labeling, as well as a variable degree of uptake or labeling of the targeted cell or

site. In the end, the measured or imaged signal levels are always questioned. Is the signal low because the

targeted pathology is minimal, or because the targeted efficiency of labeling is low? The importance of label-free

imaging is therefore high, and the need for label-free imaging across size scales is great. By identifying robust

label-free signals or biomarkers that indicate changes in structure, molecular composition, metabolism, and

function, quantitative clinical and in vivo imaging not only becomes a feasible alternative to label-based methods,

but also provides a direct and rapid translational path to clinical human studies, since regulatory approval is not

additionally needed for a contrast agent or probe. This enables rapid in vivo first-to-human and limited-scale

human subjects research studies (and subsequently larger clinical trials) to be performed with the new optical

imaging technologies, and make early determination of the clinical utility of the technologies and the new optical

biomarkers that they generate. This TRD focuses on the technological development through four specific aims

that progress from 1) the sub-cellular and cellular scale, identifying optical biomarkers and signatures that would

indicate more systemic disease processes, to 2) tissue sections in an advanced digital pathology platform with

artificial intelligence, to 3) computational optical imaging algorithms that extend the depth and performance of

optical imaging in thick tissues, and finally to 4) engineered beam delivery systems to widely expand tissue

access and application for these label-free optical imaging modalities. Collectively, this project will demonstrate

novel technological advances that will find a myriad of applications to advance the biological and medicine

sciences, and improve diagnostic and monitoring capabilities in clinical medicine.

Grant Number: 5P41EB031772-04
NIH Institute/Center: NIH

Principal Investigator: Stephen Boppart

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