grant

Improving quantitative accuracy and tissue visualization in CBCT guided radiation therapy

Organization UNIVERSITY OF COLORADO DENVERLocation Aurora, UNITED STATESPosted 15 Sept 2020Deadline 31 May 2026
NIHUS FederalResearch GrantFY20252-dimensionalAddressAffectAlgorithmsAnatomic SitesAnatomic structuresAnatomyArchitectureAreaBelgiumBody TissuesCancer PatientClinicalClinical TrialsCollaborationsColoradoComputer Software ToolsDataDevelopmentDevicesDiseaseDisorderDoseEngineering / ArchitectureEvaluationExtremitiesFloridaGeometryHealthHelical CTHelical Computed TomographyImageImage-Guided SurgeryInterventionInterventional radiologyInvestigationIonsLimb structureLimbsManufacturerMeasuresMethodsModalityModificationMonitorNoiseNon-TrunkNormal TissueNormal tissue morphologyOrthopedicOrthopedic Surgical ProfessionOrthopedicsPatientsPerformancePhotonsPlayPrediction of Response to TherapyRadiation DoseRadiation Dose UnitRadiation ScatteringRadiation therapyRadiotherapeuticsRadiotherapyRegimenReportingRoleScanningSiteSoftware ToolsSpiral Acquisition CTSpiral CTSpiral Computed TomographySpiral Volumetric CTSpiral Volumetric Computed TomographyStructureSystemTechnologyTestingTextureTissuesToxic effectToxicitiesTransmissionTreatment ProtocolsTreatment RegimenTreatment ScheduleTreatment-related toxicityTumor TissueUniversitiesVariantVariationVisualizationWorkclinical decision-makingcomputerized data processingcone-beam CTcone-beam computed tomographycustomized therapycustomized treatmentdata processingdesigndesigningdevelopmentalexperimentexperimental researchexperimental studyexperimentsfabricationimage guidanceimage guidedimage registrationimagingimprovedindividualized medicineindividualized patient treatmentindividualized therapeutic strategyindividualized therapyindividualized treatmentintra-operative imagingintraoperative imagingirradiation responsemaxillofacialmedical collegemedical schoolsnovelpatient populationpatient specific therapiespatient specific treatmentpredict therapeutic responsepredict therapy responsepreventpreventingprospectiveproton therapyprototyperadiation responseradiation treatmentradiomicsresponse to radiationschool of medicinesocial rolesoft tissuesoftware toolkitspiral CT scansurgical imagingtailored medical treatmenttailored therapytailored treatmenttherapeutic toxicitytherapy associated toxicitytherapy predictiontherapy related toxicitytherapy toxicitytooltransmission processtreatment predictiontreatment response predictiontreatment toxicitytreatment with radiationtreatment-associated toxicitytumortwo-dimensionalunique treatmentvolume CTvolume computed tomographyvolumetric computed tomography
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Full Description

PROJECT SUMMARY
Even though cone beam computed tomography (CBCT) is the most commonly used volumetric image

guidance modality, its role has been severely limited in the context of treatment monitoring and patient-specific

treatment modifications in radiation therapy. Due to CBCT’s poor image quality, clinicians cannot clearly

visualize soft tissues to assess anatomical changes, thus affecting their clinical decision-making. Moreover,

tools for treatment monitoring, such as deformable registration and dose calculation, do not function robustly

with today’s CBCT images due to the lack of CT number accuracy.

Scattered radiation remains to be the fundamental problem in improving CBCT image quality. Thus, in

this project, we propose the two-dimensional antiscatter grid (2D Grid) as a novel device to address the scatter

problem and achieve high-quality CBCT images that are suitable for treatment monitoring. Our device has

fundamentally different architecture and fabrication than existing antiscatter grids for CBCT. Due to its

optimized grid structure, our 2D Grid provides both higher primary transmission and better scatter rejection

performance than today’s state-of-the-art antiscatter grids. Due to its favorable primary transmission and

scatter rejection performance, our 2D Grid improves the contrast-to-noise ratio and CT number accuracy to

levels not achievable with existing antiscatter grids.

We hypothesize that our 2D Grid will provide significantly better soft tissue visualization and CT number

accuracy, and deformable registration algorithms are expected to perform significantly better. To test our

hypotheses, we will develop and optimize data processing methods for 2D Grid implementation in CBCT (Aim

1). Subsequently, we will fabricate 2D Grid prototypes and evaluate their performance in clinical CBCT

systems for photon and proton therapy (Aim 2). Following phantom based evaluations, we will conduct a

prospective clinical trial to evaluate the clinical utility of improved image quality (Aim 3). We will perform

observer studies to quantify the improvement in soft tissue visualization with respect to existing clinical CBCT

and gold-standard Helical CT, assess the improvement in accuracy of deformable image registration

algorithms, and evaluate the improvement in consistency of image intensity and texture features.

While our application is focused on radiation therapy, the 2D Grid can play a key role in other CBCT

applications, such as interventional radiology, extremity imaging, and intraoperative imaging. Due to its

improved low-contrast visualization performance, our 2D Grid may also allow reduction of the imaging dose in

CBCT.

Grant Number: 5R01CA245270-06
NIH Institute/Center: NIH

Principal Investigator: Cem Altunbas

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