Radiologics, Inc. — Department of Health and Human Services SBIR Phase II: 300

Description

Abstract Classicallyanatomic information provided by MRI has been essential to the neurosurgeon to maximize extent of tumor resection andas a resultimprove survival statisticsThat saidit is not routine during resections to make use of similar imaging information that reflect the functional organization of the brainTask based fMRI has been employed as a means of pre operatively localizing functionHowevertask based fMRI depends on the patient s ability to comply with the task paradigmwhich frequently is lackingThis problem can be overcome by using the recently developed method of resting state functional magnetic resonance imagingrsfMRIto localize functionMoreoverrsfMRI is highly efficientas multiple resting state networksRSNsassociated with multiple cognitive domains can be mapped at the same timeWith this in mindthe long term goal of our research is to improve survival and quality of life after surgical resection of brain tumors by improving the identification preservation of eloquent cortexThe current barrier that prevents the widespread use of rsfMRI isthe high degree of advanced imaging expertise currently necessary to create and interpret the images andthe necessary IT infrastructure necessary to support the analysesTo address this shortcomingwe propose to create a turnkey system for functional mapping within the brain that resides on a cloud computing platformAt the heart of our methodology is a multi layer perceptronMLPalgorithm that assigns RSN membership to each locus within the brain using supervised classification of rsfMRI dataCurrent data demonstrate that MLP based RSN mapping is more reliable than conventional task based fMRI and is extremely sensitive to sites identified by cortical stimulationthe standard in intraoperative mappingTranslation of the science and techniques created at Washington University will be accomplished by a deep collaboration with Radiologicsan emerging company with strong expertise in cloud computing for clinical imagingTowards this endthe overall objective of the proposed project is to create an imaging technology packagenamed Cirrusthat will integrate automatic MLP based RSN mapping with cloud based computingThe Specific Aims of this proposal are toCreate Cloud Based rsfMRI Brain Mapping CapabilityCirrusDeploy Cloud Based rsfMRI CapabilityCirrus Platformto New User Host InstitutionsandOptimize the Clinical User InterfaceUIof CirrusThe expected outcome of this translational strategy will be an integrated imaging surgical presurgical mapping technology using rsfMRI with clearly defined performance capabilitieswell delineated localization outputsand a clinician friendly user experience that scales to all types of health care settingsThusthis proposal is innovative because there currently does not exist any comparable system that integrates cutting edge image analysis tools with cloud based capabilitiesThis work is significant because it will disseminate technology that fundamentally enhances the surgeon s understanding of the functional implications of their surgical strategythereby enables safermore tailored approaches to improving outcomes