ALA SCIENTIFIC INSTRUMENTS INC — Department of Health and Human Services SBIR Phase I: NIDA

Description

Abstract Functional magnetic resonance imaging fMRI has rapidly become the dominant tool in human neuroscience research and is poised to become a transformative technology in the areas of psychiatric and neurological diagnostics In recent years large scale $ M $ B USD and long term year international investments e g NIDA Adolescent Brain Cognitive Development Study NIH Human Connectome Project White House BRAIN Initiative UK Biobank EU Human Brain Project have expanded the reach of human fMRI to include faster pulse sequences and more complex analytic tools higher T field strength integration with multi scale experiments and modeling and an emphasis on integration of data across multiple scanner study sites This generation of fMRI studies goes beyond the original simplistic models that focused upon activation maps to investigate connections networks and dynamic nonlinear circuits in the brain New ways of thinking are being applied to some of our highest impact areas of societal interest ranging from addiction depression autism and brain injury to age based cognitive degeneration However while fMRI research dramatically accelerates quality assurance protocols for the MRI machines needed to generate these findings have lagged far behind Thus todayandapos s neuroimaging centers typically use outdated static phantom protocols that are now incapable of targeting quality control issues relevant to current and emerging applications The Stony Brook Dynamic Phantom is designed to address this urgent need Building upon a st generation working prototype of the phantom patent pending as well as engineering improvements in the nd generation prototype designed to increase its durability and reliability here we focus on the next logical steps to commercialization Phase I focuses on quality control and establishing added value to the market by showing that our phantomandapos s measure of dynamic fidelity provides a uniquely informative measure of data quality with direct and concrete implications for the interpretation of human data Incorporating feedback provided by our Senior Advisory Board Phase II then proceeds towards commercialization in three steps First ALA Scientific Instruments will adapt the engineering for mass production Second the academic teams at Stony Brook University and Massachusetts General Hospital Harvard Medical School will develop algorithms that use characterization of scanner noise to clean data of associated artifact for both single subject clinical applications as well as normalization across scanners for multi site research Finally hardware and software will be then integrated into one product which will be field tested by international leaders in the neuroimaging field Feedback from this group identifying potential friction points in terms of the initial learning curve and or day to day usage of the phantom will be implemented in the final design to ensure that our final manufacturing for the hardware software and documentation make the phantom as pleasant to operate and practically useful as possible At this point our device will be ready for commercial distribution Functional magnetic resonance imaging fMRI has rapidly become the dominant tool in human neuroscience research and is poised to become a transformative technology in promoting our understanding of addiction and neuropsychopharmacology However while fMRI research dramatically accelerates quality assurance protocols for the MRI machines needed to generate these findings have lagged far behind The Stony Brook Dynamic Phantom is designed to optimize detection sensitivity of newer task free resting state brain networks by providing the first commercial fMRI calibration device phantom capable of producing a dynamic brain like signal Quantitative characterization and subsequent correction of scanner specific signal distortion not only will markedly improve the detection of clinically relevant biomarkers at the level of the single patient but also will permit normalization across scanner platforms for large scale research studies collected across multiple sites