Magnetic Particle Imaging (MPI) for Functional Brain Imaging in Humans

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 DESCRIPTION (provided by applicant): In this planning grant we propose several engineering developments to advance Magnetic Particle Imaging (MPI) to replace MRI as the next-generation functional brain imaging tool for human neuroscience. We assemble a group of technology experts to solve a myriad of identified and unidentified barriers, we employ simulation and bench-top experiments to characterize and test solutions for these technical obstacles and validate solutions by bench testing specific sub-sections of the imager. Finally we simulate the overall performance of the planned device and assess its benefit for human functional brain imaging. MPI is a young but extremely promising technology that uses the nonlinear magnetic response of iron- oxide nanoparticles to localize their presence in the body. MPI directly detects the nanoparticle's magnetization rather than using secondary effects on the Magnetic Resonance relaxation times. Thus, while MPI and MRI share many technologies, the MPI method does not use the MR phenomena in any way. Our plan is to detect the activation-induced and resting-state changes in the iron-oxide concentration in the cerebral capillary network by monitoring the local iron oxide concentration (and thus local Cerebral Blood Volume, CBV). This CBV-contrast source is well-proven in animal and human fMRI studies which detect CBV changes by MRI using the same iron-oxide agents. But, by developing MPI as the detection modality, we show that there is a potential 120-fold increase in the contrast-to-noise ratio (CNR) of neuronal activation. This astronomical detection benefit dwarfs any potential benefit envisioned by improving MRI technology. For example, given that the BOLD CNR scales with the square of the magnet strength, this increase in CNR would be equivalent to a 30 Tesla MRI scanner, which is clearly infeasible. We envision the sensitivity boon will have an instantaneous and revolutionary impact on neuroscience. It will eliminate the need to perform group averaging to see an activation or networks, bringing analysis to the individual level needed to impact clinical medicine. By improving the basic detection methodology by 100 fold, we hope to revolutionize non-invasive functional imaging methods applicable to the human brain in health and disease.