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.
In this planning grant, we will provide a roadmap that will allow us to develop Magnetic Particle Imaging (MPI) as a method for imaging the function of the human brain in health and disease. By producing a method that allows us to see the brain in operation with a clarity of up to 100-fold higher than existing MRI based methods, we hope to significantly impact our understanding of disease mechanisms.
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|Tay, Zhi Wei; Hensley, Daniel W; Vreeland, Erika C et al. (2017) The Relaxation Wall: Experimental Limits to Improving MPI Spatial Resolution by Increasing Nanoparticle Core size. Biomed Phys Eng Express 3:|
|Davids, Mathias; Guérin, Bastien; Malzacher, Matthias et al. (2017) Predicting Magnetostimulation Thresholds in the Peripheral Nervous System using Realistic Body Models. Sci Rep 7:5316|
|Zheng, Bo; Goodwill, Patrick W; Dixit, Neerav et al. (2017) Optimal Broadband Noise Matching to Inductive Sensors: Application to Magnetic Particle Imaging. IEEE Trans Biomed Circuits Syst 11:1041-1052|
|Khandhar, A P; Keselman, P; Kemp, S J et al. (2017) Evaluation of PEG-coated iron oxide nanoparticles as blood pool tracers for preclinical magnetic particle imaging. Nanoscale 9:1299-1306|
|Orendorff, Ryan; Peck, Austin J; Zheng, Bo et al. (2017) First in vivo traumatic brain injury imaging via magnetic particle imaging. Phys Med Biol 62:3501-3509|
|Mason, Erica E; Cooley, Clarissa Z; Cauley, Stephen F et al. (2017) Design analysis of an MPI human functional brain scanner. Int J Magn Part Imaging 3:|
|Keselman, Paul; Yu, Elaine Y; Zhou, Xinyi Y et al. (2017) Tracking short-term biodistribution and long-term clearance of SPIO tracers in magnetic particle imaging. Phys Med Biol 62:3440-3453|
|Yu, Elaine Y; Chandrasekharan, Prashant; Berzon, Ran et al. (2017) Magnetic Particle Imaging for Highly Sensitive, Quantitative, and Safe in Vivo Gut Bleed Detection in a Murine Model. ACS Nano 11:12067-12076|
|Cauley, Stephen F; Setsompop, Kawin; Bilgic, Berkin et al. (2017) Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction. Magn Reson Med 78:1093-1099|
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