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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Resource-Related Research Projects (R24)
Project #
1R24MH106053-01
Application #
8827525
Study Section
Special Emphasis Panel (ZMH1-ERB-C (09))
Program Officer
Farber, Gregory K
Project Start
2014-09-26
Project End
2017-06-30
Budget Start
2014-09-26
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
$523,879
Indirect Cost
$123,579
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Cauley, Stephen F; Setsompop, Kawin; Bilgic, Berkin et al. (2016) Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction. Magn Reson Med :
Zheng, Bo; von See, Marc P; Yu, Elaine et al. (2016) Quantitative Magnetic Particle Imaging Monitors the Transplantation, Biodistribution, and Clearance of Stem Cells In Vivo. Theranostics 6:291-301
Bauer, L M; Hensley, D W; Zheng, B et al. (2016) Eddy current-shielded x-space relaxometer for sensitive magnetic nanoparticle characterization. Rev Sci Instrum 87:055109
Dhavalikar, R; Hensley, D; Maldonado-Camargo, L et al. (2016) Finite magnetic relaxation in x-space magnetic particle imaging: Comparison of measurements and ferrohydrodynamic models. J Phys D Appl Phys 49:
Tay, Zhi Wei; Goodwill, Patrick W; Hensley, Daniel W et al. (2016) A High-Throughput, Arbitrary-Waveform, MPI Spectrometer and Relaxometer for Comprehensive Magnetic Particle Optimization and Characterization. Sci Rep 6:34180
Croft, Laura R; Goodwill, Patrick W; Konkle, Justin J et al. (2016) Low drive field amplitude for improved image resolution in magnetic particle imaging. Med Phys 43:424
Bauer, Lisa M; Situ, Shu F; Griswold, Mark A et al. (2015) Magnetic Particle Imaging Tracers: State-of-the-Art and Future Directions. J Phys Chem Lett 6:2509-17
Zheng, Bo; Vazin, Tandis; Goodwill, Patrick W et al. (2015) Magnetic Particle Imaging tracks the long-term fate of in vivo neural cell implants with high image contrast. Sci Rep 5:14055