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Magnetic Particle Imaging Emerges into Preclinical Research: Hardware, Nanoparticles, and Animal Models
February 26, 2019 @ 6:30 pm - 8:30 pm PST
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Location: Western Digital, 1710 Automation Parkway, San Jose, CA
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Cookies, Conversation & Pizza at 6:30 P.M.
Talk & Questions at 7:00 – 8:30 P.M.
Patrick W Goodwill 1*, Elaine Yu 1,2, Daniel Hensley 1,2, Ryan Orendorff 1,2,
Zhi Wei Tay 2, Justin Konkle 1, Jeff Gaudet 1, Robert B. Kettlewell 1,
John Schmidt 1, Matthias Weber 1, Bo Zheng 2, Steven Conolly 2
1 Magnetic Insight, Inc., Alameda, CA
2 Departments of Bioengineering and EECS, University of California, Berkeley, Berkeley, CA
Magnetic Particle Imaging (MPI) is an emerging tracer imaging technique that directly detects superparamagnetic iron oxide (SPIO) nanoparticles with exceptional contrast and high sensitivity at millimeter-scale resolutions.1 MPI’s contrast is similar to nuclear medicine, but without the complex workflow, safety, and half-life limitations of a radioactive tracer. These capabilities help fill in some of the gaps of current technologies and position MPI as complementary to anatomical imaging techniques such as MRI and X-ray/CT.
The magnetic properties of the nanoparticle tracers seen by MPI define the technique’s image resolution and sensitivity. This is because the MPI signal arise from the interaction between a magnetic nanoparticle, a strong magnetic field gradient, and a time varying magnetic drive field.2,3 To improve MPI’s spatial resolution, we and others have researched SPIOs with varying core diameters, since Langevin steady-state physics predicts a cubic resolution improvement with increasing SPIO core size. This effort has already produced exciting results, effectively reducing the FWHM resolution by a factor of two and increasing sensitivity an order of magnitude.4 However, much remains unknown about the physical mechanisms and trade-offs for further improvements.
Our group and others have begun testing MPI on animal models. We have used long circulating PEG coated nanoparticles to image the brain, detect cancers,5 detect gut bleeds,6 and visualize traumatic brain injury.7 MPI’s high sensitivity also makes it well suited for cell tracking. For example, we have tracked therapeutic neural stem cells for three months8 and the biodistribution of mesenchymal stem cells post administration.9 In the future, we see opportunities for MPI tracking of immune therapies or immune cells to sites of inflammation such as cancer.
(a) Blood pool image of a rat brain using a PEG coated tracer. (b) Breast Cancer (MDA-MB-231) visualization using the EPR effect using the same PEG coated tracer.5 (c) The cancer location detected with MPI matches with bioluminescence. (d) MPI is inherently linearly quantitative and can quantitate the total amount of tracer in the tumor and blood pool.5 (e) Tracking macrophages (RAW264.7) in a mouse brain. (f) Tracking islet cells implanted under the kidney capsule at day 14 post-transplant.10 (g) Tracking 5×106 mesenchymal immediately after tail vein administration.9 (h) Commercial small animal MPI imager.
After more than a decade in development, MPI has now emerged from the engineering laboratory and into commercial products. This is important as MPI hardware is completely distinct from other magnetic imaging hardware; MPI scans cannot be obtained with an MRI scanner. We now look to the future as our group and others continue to push the limits of the technique as we scale MPI to clinical sizes.
1. Gleich, B. & Weizenecker, J. Nature 435, 1214–1217 (2005).
2. Goodwill, P. W. & Conolly, S. M. IEEE Trans Med Imaging 29, 1851–1859 (2010).
3. Rahmer, J., Weizenecker, J., Gleich, B. & Borgert, J. BMC Med Imaging 9, 4 (2009).
4. Kemp, S. J., Matthew Ferguson, R., Khandhar, A. P. & Krishnan, K. M. RSC Adv 6, 77452–77464 (2016).
5. Yu, E. Y. et al. Nano Lett 17, 1648–1654 (2017).
6. Yu, E. Y. et al. ACS Nano 11, 12067–12076 (2017).
7. Orendorff, R. et al. Phys Med Biol 62, 3501–3509 (2017).
8. Zheng, B. et al. Sci Rep 5, 14055 (2015).
9. Zheng, B. et al. Theranostics (2015). 10. Wang, P. et al. Quant. Imaging Med. Surg. 8, 114–122 (2018).
Patrick W Goodwill’s Bio:
Dr. Goodwill has spent the last 9 years leading the development team in Magnetic Particle Imaging at UC Berkeley. In his role, he has designed and built five prototype MPI scanners, and developed the x-space reconstruction systems theory that enables quantitative, high-quality MPI images. Dr. Goodwill has a PhD in Bioengineering from UC Berkeley, and a B.S. and M.S. in Electrical Engineering from Stanford University. Prior to UC Berkeley, Dr. Goodwill designed microprocessors at Intel Corporation.