Education & Certifications


  • M.S., Stanford University, Bioengineering (2015)
  • B.S., Northwestern University, Biomedical Engineering (2013)

Stanford Advisors


Lab Affiliations


All Publications


  • Imaging of magnetic ink patterns via off-resonance sensitivity. Magnetic resonance in medicine Perkins, S. L., Daniel, B. L., Hargreaves, B. A. 2018

    Abstract

    Printed magnetic ink creates predictable B0 field perturbations based on printed shape and magnetic susceptibility. This can be exploited for contrast in MR imaging techniques that are sensitized to off-resonance. The purpose of this work was to characterize the susceptibility variations of magnetic ink and demonstrate its application for creating MR-visible skin markings.The magnetic susceptibility of the ink was estimated by comparing acquired and simulated B0 field maps of a custom-built phantom. The phantom was also imaged using a 3D gradient echo sequence with a presaturation pulse tuned to different frequencies, which adjusts the range of suppressed frequencies. Healthy volunteers with a magnetic ink pattern pressed to the skin or magnetic ink temporary flexible adhesives applied to the skin were similarly imaged.The volume-average magnetic susceptibility of the ink was estimated to be 131 ± 3 parts per million across a 1-mm isotropic voxel (13,100 parts per million assuming a 10-μm thickness of printed ink). Adjusting the saturation frequency highlights different off-resonant regions created by the ink patterns; for example, if tuned to suppress fat, fat suppression will fail near the ink due to the off-resonance. This causes magnetic ink skin markings placed over a region with underlying subcutaneous fat to be visible on MR images.Patterns printed with magnetic ink can be imaged and identified with MRI. Temporary flexible skin adhesives printed with magnetic ink have the potential to be used as skin markings that are visible both by eye and on MR images.

    View details for PubMedID 29603366

  • A Mixed-Reality System for Breast Surgical Planning Perkins, S. L., Lin, M. A., Srinivasan, S., Wheeler, A. J., Hargreaves, B. A., Daniel, B. L., Broll, W., Regenbrecht, H., Swan, J. E., Bruder, G., Servieres, M., Sugimoto, M. IEEE. 2017: 269–74