Automated algorithms for improved pre-processing of magnetic relaxometry data

W. Stefan, Kelsey Boitnott Mathieu, S. L. Thrower, David Thomas Alfonso Fuentes, C. Kaffes, Javad Sovizi, John D Hazle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present a novel method to pre-process magnetic relaxation (MRX) data. The method is used to estimates the initial magnetic field generated by Super Paramagnetic Nano Particles (SPIONs) from decay curves measured by superconducting quantum interference devices (SQUIDs). The curves are measured using a MagSense MRX Instrument (PrecisionMRX, Imagion Biosystems, Albuquerque, NM). We compare the initial field estimates to the standard method used by Imagion Biosystems. As compared to the standard method our new method results in more stable estimates in the presence of noise and allows monitoring of the long term stability of the MagSense MRX instrument. We demonstrate these findings with phantom scans conducted over the period of about one year.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationPhysics of Medical Imaging
EditorsTaly Gilat Schmidt, Guang-Hong Chen, Joseph Y. Lo
PublisherSPIE
Volume10573
ISBN (Electronic)9781510616356
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Physics of Medical Imaging - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Other

OtherMedical Imaging 2018: Physics of Medical Imaging
CountryUnited States
CityHouston
Period2/12/182/15/18

Fingerprint

Magnetic relaxation
magnetic relaxation
preprocessing
estimates
Processing
SQUIDs
curves
Magnetic fields
interference
Magnetic Fields
Monitoring
decay
Noise
magnetic fields
Equipment and Supplies

Keywords

  • MRX
  • Magnetic relaxometry
  • SPION
  • iron oxide nano particles

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Stefan, W., Mathieu, K. B., Thrower, S. L., Fuentes, D. T. A., Kaffes, C., Sovizi, J., & Hazle, J. D. (2018). Automated algorithms for improved pre-processing of magnetic relaxometry data. In T. G. Schmidt, G-H. Chen, & J. Y. Lo (Eds.), Medical Imaging 2018: Physics of Medical Imaging (Vol. 10573). [105733R] SPIE. https://doi.org/10.1117/12.2293859

Automated algorithms for improved pre-processing of magnetic relaxometry data. / Stefan, W.; Mathieu, Kelsey Boitnott; Thrower, S. L.; Fuentes, David Thomas Alfonso; Kaffes, C.; Sovizi, Javad; Hazle, John D.

Medical Imaging 2018: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Joseph Y. Lo. Vol. 10573 SPIE, 2018. 105733R.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Stefan, W, Mathieu, KB, Thrower, SL, Fuentes, DTA, Kaffes, C, Sovizi, J & Hazle, JD 2018, Automated algorithms for improved pre-processing of magnetic relaxometry data. in TG Schmidt, G-H Chen & JY Lo (eds), Medical Imaging 2018: Physics of Medical Imaging. vol. 10573, 105733R, SPIE, Medical Imaging 2018: Physics of Medical Imaging, Houston, United States, 2/12/18. https://doi.org/10.1117/12.2293859
Stefan W, Mathieu KB, Thrower SL, Fuentes DTA, Kaffes C, Sovizi J et al. Automated algorithms for improved pre-processing of magnetic relaxometry data. In Schmidt TG, Chen G-H, Lo JY, editors, Medical Imaging 2018: Physics of Medical Imaging. Vol. 10573. SPIE. 2018. 105733R https://doi.org/10.1117/12.2293859
Stefan, W. ; Mathieu, Kelsey Boitnott ; Thrower, S. L. ; Fuentes, David Thomas Alfonso ; Kaffes, C. ; Sovizi, Javad ; Hazle, John D. / Automated algorithms for improved pre-processing of magnetic relaxometry data. Medical Imaging 2018: Physics of Medical Imaging. editor / Taly Gilat Schmidt ; Guang-Hong Chen ; Joseph Y. Lo. Vol. 10573 SPIE, 2018.
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