Gaussian process classification of superparamagnetic relaxometry data: Phantom study.

Journal: Artificial intelligence in medicine
PMID: 28911905

Abstract

MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIONs) enables the reconstruction of SPIONs spatial distribution and hence tumor detection. However, image reconstruction often requires solving an ill-posed inverse problem that is computationally challenging and sensitive to measurement uncertainty. Moreover, an additional image processing module is required to automatically detect and localize the tumor in the reconstructed image.

Authors

  • Javad Sovizi
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States. Electronic address: jsovizi@mdanderson.org.
  • Kelsey B Mathieu
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States.
  • Sara L Thrower
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States.
  • Wolfgang Stefan
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States.
  • John D Hazle
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030.
  • David Fuentes
    Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, United States.

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