Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry

S. L. Thrower, D. Fuentes, W. Stefan, J. Sovizi, K. Mathieu, J. D. Hazle

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

Abstract

Ovarian cancer survival rates could be greatly improved through effective early detection. However, several clinical studies have shown that proposed screening methodologies have no impact on overall survival. Our lab is participating in the development of a novel nanoparticle imaging device that can be incorporated as a third-line test to improve the specificity and sensitivity of the overall screening program. The device's highly sensitive detectors can detect the residual magnetic field of only those nanoparticles that have become bound to cancer cells via specific antibody interactions. However, the reconstruction of the bound particle distribution from this residual field map is challenging due to the highly ill-posed nature of the inverse problem. Our lab has developed a sparse reconstruction algorithm to overcome this challenge. Here, we present the results of a blinded phantom study to simulate the pre-clinical scenario of detecting a tumor signal in the presence of a large signal from bound particles in the liver. Overall, our algorithm identified the correct location of bound particle sources with 84% accuracy. We were able to detect as little as 1.6ug of bound particles with 100% accuracy when the source was alone, and as little as 3.13ug when there was a stronger source present. We also show the effect of manual and automatic parameter selection on the performance of the algorithm. These results provide valuable information about the expected performance of the algorithm that we can use to optimize the design of future small animal studies as we work to bring this novel technology to the clinic.

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
ISBN (Electronic)9781510616356
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Physics of Medical Imaging - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10573
ISSN (Print)1605-7422

Other

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

Fingerprint

Sensitivity and Specificity
sensitivity
Nanoparticles
Screening
screening
cancer
Equipment and Supplies
nanoparticles
Magnetic Fields
antibodies
Inverse problems
liver
Antibodies
Liver
Ovarian Neoplasms
animals
Tumors
Neoplasms
Animals
tumors

Keywords

  • Superparamagnetic relaxometry
  • early detection
  • magnetic inverse problem
  • magnetic particle imaging
  • magnetic relaxometry
  • nanoparticle
  • ovarian cancer
  • sparse reconstruction

ASJC Scopus subject areas

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

Cite this

Thrower, S. L., Fuentes, D., Stefan, W., Sovizi, J., Mathieu, K., & Hazle, J. D. (2018). Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. In T. G. Schmidt, G-H. Chen, & J. Y. Lo (Eds.), Medical Imaging 2018: Physics of Medical Imaging [1057327] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573). SPIE. https://doi.org/10.1117/12.2293796

Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. / Thrower, S. L.; Fuentes, D.; Stefan, W.; Sovizi, J.; Mathieu, K.; Hazle, J. D.

Medical Imaging 2018: Physics of Medical Imaging. ed. / Taly Gilat Schmidt; Guang-Hong Chen; Joseph Y. Lo. SPIE, 2018. 1057327 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10573).

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

Thrower, SL, Fuentes, D, Stefan, W, Sovizi, J, Mathieu, K & Hazle, JD 2018, Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. in TG Schmidt, G-H Chen & JY Lo (eds), Medical Imaging 2018: Physics of Medical Imaging., 1057327, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10573, SPIE, Medical Imaging 2018: Physics of Medical Imaging, Houston, United States, 2/12/18. https://doi.org/10.1117/12.2293796
Thrower SL, Fuentes D, Stefan W, Sovizi J, Mathieu K, Hazle JD. Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. In Schmidt TG, Chen G-H, Lo JY, editors, Medical Imaging 2018: Physics of Medical Imaging. SPIE. 2018. 1057327. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2293796
Thrower, S. L. ; Fuentes, D. ; Stefan, W. ; Sovizi, J. ; Mathieu, K. ; Hazle, J. D. / Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. Medical Imaging 2018: Physics of Medical Imaging. editor / Taly Gilat Schmidt ; Guang-Hong Chen ; Joseph Y. Lo. SPIE, 2018. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
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