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Extrapolation of System Matrices in Magnetic Particle Imaging. | LitMetric

AI Article Synopsis

  • Magnetic particle imaging uses superparamagnetic iron-oxide particles to create detailed images in a specific area through a complex calibration process involving a robot.
  • This calibration usually takes a long time and requires significant memory because it needs to cover not just the targeted area but also the surrounding space.
  • The study introduces a new method for extrapolating data that only focuses on the specific area of interest, which significantly reduces both calibration time and memory usage while still delivering high-quality, artifact-free images.

Article Abstract

Magnetic particle imaging exploits the non-linear magnetization of superparamagnetic iron-oxide particles to generate a tomographic image in a defined field-of-view. For reconstruction of the particle distribution, a time-consuming calibration step is required, in which system matrices get measured using a robot. To achieve artifact-free images, system matrices need to cover not only the field-of-view but also a larger area around it. Especially for large measurements- inevitable for future clinical application- this leads to long calibration time and high consumption of persistent memory. In this work, we analyze the signal in the outer part of the system matrix and motivate the usage of extrapolation methods to computationally expand the system matrix after restricting the calibration to the field-of-view. We propose a suitable extrapolation method and show its applicability on measured 2D and 3D data. In doing so, we achieve a considerable reduction of calibration time and consumption of persistent memory while preserving an artifact-free result.

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Source
http://dx.doi.org/10.1109/TMI.2022.3224310DOI Listing

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