Signal encoding in magnetic particle imaging: properties of the system function

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• 作者：Jürgen Rahmer (1)
  Jürgen Weizenecker (1)
  Bernhard Gleich (1)
  J?rn Borgert (1)
  
• 刊名：BMC Medical Imaging
• 出版年：2009
• 出版时间：December 2009
• 年：2009
• 卷：9
• 期：1
• 全文大小：2749KB
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  15. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2342/9/4/prepub
  
• 作者单位：Jürgen Rahmer (1)
  Jürgen Weizenecker (1)
  Bernhard Gleich (1)
  J?rn Borgert (1)
  
  1. Philips Research Europe -Hamburg, R?ntgenstra?e 24-26, 22335, Hamburg, Germany
  

文摘

Background Magnetic particle imaging (MPI) is a new tomographic imaging technique capable of imaging magnetic tracer material at high temporal and spatial resolution. Image reconstruction requires solving a system of linear equations, which is characterized by a "system function" that establishes the relation between spatial tracer position and frequency response. This paper for the first time reports on the structure and properties of the MPI system function. Methods An analytical derivation of the 1D MPI system function exhibits its explicit dependence on encoding field parameters and tracer properties. Simulations are used to derive properties of the 2D and 3D system function. Results It is found that for ideal tracer particles in a harmonic excitation field and constant selection field gradient, the 1D system function can be represented by Chebyshev polynomials of the second kind. Exact 1D image reconstruction can thus be performed using the Chebyshev transform. More realistic particle magnetization curves can be treated as a convolution of the derivative of the magnetization curve with the Chebyshev functions. For 2D and 3D imaging, it is found that Lissajous excitation trajectories lead to system functions that are closely related to tensor products of Chebyshev functions. Conclusion Since to date, the MPI system function has to be measured in time-consuming calibration scans, the additional information derived here can be used to reduce the amount of information to be acquired experimentally and can hence speed up system function acquisition. Furthermore, redundancies found in the system function can be removed to arrive at sparser representations that reduce memory load and allow faster image reconstruction.