The Noise Power Spectrum in Heavy Ion CT Based on Measurement of Residual Range Distribution

  • Yasuda, Naruomi (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Abe, Shinji (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Nishimura, Katsuyuki (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Tomita, Tetsuya (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Sato, Hitoshi (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Muraishi, Hiroshi (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Kanzaki, Takayuki (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Inada, Tetsuo (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki) ;
  • Fujisaki, Tatsuya (Department of Radiological Sciences, Ibaraki Prefectural University of Health Sciences, Ibaraki)
  • Published : 2002.09.01

Abstract

The relative electron density resolution was discussed by the noise power spectrum (NPS) in the heavy ion CT image. The heavy ion beam $\^$12/C accelerated up to 400MeV/u by RIMAC was used in this study. The two-dimensional (2-D) NPS in the CT image was obtained from the one-dimensional (1-D) NPS of the measured residual range distribution of water phantom for single projection, and the noise variance in the CT image was calculated from 2-D NPS. The technique used in the reconstruction was the filtered back-projection method with Shepp-Logan filter. The calculated value suggests the result of our previous works using the density resolution phantom, assuming that the relative electron density resolution is twice the standard deviation. Therefore, the estimation of the noise in CT images by 2-D NPS obtained the measured residual range distribution is the useful method.

Keywords