Choice of initial conditions in the ML reconstruction for transmission CT with truncated projection data

Tin Su Pan, Benjamin M.W. Tsui, Charles L. Byrne

Research output: Contribution to conferencePaper

Abstract

We investigate the effects of various initial conditions in the maximum-likelihood gradient (ML-G) iterative reconstruction of transmission map when projection data suffer from the truncation of fan beam sampling. An ML-G iteration is normally initialized with a flat initial condition (FIC) - an image with a positive constant value in each pixel, rather than a zero initial condition (ZIC) - an image with a zero value in each pixel. We demonstrate that using FIC in the ML iterative reconstruction can introduce a bias to the data inside the densely sampled region (DSR), whose projection data have no truncation at every angle. To reduce this bias, we propose to use the largest right singular vector (LRSV) of the system matrix as initial condition, and demonstrate that this bias can be reduced with the usage of LRSV.

Original languageEnglish (US)
Pages1232-1236
Number of pages5
StatePublished - Dec 1 1995
Externally publishedYes
EventProceedings of the 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference. Part 1 (of 3) - San Francisco, CA, USA
Duration: Oct 21 1995Oct 28 1995

Other

OtherProceedings of the 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference. Part 1 (of 3)
CitySan Francisco, CA, USA
Period10/21/9510/28/95

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Industrial and Manufacturing Engineering

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    Pan, T. S., Tsui, B. M. W., & Byrne, C. L. (1995). Choice of initial conditions in the ML reconstruction for transmission CT with truncated projection data. 1232-1236. Paper presented at Proceedings of the 1995 IEEE Nuclear Science Symposium and Medical Imaging Conference. Part 1 (of 3), San Francisco, CA, USA, .