A recursive filter for noise reduction in statistical iterative tomographic imaging

Jean-Baptiste Thibault; Charles A. Bouman; Ken D. Sauer; Jiang Hsieh

doi:10.1117/12.660281

2 February 2006 A recursive filter for noise reduction in statistical iterative tomographic imaging

Jean-Baptiste Thibault, Charles A. Bouman, Ken D. Sauer, Jiang Hsieh

Proceedings Volume 6065, Computational Imaging IV; 60650X (2006) https://doi.org/10.1117/12.660281
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

Computed Tomography (CT) screening and pediatric imaging, among other applications, demand the development of more efficient reconstruction techniques to diminish radiation dose to the patient. While many methods are proposed to limit or modulate patient exposure to x-ray at scan time, the resulting data is excessively noisy, and generates image artifacts unless properly corrected. Statistical iterative reconstruction (IR) techniques have recently been introduced for reconstruction of low-dose CT data, and rely on the accurate modeling of the distribution of noise in the acquired data. After conversion from detector counts to attenuation measurements, however, noisy data usually deviate from simple Gaussian or Poisson representation, which limits the ability of IR to generate artifact-free images. This paper introduces a recursive filter for IR, which conserves the statistical properties of the measured data while pre-processing attenuation measurements. A basic framework for inclusion of detector electronic noise into the statistical model for IR is also presented. The results are shown to successfully eliminate streaking artifacts in photon-starved situations.

Citation Download Citation

Jean-Baptiste Thibault, Charles A. Bouman, Ken D. Sauer, and Jiang Hsieh "A recursive filter for noise reduction in statistical iterative tomographic imaging", Proc. SPIE 6065, Computational Imaging IV, 60650X (2 February 2006); https://doi.org/10.1117/12.660281

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