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Proceedings Paper

A recursive filter for noise reduction in statistical iterative tomographic imaging
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Paper 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.

Paper Details

Date Published: 2 February 2006
PDF: 10 pages
Proc. SPIE 6065, Computational Imaging IV, 60650X (2 February 2006); doi: 10.1117/12.660281
Show Author Affiliations
Jean-Baptiste Thibault, GE Healthcare Technologies (United States)
Charles A. Bouman, Purdue Univ. (United States)
Ken D. Sauer, Univ. of Notre Dame (United States)
Jiang Hsieh, GE Healthcare Technologies (United States)

Published in SPIE Proceedings Vol. 6065:
Computational Imaging IV
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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