Novel approach for a bias-free reduction of image variance in ultrasonic diffraction tomography

Xiaochuan Pan, Mark Anastasio

Research output: Contribution to journalConference article

Abstract

In this work, we considered the optimal noise control in image reconstruction in ultrasonic diffraction tomography (UDT). We revealed the existence of statistically complementary information inherent in the scattered data and proposed a linear strategy that makes use of this information to achieve a bias-free reduction of the image variance in UDT. This strategy leads to the development of an infinite class of hybrid algorithms that reconstruct UDT images from the measured scattered data. We also developed infinite classes of generalized filtered back-propagation (GFBPP) algorithms that include the widely used filtered back-propagation (FBPP) algorithm as a special member. Furthermore, we established the corresponding relationship between the hybrid and GFBPP algorithms. In the absence of noise, these hybrid and GFBPP algorithms are identical. However, they respond to noise and the effect of finite sampling differently. Our simulation studies validate these theoretical results.

Original languageEnglish (US)
Pages (from-to)1627-1630
Number of pages4
JournalProceedings of the IEEE Ultrasonics Symposium
Volume2
StatePublished - Dec 1 1998
Externally publishedYes
EventProceedings of the 1998 International Ultrasonics Symposium - Sendai, Miyagi, Jpn
Duration: Oct 5 1998Oct 8 1998

ASJC Scopus subject areas

  • Engineering(all)

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