Bayesian multiresolution algorithm for pet reconstruction

T. Frese, C. A. Bouman, N. C. Rouze, G. D. Hutchins, K. Sauer

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

We introduce a spatially non-homogeneous adaptive image model and multiresolution reconstruction algorithm for Bayesian tomographic reconstruction. In contrast to existing approaches, the proposed image model is formulated in a multiresolution wavelet domain and relies on training data to incorporate the expected characteristics of typical reconstructions. The actual tomographic reconstruction is performed in the space domain to simplify enforcement of the positivity constraint. We apply the proposed algorithm to simulated data and to data acquired using the IndyPET dedicated research scanner. Our experimental results indicate that our algorithm can improve reconstruction quality over fixed resolution Bayesian methods.

Original languageEnglish (US)
Pages613-616
Number of pages4
StatePublished - Dec 1 2000
Externally publishedYes
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

Cite this

Frese, T., Bouman, C. A., Rouze, N. C., Hutchins, G. D., & Sauer, K. (2000). Bayesian multiresolution algorithm for pet reconstruction. 613-616. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.