Statistical image reconstruction for lesion detection

Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detecting cancerous lesions is one major application in emission tomography. In this paper, we study statistical image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modeling the photon detection process and data noise. To explore the full potential of statistical reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters for the maximum lesion detectability. Results are validated using Monte Carlo simulations.

Original languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
EditorsM.B. Matthews
Pages153-157
Number of pages5
Volume1
Publication statusPublished - 2004
EventConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 7 2004Nov 10 2004

Other

OtherConference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/7/0411/10/04

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ASJC Scopus subject areas

  • Engineering(all)

Cite this

Qi, J. (2004). Statistical image reconstruction for lesion detection. In M. B. Matthews (Ed.), Conference Record - Asilomar Conference on Signals, Systems and Computers (Vol. 1, pp. 153-157)