Iterative Principal Axes Registration Method for Analysis of MR-PET Brain Images

Atam P. Dhawan, Louis K. Arata, Alejandro V. Levy, Joseph Mantil

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Computerized automatic registration of MR-PET images of the brain is of significant interest for multimodality brain image analysis. In this paper, we discuss the Principal Axes Transformation for registration of three-dimensional MR and PET images. A new brain phantom designed to test MR-PET registration accuracy determines that the Principal Axes Registration method is accurate to within an average of 1.37 mm with a standard deviation of 0.78 mm. Often the PET scans are not complete in the sense that the PET volume does not match the respective MR volume. We have developed an Iterative Principal Axes Registration (IPAR) algorithm for such cases. Partial volumes of PET can be accurately registered to the complete MR volume using the new iterative algorithm. The quantitative and qualitative analyses of MR-PET image registration are presented and discussed. Results show that the new Principal Axes Registration algorithm is accurate and practical in MR-PET correlation studies.

Original languageEnglish (US)
Pages (from-to)1079-1087
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume42
Issue number11
DOIs
StatePublished - Nov 1995
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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