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

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

doi:10.1109/10.469374

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 journal › Article › peer-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 language	English (US)
Pages (from-to)	1079-1087
Number of pages	9
Journal	IEEE Transactions on Biomedical Engineering
Volume	42
Issue number	11
DOIs	https://doi.org/10.1109/10.469374
State	Published - Nov 1995
Externally published	Yes

All Science Journal Classification (ASJC) codes

Biomedical Engineering

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title = "Iterative Principal Axes Registration Method for Analysis of MR-PET Brain Images",

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.",

author = "Dhawan, {Atam P.} and Arata, {Louis K.} and Levy, {Alejandro V.} and Joseph Mantil",

note = "Funding Information: Manuscript received April 15, 1993; revised July 13, 1995. The work of A. V. Levy was supported in pm by NIMH Grant MH44832, and the U.S. Department of Energy under Contract DE-ACO2-76CHooO16. A. P. Dhawan is with the Depamnent of Electrical and Computer Engineering and Radiology, University of Cincinnati, Cincinnati, OH 45221 USA. L. K. Arata was with the University of Cincinnati, Cincinnati, OH 45221 USA. He is now with Picker International, Cleveland, OH 44109 USA. A. V. Levy is with Brookhaven National Laboratory, Upton, NY 11973 USA. J. Mantil is with the Departments of Magnetic Resonance Imaging, and Nuclear Medicine, Kettering Medical Center, Kettering, OH 45429 USA. IEEE Log Number 9414806.",

year = "1995",

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AU - Arata, Louis K.

AU - Levy, Alejandro V.

AU - Mantil, Joseph

N1 - Funding Information: Manuscript received April 15, 1993; revised July 13, 1995. The work of A. V. Levy was supported in pm by NIMH Grant MH44832, and the U.S. Department of Energy under Contract DE-ACO2-76CHooO16. A. P. Dhawan is with the Depamnent of Electrical and Computer Engineering and Radiology, University of Cincinnati, Cincinnati, OH 45221 USA. L. K. Arata was with the University of Cincinnati, Cincinnati, OH 45221 USA. He is now with Picker International, Cleveland, OH 44109 USA. A. V. Levy is with Brookhaven National Laboratory, Upton, NY 11973 USA. J. Mantil is with the Departments of Magnetic Resonance Imaging, and Nuclear Medicine, Kettering Medical Center, Kettering, OH 45429 USA. IEEE Log Number 9414806.

PY - 1995/11

Y1 - 1995/11

N2 - 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.

AB - 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.

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

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