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MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration

¹ Laboratory for Preclinical Imaging and Imaging Technology of the Werner Siemens-Foundation, Department of Radiology, Eberhard-Karls-University, Tuebingen, Germany; ² Max Planck Institute for Biological Cybernetics, Tuebingen, Germany; ³ Wolfson Medical Vision Laboratory, University of Oxford, Oxford, United Kingdom; and ⁴ Department of Radiology, Eberhard-Karls-University, Tuebingen, Germany

Correspondence: For correspondence or reprints contact: Bernd J. Pichler, Laboratory for Preclinical Imaging and Imaging Technology of the Werner Siemens-Foundation, Röntgenweg 13 72076, Tuebingen, Germany. E-mail: Bernd.Pichler{at}med.uni-tuebingen.de

For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating radionuclide source, or from the CT scan in a combined PET/CT scanner. In the case of PET/MRI scanners currently under development, insufficient space for the rotating source exists; the attenuation map can be calculated from the MR image instead. This task is challenging because MR intensities correlate with proton densities and tissue-relaxation properties, rather than with attenuation-related mass density. Methods: We used a combination of local pattern recognition and atlas registration, which captures global variation of anatomy, to predict pseudo-CT images from a given MR image. These pseudo-CT images were then used for attenuation correction, as the process would be performed in a PET/CT scanner. Results: For human brain scans, we show on a database of 17 MR/CT image pairs that our method reliably enables estimation of a pseudo-CT image from the MR image alone. On additional datasets of MRI/PET/CT triplets of human brain scans, we compare MRI-based attenuation correction with CT-based correction. Our approach enables PET quantification with a mean error of 3.2% for predefined regions of interest, which we found to be clinically not significant. However, our method is not specific to brain imaging, and we show promising initial results on 1 whole-body animal dataset. Conclusion: This method allows reliable MRI-based attenuation correction for human brain scans. Further work is necessary to validate the method for whole-body imaging.

Key Words: PET/MR • attenuation correction • atlas • machine learning

COPYRIGHT © 2008 by the Society of Nuclear Medicine, Inc.

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