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

• Published in: Journal of Nuclear Medicine Vol. 49; no. 11; pp. 1875 - 1883
• Main Authors: Hofmann, Matthias, Steinke, Florian, Scheel, Verena, Charpiat, Guillaume, Farquhar, Jason, Aschoff, Philip, Brady, Michael, Scholkopf, Bernhard, Pichler, Bernd J
• Format: Journal Article
• Language: English
• Published: United States Soc Nuclear Med 01.11.2008; Society of Nuclear Medicine
• Subjects: Brain - diagnostic imaging; Databases, Factual; Humans; Magnetic Resonance Imaging - methods; Pattern Recognition, Automated - methods; Positron-Emission Tomography - methods; Reproducibility of Results; Tomography, X-Ray Computed; Whole Body Imaging
• ISSN: 0161-5505; 1535-5667; 2159-662X
• DOI: 10.2967/jnumed.107.049353

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. 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. 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. This method allows reliable MRI-based attenuation correction for human brain scans. Further work is necessary to validate the method for whole-body imaging.