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New SPM8-based MRAC method for simultaneous PET/MR brain images: comparison with state-of-the-art non-rigid registration methods
© Izquierdo-Garcia et al; licensee Springer 2014
- Published: 29 July 2014
- Attenuation Correction
- Image Registration
- Linear Attenuation Coefficient
- Tissue Class
- Image Registration Algorithm
We describe a new MR-based attenuation correction (MRAC) method for neurological studies performed using integrated PET/MR scanners. The method, combining the advantages of image segmentation and atlas-based approaches to generate a high-resolution template, is based on the widely available SPM8 software and provides robust and accurate linear attenuation coefficients (LACs) for head while requiring minimal user interaction.
Atlas generation: 3T MR and CT images from 15 glioblastoma subjects were used to generate the high-resolution atlas. MR images were segmented into 6 tissue classes: GM, WM, CSF, soft tissue, bone and air). Tissue classes were then coregistered using an iterative diffeomorphic image registration algorithm  to form the template.
Atlas validation: The template was validated on 16 subjects. SyN  and IRTK , considered state-of-the-art for non-rigid image registration, were used for comparison. Final attenuation maps were created from the warped CT atlas following . PET images were then reconstructed using the proposed methods as well as the manufacturer’s built-in method (dual-echo Dixon-VIBE sequence)  and compared to the gold standard CT-based attenuation correction (CTAC).
Summary of voxel- and ROI-based results between our method (atlas) and the current manufacturer’s method (Dixon)
We presented a new MRAC technique for brain images acquired on simultaneous PET/MR scanners. The new approach relies on segmentation- and atlas-based features to provide robust and more accurate LACs than using state-of-art non-rigid image registration while avoiding sophisticated user input or interaction.
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This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.