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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
EJNMMI Physics volume 1, Article number: A29 (2014)
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).
The qualitative and quantitative analysis of the attenuation maps revealed that the SPM8-based method produces very robust results (Figure 1). In terms of the PET data quantification, we observed improvements of > 70% compared to the VIBE-based method (Table 1 and Figure 2). When compared to SyN-based image registration, the SPM8 approach showed improved global results on the brain area (Figures 1 and 2).
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.
Ashburner J, Friston KJ: Unified segmentation. Neuroimage 2005, 26: 839–851. doi:10.1016/j.neuroimage.2005.02.018 10.1016/j.neuroimage.2005.02.018
Ashburner J: A fast diffeomorphic image registration algorithm. Neuroimage 2007, 38: 95–113. doi:10.1016/j.neuroimage.2007.07.007 10.1016/j.neuroimage.2007.07.007
Avants BB, Epstein CL, Grossman M, Gee JC: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis 2008, 12: 26–41. doi:10.1016/j.media.2007.06.004 10.1016/j.media.2007.06.004
Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, Hawkes DJ: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 1999,18(8):712–721. 10.1109/42.796284
Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang MC, et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 2009, 46: 786–802. doi:10.1016/j.neuroimage.2008.12.037 10.1016/j.neuroimage.2008.12.037
Burger C, Goerres G, Schoenes S, Buck A, Lonn AH, Von Schulthess GK: PET attenuation coefficients from CT images: experimental evaluation of the transformation of CT into PET 511-keV attenuation coefficients. Eur J Nucl Med Mol Imaging 2002, 29: 922–7. doi:10.1007/s00259E002E0796E3 10.1007/s00259-002-0796-3
Martinez-Moller A, Souvatzoglou M, Delso G, Bundschuh RA, Chefd'hotel C, Ziegler SI, et al.: Tissue classification as a potential approach for attenuation correction in whole-body PET/MRI: evaluation with PET/CT data. J Nucl Med 2009, 50: 520–6. doi: 10.2967/jnumed.108.054726 10.2967/jnumed.108.054726
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Izquierdo-Garcia, D., Chen, K.T., Hansen, A.E. et al. New SPM8-based MRAC method for simultaneous PET/MR brain images: comparison with state-of-the-art non-rigid registration methods. EJNMMI Phys 1, A29 (2014) doi:10.1186/2197-7364-1-S1-A29
- Attenuation Correction
- Image Registration
- Linear Attenuation Coefficient
- Tissue Class
- Image Registration Algorithm