- Meeting abstract
- Open Access
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.
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