Skip to main content

New SPM8-based MRAC method for simultaneous PET/MR brain images: comparison with state-of-the-art non-rigid registration methods

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)[1]. Tissue classes were then coregistered using an iterative diffeomorphic image registration algorithm [2] to form the template.

Atlas validation: The template was validated on 16 subjects. SyN [3] and IRTK [4], considered state-of-the-art for non-rigid image registration[5], were used for comparison. Final attenuation maps were created from the warped CT atlas following [6]. PET images were then reconstructed using the proposed methods as well as the manufacturer’s built-in method (dual-echo Dixon-VIBE sequence) [7] 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).

Figure 1

Comparison of LACs from a validation subject for our proposed method (A), the SyN method (B) and the manufacturer’s built-in Dixon method (C) to the gold standard CTAC (D). Image differences with respect to the gold standard CTAC of our method (E), the SyN method (F) and the Dixon method (G).

Table 1 Summary of voxel- and ROI-based results between our method (atlas) and the current manufacturer’s method (Dixon)
Figure 2

PET images from a validation subject reconstructed with our proposed method (A), with the SyN method (B) and with the manufacturer’s built-in Dixon method (C), compared with the gold standard CTAC (D). Relative changes (in % with respect to gold standard, CTAC) for our method (E), the SyN method (F) and the Dixon method (G).

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.


  1. 1.

    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

    PubMed  Article  Google Scholar 

  2. 2.

    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

    PubMed  Article  Google Scholar 

  3. 3.

    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/ 10.1016/

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  4. 4.

    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

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    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

    PubMed Central  PubMed  Article  Google Scholar 

  6. 6.

    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

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    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

    PubMed  Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to David Izquierdo-Garcia.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation


  • Attenuation Correction
  • Image Registration
  • Linear Attenuation Coefficient
  • Tissue Class
  • Image Registration Algorithm