Attenuation correction for hybrid MR/PET scanners: a comparison study
Attenuation correction for hybrid MR/PET scanners: a comparison study
Attenuation correction of PET data acquired in hybrid MR/PET scanners is still a challenge. Different methods have been adopted by several groups to obtain reliable attenuation maps (mu-maps). In this study we compare three methods: MGH, UCL, Neural-Network. The MGH method is based on an MR/CT templ...
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Personal Name(s): | Rota Kops, Elena (Corresponding author) |
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Ribeiro, Andre / Caldeira, Liliana / Hautzel, Hubertus / Lukas, Mathias / Antoch, Gerald / Lerche, Christoph / Shah, N. J. | |
Contributing Institute: |
Physik der Medizinischen Bildgebung; INM-4 JARA-BRAIN; JARA-BRAIN |
Published in: | EJNMMI Physics, 2 (2015) Suppl 1, S. A38 - |
Imprint: |
Berlin
Springer Open
2015
|
PubMed ID: |
26956295 |
DOI: |
10.1186/2197-7364-2-S1-A38 |
Document Type: |
Journal Article |
Research Program: |
Neuroimaging |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1186/2197-7364-2-S1-A38 in citations.
Attenuation correction of PET data acquired in hybrid MR/PET scanners is still a challenge. Different methods have been adopted by several groups to obtain reliable attenuation maps (mu-maps). In this study we compare three methods: MGH, UCL, Neural-Network. The MGH method is based on an MR/CT template obtained with the SPM8 software. The UCL method uses a database of MR/CT pairs. Both generate mu-maps from MP-RAGE images. The feed-forward neural-network from Juelich (NN-Juelich) requires two UTE images; it generates segmented mu-maps. Data from eight subjects (S1-S8) measured in the Siemens 3T MR-BrainPET scanner were used. Corresponding CT images were acquired. The resulting mu-maps were compared against the CT-based mu-maps for each subject and method. Overlapped voxels and Dice similarity coefficients, D, for bone, soft-tissue and air regions, and relative differences images were calculated. The true positive (TP) recognized voxels for the whole head were 79.9% (NN-Juelich, S7) to 92.1% (UCL method, S1). D values of the bone were D=0.65 (NN-Juelich, S1) to D=0.87 (UCL method, S1). For S8 the MHG method failed (TP=76.4%; D=0.46 for bone). D values shared a common tendency in all subjects and methods to recognize soft-tissue as bone. The relative difference images showed a variation of -10.9% - +10.1%; for S8 and MHG method the values were -24.5% and +14.2%. A preliminary comparison of three methods for generation of mu-maps for MR/PET scanners is presented. The continuous methods (MGH, UCL) seem to generate reliable mu-maps, whilst the binary method seems to need further improvement. Future work will include more subjects, the reconstruction of corresponding PET data and their comparison. |