Abstract
In this work, we further validate a CT and attenuation map (μ-map) synthesis algorithm [1]. The CT synthesis method relies on a pre-acquired set of aligned MRI/CT pairs from multiple subjects. Each MRI from the database is non-rigidly registered to the target MRI. The CTs in the database are then mapped using the same transformation to the target MRI. A local image similarity measure between the target MRI and the set of registered MRIs is used as a surrogate of the underlying morphological similarity. Finally, the synthetic CT is generated using a voxel-wise weighting scheme, and converted to linear attenuation coefficients by a piecewise linear transformation. Following the proposed method, a pseudo CT (pCT) was generated using only the MRI of the subject and compared to the ground truth CT, validating the accuracy of the CT synthesis. A PET image (PETpCT) was then reconstructed with an off-line version of the Siemens Healthcare reconstruction software using the pCT μ-map, and compared with the gold standard PET reconstructed using the CT μ-map. We validated our method for brain-related applications with 16 subjects and compared our solution to: a simpler atlas-based method, named the best-atlas method,
Highlights
The CT synthesis method relies on a pre-acquired set of aligned MRI/CT pairs from multiple subjects
A local image similarity measure between the target MRI and the set of registered MRIs is used as a surrogate of the underlying morphological similarity
More accurate results are reached with the proposed method compared to the best-atlas method, which demonstrates the advantages of synthesising CTs at a local scale instead of a global scale (Figure 1)
Summary
The CT synthesis method relies on a pre-acquired set of aligned MRI/CT pairs from multiple subjects. Attenuation correction synthesis for hybrid PETMR scanners: validation for brain study applications We further validate a CT and attenuation map (μ-map) synthesis algorithm [1].
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