Objective: Recently, whole-body positron emission tomography (PET) examination has greatly developed. To reduce the overall examination time, the transmission scan has been increasingly shortened. Many noise-reduction processes have been developed for count-limited transmission data. Segmented attenuation correction (SAC) is one method by which the pixel values of transmission image are transformed into several groups. The median root prior-ordered subset convex (MRP-OSC) algorithm is another method that is applicable to control the noise level on the basis that the change of the pixel value is locally monotonous. This article presents an alternative approach on the basis of the Bayesian iterative reconstruction technique incorporating a median prior and an anatomical prior from the segmented mu-map for count-limited transmission data.
Methods: The proposed method is based on the Bayesian iterative reconstruction technique. The median prior and the anatomical prior are represented as two Gibbs distributions. The product of these distributions was used as a penalty function.
Results: In the thorax simulation study, the mean square error from the true transmission image of the presented method (5.74 x 10(-5)) was lower than MRP-OSC (6.72 x 10(-5)) and SAC (7.08 x 10(-5)). The results indicate that the noise of the image reconstructed from the proposed technique was decreased more than that of MRP-OSC without segmentation error such as that of an SAC image. In the thorax phantom study, the emission image that was corrected using the proposed technique displayed little noise and bias (27.42 +/- 0.96 kBq/ml, calculated from a region of interest drawn on the liver of the phantom); it was very similar to the true value (28.0 kBq/ml).
Conclusions: The proposed method is effective for reducing propagation of noise from transmission data to emission data without loss of the quantitative accuracy of the PET image.
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