Search

PET scanners with an elongated axial field of view intended to increase overall system sensitivity, such as the high-resolution research tomograph (HRRT) scanner, have been reported to produce images with decreased signals in the brain stem and cerebellum. The cause of this negative bias of the images was analyzed, and the effects of an inaccurate linear attenuation coefficient (μ-value) of tissue and bones were separately examined. Methods: A new phantom was manufactured, and 18 human subjects were recruited for the study. 18F-FDG PET images were reconstructed using attenuation coefficient maps generated by various algorithms. The algorithms included maximum a posteriori reconstruction for transmission data (MAP-TR) with default priors, MAP-TR with adjusted priors for bone (MAP-TRadj-b), MAP-TR with adjusted priors for tissue (MAP-TRadj-t), and noise-equivalent count TR and CT-TR. Results: With the CT-TR and MAP-TRadj-t algorithms, increased intensity in the brain stem and cerebellum was seen, and negative bias was reduced. With the MAP-TRadj-t algorithm, however, positive bias increased in the central region. Inappropriate attenuation coefficients of brain tissue increased the positive or negative bias of reconstructed images, especially for the central regions of the volume. Poor representation of the skull or bone also locally increased the bias in the near regions where bone detection had failed. Conclusion: An inaccurate μ-map obtained from the MAP-TR algorithm caused the bias problem for the HRRT system. The CT-TR algorithm provided a relatively more reliable μ-map that demonstrated a small degree of intensity bias. Appropriate priors for μ-values of each tissue compartment and better classification to distinguish bone from tissue are necessary for accurate attenuation correction.

REPLY: We very much appreciate the comments and suggestions of Yakushev and Fellgiebel on our paper ( [1][1]), which was the first in a series we are publishing on in vivo human molecular imaging using our newly introduced ultra-high-resolution PET-MRI fusion system ( [2][1]). Like Yakushev et al