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Reducing Loss of Image Quality Because of the Attenuation Artifact in Uncorrected PET Whole-Body Images

¹ Nuclear Medicine, Katholieke Universiteit Leuven, Leuven, Belgium

In whole-body PET, it is not unusual to shorten the study time by omitting the transmission scan and to ignore attenuation during reconstruction. If a transmission scan is available, many centers reconstruct the images with, but also without, attenuation correction. Although ignoring attenuation leads to an artifact in the reconstructed images, these images still provide valuable diagnostic information in oncologic applications. Several authors have reported that the attenuation artifact may actually increase the tumor-to-background ratio. In this study, we analyzed the causes of the artifact and proposed a new algorithm to reduce the adverse effects on visual image quality. Methods: We analyzed the causes of the attenuation artifact mathematically and numerically, and we examined its effect on tumor-to-background ratio and on signal-to-noise ratio. In addition, we showed that the attenuation artifact may lead to loss of image detail in conventional maximum-likelihood expectation maximization (MLEM) reconstruction. A new maximum-likelihood algorithm allowing negative reconstruction values (NEG-ML) was derived to reduce this loss. Results: The attenuation artifact consists of 2 components. The first component is the well-known scaling effect: The apparent activity is reduced because attenuation decreases the fraction of detected photons. The second component is a relatively smooth negative contribution that is added to attenuated regions surrounded by activity. The second component tends to increase the tumor-to-background ratio. However, a simulation experiment shows that this increase in signal may be entirely offset by an increase in noise. The negative contribution can interfere with the nonnegativity constraint of the MLEM algorithm, leading to loss of image detail in regions of high attenuation. The new NEG-ML algorithm avoids the problem by allowing negative pixel values. The algorithm is similar to MLEM in the suppression of the streak artifact but provides more anatomic information. In our department, it is in routine clinical use for reconstruction of PET whole-body images without attenuation correction. Conclusion: Ignoring attenuation may increase the tumor-to-background ratio, but this increase does not imply improved tumor detection. The NEG-ML algorithm reduces the adverse effect of the attenuation artifact on visual image quality.

Key Words: PET • attenuation correction • iterative reconstruction • contrast • tumor-to-background ratio

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