LAUSR

Purpose The effects of regularizing priors on the maximum likelihood (ML) reconstruction of activity patterns in Positron Emission Tomography (PET) were assessed. Methods Two edge‐preserving priors (one originally proposed by Nuyts et al. and nowadays implemented and commercialized by General Electric Medical Systems as Q.Clear software, and a second one originally proposed by Rapisarda et al. and our group) were assessed and compared to a standard Ordered Subset (OS)‐ML reconstruction, assumed as reference. The main difference between the two priors is that Nuyts prior (NY‐p) penalizes relative voxel differences while Rapisarda prior (RP‐p) absolute ones. Prior parameters were selected by imposing a reference noise texture inside uniform regions with activity comparable to that measured in 18F‐FluoroDeoxyGlucose (FDG) patient livers overall the field of view. Comparisons were then made: (a) on phantom data in terms of sphere recovery coefficients, ability to correctly reconstruct uniform irregularly shaped objects and heterogeneous patterns in patient backgrounds; (b) on patient data in terms of lesion detectability and image quality. Results On phantoms, both priors succeeded in improving all the assessed features with respect to standard OS‐ML reconstruction, mainly thanks to the better signal convergence and to the noise breakup control. On 10 mm spheres, an average recovery coefficient augment of 9% (NY‐p) and 34% (RP‐p) was obtained; homogeneity of uniform activity objects augmented of 4% (NY‐p) and 11% (RP‐p); accuracy in reconstructing heterogeneous lesions improved on average of 5% (NY‐p) and 15% (RP‐p). On patients, lesion detectability resulted improved (on 27 of 30 lesions), regardless of lesion anatomical districts and position in the scanner field of view. NY‐p provides a spatial resolution and a noise texture more uniform in the field of view and an image quality similar to standard OS‐ML. RP‐p has instead a behavior more dependent on the local counting statistics that imposes a trade‐off between spatial resolution uniformity and noise texture homogeneity. Conclusions The assessed regularizing priors improve PET uptake pattern reconstruction accuracy. Therefore, they should be considered both for oncological lesion detection and uptake spatial distribution assessment. Pitfalls and open challenges are also discussed.