  Imaging distributions of radioactive sources plays a substantial role in
nuclear medicine as well as in monitoring nuclear waste and its deposit. Coded
Aperture Imaging has been proposed as an alternative to parallel or pinhole
collimators, but requires image reconstruction as an extra step. Multiple
reconstruction methods with varying run time and computational complexity have
been proposed. Yet, no quantitative comparison between the different
reconstruction methods has been carried out so far. This paper focuses on a
comparison based on three sets of hot-rod phantom images captured with an
experimental Gamma-camera consisting of a Tungsten-based MURA mask with a 2mm
thick 256x256 pixelated CdTe semiconductor detector coupled to a Timepix
readout circuit. Analytical reconstruction methods, MURA Decoding, Wiener
Filter and a convolutional Maximum Likelihood Expectation Maximization (MLEM)
algorithm were compared to data-driven Convolutional Encoder-Decoder (CED)
approaches. The comparison is based on the contrast-to-noise ratio as it has
been previously used to assess reconstruction quality. For the given set-up,
MURA Decoding, the most commonly used CAI reconstruction method, provides
robust reconstructions despite the assumption of a linear model. For single
image reconstruction, however, MLEM performed best among analytical
reconstruction methods, but took the longest with an average of 13s run time.
The fastest reconstruction method is the Wiener Filter with 67ms and mediocre
quality. The CED with a specifically tailored training set was able to succeed
the most commonly used MURA decoding on average by a factor between 1.37 and
2.60 and a run time of around 300ms.
