  Image reconstruction in low-count PET is particularly challenging because
gammas from natural radioactivity in Lu-based crystals cause high random
fractions that lower the measurement signal-to-noise-ratio (SNR). In
model-based image reconstruction (MBIR), using more iterations of an
unregularized method may increase the noise, so incorporating regularization
into the image reconstruction is desirable to control the noise. New
regularization methods based on learned convolutional operators are emerging in
MBIR. We modify the architecture of an iterative neural network, BCD-Net, for
PET MBIR, and demonstrate the efficacy of the trained BCD-Net using XCAT
phantom data that simulates the low true coincidence count-rates with high
random fractions typical for Y-90 PET patient imaging after Y-90 microsphere
radioembolization. Numerical results show that the proposed BCD-Net
significantly improves CNR and RMSE of the reconstructed images compared to
MBIR methods using non-trained regularizers, total variation (TV) and non-local
means (NLM). Moreover, BCD-Net successfully generalizes to test data that
differs from the training data. Improvements were also demonstrated for the
clinically relevant phantom measurement data where we used training and testing
datasets having very different activity distributions and count-levels.
