  Positron emission tomography (PET) scanners continue to increase sensitivity
and axial coverage by adding an ever expanding array of block detectors. As
they age, one or more block detectors may lose sensitivity due to a malfunction
or component failure. The sinogram data missing as a result thereof can lead to
artifacts and other image degradations. We propose to mitigate the effects of
malfunctioning block detectors by carrying out sinogram repair using a deep
convolutional neural network. Experiments using whole-body patient studies with
varying amounts of raw data removed are used to show that the neural network
significantly outperforms previously published methods with respect to
normalized mean squared error for raw sinograms, a multi-scale structural
similarity measure for reconstructed images and with regard to quantitative
accuracy.
