  Current methods for magnetic resonance-based positron emission tomography
attenuation correction (PET-MR AC) are time consuming, and less able than
computed tomography (CT)-based AC methods to capture inter-individual
variability and skull abnormalities. Deep learning methods have been proposed
to produce pseudo-CT from MR images, but these methods have not yet been
evaluated in large clinical cohorts. Methods trained on healthy adult data may
not work in clinical cohorts where skull morphometry may be abnormal, or in
paediatric data where skulls tend to be thinner and smaller. Here, we train a
convolutional neural network based on the U-Net to produce pseudo-CT for PET-MR
AC. We trained our network on a mixed cohort of healthy adults and patients
undergoing clinical PET scans for neurology investigations. We show that our
method was able to produce pseudo-CT with mean absolute errors (MAE) of 100.4
$\pm$ 21.3 HU compared to reference CT, with a Jaccard overlap coefficient of
0.73 $\pm$ 0.07 in the skull masks. Linear attenuation maps based on our
pseudo-CT (relative MAE = 8.4 $\pm$ 2.1\%) were more accurate than those based
on a well-performing multi-atlas-based AC method (relative MAE = 13.1 $\pm$
1.5\%) when compared with CT-based linear attenuation maps. We refined the
trained network in a clinical paediatric cohort. MAE improved from 174.7 $\pm$
33.6 HU when using the existing network to 127.3 $\pm$ 39.9 HU after transfer
learning in the paediatric dataset, thus showing that transfer learning can
improve pseudo-CT accuracy in paediatric data.
