  Deep learning based computed tomography (CT) reconstruction has demonstrated
outstanding performance on simulated 2D low-dose CT data. This applies in
particular to domain adapted neural networks, which incorporate a handcrafted
physics model for CT imaging. Empirical evidence shows that employing such
architectures reduces the demand for training data and improves upon
generalisation. However, their training requires large computational resources
that quickly become prohibitive in 3D helical CT, which is the most common
acquisition geometry used for medical imaging. Furthermore, clinical data also
comes with other challenges not accounted for in simulations, like errors in
flux measurement, resolution mismatch and, most importantly, the absence of the
real ground truth. The necessity to have a computationally feasible training
combined with the need to address these issues has made it difficult to
evaluate deep learning based reconstruction on clinical 3D helical CT. This
paper modifies a domain adapted neural network architecture, the Learned
Primal-Dual (LPD), so that it can be trained and applied to reconstruction in
this setting. We achieve this by splitting the helical trajectory into sections
and applying the unrolled LPD iterations to those sections sequentially. To the
best of our knowledge, this work is the first to apply an unrolled deep
learning architecture for reconstruction on full-sized clinical data, like
those in the Low dose CT image and projection data set (LDCT). Moreover,
training and testing is done on a single GPU card with 24GB of memory.
