Helical acquisition geometry is the most common geometry used in computed tomography (CT) scanners for medical imaging. We adapt the Learned Primal-Dual
(LPD) deep neural network architecture so that it can be applied to helical 3D CT
reconstruction. We achieve this by splitting the geometry and the data in parts that
fit the memory and by splitting images into corresponding sub-volumes. The architecture can be applied to images different in size along the rotation axis. We perform
the experiments on tomographic data simulated from realistic helical geometries.