In this paper, we introduced an XRD-CT reconstruction approach, ParallaxNet, designed to reconstruct images from XRD-CT data containing parallax artefacts. The ParallaxNet strategy employs a 3D self-supervised neural network generator framework, SD2Vol, together with a customized forward projector to produce parallax artefact-free images/diffraction patterns. This is achieved through an iterative approach by comparing the difference between the generated sinogram volume and the input reference sinogram volume. We evaluated ParallaxNet's performance using three datasets: a simulated XRD-CT dataset, an experimental XRD-CT dataset acquired using a phantom object and an experimental XRD-CT dataset recorded on an NMC532 cylindrical Li-ion battery.