We developed a self-supervised parallax XRD-CT data reconstruction architecture by integrating a forward operator that can transfer an XRD-CT volume without parallax artefacts to the sinograms with parallax artefacts. A schematic representation of the method is shown in Figure . We use an artificial neural network which acts as an XRD-CT volume generator i.e. it creates a stack of real-space XRD-CT images. The input to the generator is a random non-zero constant. Next, the generated images are converted into sinograms with the addition of parallax artefacts using a differentiable forward operator. The forward operator contains two parts, the first part adds artificial parallax artefacts into the images by taking into account the geometry of the experimental setup, and the second part is the Radon transformation that converts the images to sinograms. This generated XRD-CT sinogram volume is then compared with the experimental sinogram dataset using a designated loss function. Based on this comparison, the weights of the generator network are updated accordingly. Building on the recently developed SD2I architecture , which is a lightweight and scalable CNN architecture that utilizes a single number input for CT image reconstruction and addresses angular undersampling artefacts, we introduce the Single Digit to Volume (SD2Vol) network architecture for enhanced volumetric reconstructions. By transitioning from 2D to 3D convolutional layers and reducing layer parameters, SD2Vol offers both tailored 3D capability and greater efficiency. Figure illustrates the design of the SD2Vol network employed for 3D image reconstruction from a sinogram volume (stack).