In this work, we present results from an operando X-ray diffraction computed tomography (XRD-CT) experiment with a commercial 18650 NCA battery. Diffraction point measurements were collected during early cycling stages to investigate the evolving solid-state chemistry within the battery cell. In addition, multiple XRD-CT datasets were acquired at different potentials while charging the cell in order to visualise the state-of-charge heterogeneities. These are observed through the distribution of lithiated graphitic phases as well as the average lattice parameters of cathode layered oxide. Data analysis was performed with a deep learning approach using the state-of-the-art Parameter Quantification Network (PQ-Net) which allowed us to extract crystallographic information, from both point measurements and XRD-CT data, orders of magnitude faster compared to conventional approaches (ca. 3 min vs 10 hr for analysis of one XRD-CT dataset with the PQ-Net and conventional full profile analysis using the Rietveld method with the TOPAS software respectively) .