Lithium-sulfur batteries are complex systems where multiple parallel electrochemical reactions occur and their electrochemical performance is fundamentally dependent on the microstructure of the carbon/sulfur cathode commonly used. Dissolution of elemental sulfur in the electrolyte and precipitation of solid polysulfides leads to ever-evolving cathode morphology, which is critical to accurately represent with 1D models, and cannot be interrogated with lab-based electrochemical experiments. Hence, to underpin the interplay between sulfur loading, sulfur distribution, CBD morphology, solid precipitation and electrochemical performance, image-based modelling approaches should be used. Here, we have demonstrated the limitations of widely deployed volume-averaged 1D models which do not consider relevant microstructural properties of the electrode, and consequently overpredict the capacity delivered by these electrodes. Even when using accurate parameterisation from X-ray CT, volume-averaging these parameters in 1D models still fails to capture many of the implications of the heterogenous electrode structure. This highlights the importance of building a framework that would effectively bridge the gap between high-fidelity advanced models and volume-averaged 1D models which can be applied with substantially reduced computational expense.