Mitigating the Resolution-Field of View Trade-Off for Comprehensive Microstructural Characterization with Advanced AI Methods.

Microstructural characterization of pharmaceutical drug products is essential for understanding process-microstructure-performance relationships and ensuring consistent product quality. However, quantitative characterization is limited by a trade-off between imaging resolution and field of view: high-resolution imaging captures fine structural detail but over small sample volumes, whereas lower-resolution imaging provides broader coverage while missing critical morphological features. To address this limitation, we developed and validated an integrated framework combining convolutional neural network (CNN)-based super-resolution with Generative Adversarial Network (GAN)-based microstructure synthesis. Lyophilized drug products imaged by X-ray microscopy at multiple resolutions served as the model system. We first demonstrated that imaging resolution is a governing factor in quantitative microstructural analysis: mean pore size showed a coefficient of variation of 40.2% across resolution levels, compared with only 0.47% attributable to spatial heterogeneity within the same sample. CNN-based upscaling using ESRGAN/BSRGAN recovered solid-wall structures and pore-size distributions lost after downsampling and restored effective diffusivity toward values measured in the original high-resolution data. In an independent validation using a separate formulation imaged at 5 and 10 µm per voxel, the upscaled images reproduced pore-size distributions and diffusivity profiles closer to the 5 µm reference and showed better recovery of CQA-relevant structural features than conventional bicubic interpolation, despite lower pixel-level image fidelity scores. GAN-based synthesis expanded the field of view four-fold from a small high-resolution seed region while preserving pore-size distributions and transport properties. Together, these findings demonstrate a scalable approach for improving microstructural characterization under practical imaging constraints.