Deep Learning for High‐Resolution Synchrotron X‐Ray Microtomography of Biochar and Ash Materials: A Review

Image reconstruction is fundamental to advanced imaging systems, transforming large datasets into interpretable representations that facilitate a deeper understanding of material structures. High‐resolution imaging techniques, such as synchrotron X‐ray microtomography, operate over a wide dynamic range and are susceptible to noise, thereby requiring advanced reconstruction strategies beyond conventional approaches. Recent integration of iterative optimization with deep‐learning architectures, including convolutional neural networks and U‐Nets, has advanced this field by enabling accelerated image enhancement, artifact mitigation, and segmentation. Advanced neural‐network architectures can improve structural detail recovery and segmentation accuracy while supporting efficient analysis of large volumetric datasets. The application of AI‐enhanced deep learning for reconstruction, denoising, segmentation, and quantitative analysis of synchrotron microtomography datasets from biomass‐derived materials such as biochar and ash remains relatively unexplored. Deep learning‐guided segmentation enables quantitative characterization of pore networks, interfacial features, phase distributions, porosity, and other microstructural descriptors. These developments support applications in materials characterization, energy‐storage systems, environmental engineering, and biomass‐derived materials research. Future developments may enable the resolution of previously inaccessible structural features, integrate multimodal datasets, and develop adaptive experimental processes. Together, advances in imaging physics, deep learning, and data‐driven analysis are expected to enhance reconstruction quality, quantitative interpretation, and the versatility of synchrotron tomography.