This project explores a novel approach for reconstructing hyperspectral 4D-µCT (4-dimensional microcomputed tomography) data, with the aim of advancing the analysis of complex volumetric datasets.

Through the integration of innovative algorithms and methodologies, we aim for a shift in processing and interpreting hyperspectral imaging data obtained via hyperspectral technology.
This innovative framework promises enhanced spatial and spectral resolution, enabling insights into the structural and chemical composition of materials. 

By leveraging state-of-the-art techniques such as machine learning and computational modeling, our methodology endeavors to surpass traditional limitations, thereby enabling more accurate and comprehensive analysis of 4D hyperspectral µCT datasets.