3D high resolution imaging of biological materials enhanced by machine learning

Nature always impresses us with some functional marvels which result from a long evolutionary process of billion years. The existence of biological materials like insects serves as inspiration for research into novel materials with remarkable properties. For example, outstanding mechanical properties of certain insect parts are often achieved through hierarchically organized structures which can only be studied by means of 3D microscopic characterizations in multiple length scales.

Employing X-ray microscopy (XRM) in a computed tomography (CT) approach we performed 3D imaging of biological materials like insects covering multiple length scales. To improve the visualization of details of microstructure or low-contrast features, DeepRecon Pro is applied for high image contrast, high resolution, and high signal-to-noise ratios (SNR). DeepRecon Pro is the latest ZEISS reconstruction software based on the deep learning which uniquely opens opportunities in big data generated by 3D X-ray imaging and provides significant AI-driven speed or image quality improvement. By means of DeepRecon Pro, image artifacts are reduced which are typically inherent in traditional faster imaging processes.

Various biomaterials are successfully characterized using both ZEISS FESEM and XRM to visualize the 3D structures and morphology. The understanding of the hierarchical assembly of architectures at multiple length scales is of great importance for future design and improvement of high-performance materials with extremely high specific strength to weight ratio. Considerable efforts can be made to mimic the structures to have the similar incredible toughness or damage resistance of synthetic composites by means of 3D printing.