Quantitative analysis of the effect of radiation on mitochondria structure using coherent diffraction imaging with a clustering algorithm

Radiation damage and a low signal-to-noise ratio are the primary factors that limit spatial resolution in coherent diffraction imaging (CDI) of biomaterials using X-ray sources. Introduced here is a clustering algorithm named ConvRe based on deep learning, and it is applied to obtain accurate and consistent image reconstruction from noisy diffraction patterns of weakly scattering biomaterials. To investigate the impact of X-ray radiation on soft biomaterials, CDI experiments were performed on mitochondria from human embryonic kidney cells using synchrotron radiation. Benefiting from the new algorithm, structural changes in the mitochondria induced by X-ray radiation damage were quantitatively characterized and analysed at the nanoscale with different radiation doses. This work also provides a promising approach for improving the imaging quality of biomaterials with XFEL-based plane-wave CDI.

Automated summary

This article introduces a clustering algorithm named ConvRe based on deep learning, which is used to accurately reconstruct images of biomaterials from noisy diffraction patterns. Through CDI experiments and application of different radiation doses, the impact of X-ray radiation on mitochondrial structure was investigated.