Self-supervised image restoration in coherent X-ray neuronal microscopy

Coherent X-ray microscopy is emerging as a transformative technology for neuronal imaging, with the potential to offer a scalable solution for reconstruction of neural circuits in millimeter sized tissue volumes. Specifically, X-ray holographic nanoto-mography (XNH) brings together outstanding capabilities in terms of contrast, spatial resolution and data acquisition speed. While recent XNH developments already enabled generating valuable datasets for neuro-sciences, a major challenge for reconstruction of neural circuits remained overcoming resolving power limits to distinguish smaller neurites and synapses in the reconstructed volumes. Here we present a self-supervised image restoration approach that simultaneously improves spatial resolution, contrast, and data acquisition speed. This enables revealing synapses with XNH, marking a major milestone in the quest for generating connectomes of full mammalian brains. We demonstrate that this method is effective for various types of neuronal tissues and acquisition schemes. We propose a scalable implementation compatible with multi-terabyte image volumes. Altogether, this work brings large-scale X-ray nanotomography to a new precision level.