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Abstract

X-ray single-particle imaging involves the measurement of a large number of noisy diffraction patterns of isolated objects in random orientations. The missing information about these patterns is then computationally recovered in order to obtain the 3D structure of the particle. While the method has promised to deliver room-temperature structures at near-atomic resolution, there have been significant experimental hurdles in collecting data of sufficient quality and quantity to achieve this goal. This paper describes two ways to modify the conventional methodology that significantly ease the experimental challenges, at the cost of additional computational complexity in the reconstruction procedure. Both these methods involve the use of holographic reference objects in close proximity to the sample of interest, whose structure can be described with only a few parameters. A reconstruction algorithm for recovering the unknown degrees of freedom is also proposed and tested with toy model simulations. The techniques proposed here enable 3D imaging of biomolecules that is not possible with conventional methods and open up a new family of methods for recovering structures from datasets with a variety of hidden parameters.