Abstract
The high resolution of synchrotron cryo-nano tomography can be easily undermined by setup instabilities and sample stage deficiencies such as runout or backlash. At the cost of limiting the sample visibility, especially in the case of bio-specimens, high contrast nano-beads are often added to the solution to provide a set of landmarks for a manual alignment. However, the spatial distribution of these reference points within the sample is difficult to control, resulting in many datasets without a sufficient amount of such critical features for tracking. Fast automatic methods based on tomography consistency are thus desirable, especially for biological samples, where regular, high contrast features can be scarce. Current off-the-shelf implementations of such classes of algorithms are slow if used on a real-world high-resolution dataset. In this paper, we present a fast implementation of a consistency-based alignment algorithm especially tailored to a multi-GPU system. Our implementation is released as open-source.
Highlights
IntroductionSoft X-ray cryo-nano tomography is an effective imaging tool that allows one to analyse biological samples in their native environment, providing ultrastructural three-dimensional information of cells [1]; the high resolution of this technique, typically in the tens of nanometres [2], is paired to a profitable contrast behaviour in a specific range of energy, the water window, and permits imaging in the hydrated state without staining, as the sample preparation requires only vitrification of water via a cryo-fixation process [3]
The main idea of reconstruction “self-consistency” [33] is that at the convergence, from a reconstructed volume, one can simulate a set of projections which are virtually indistinguishable from the original dataset
To measure the algorithm acceleration, we benchmarked each module against the corresponding block in the algorithm implementation [12] in Tomopy [7]
Summary
Soft X-ray cryo-nano tomography is an effective imaging tool that allows one to analyse biological samples in their native environment, providing ultrastructural three-dimensional information of cells [1]; the high resolution of this technique, typically in the tens of nanometres [2], is paired to a profitable contrast behaviour in a specific range of energy, the water window, and permits imaging in the hydrated state without staining, as the sample preparation requires only vitrification of water via a cryo-fixation process [3]. To obtain a reconstruction from a series of 2D projections, tomography relies on a corpus of a priori information which constitutes an image formation model [5]. If any of the previous assumptions is not met, a dataset acquired under these conditions is defined as misaligned, and exploiting the ideal model introduced earlier produces several artefacts in the reconstruction. While for a constant systematic error [6] the misalignment can be corrected by a relatively easy determination of the rotation centre [7,8]
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