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Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging.

ABSTRACT: Diffraction before destruction using X-ray free-electron lasers (XFELs) has the potential to determine radiation-damage-free structures without the need for crystallization. This article presents the three-dimensional reconstruction of the Melbournevirus from single-particle X-ray diffraction patterns collected at the LINAC Coherent Light Source (LCLS) as well as reconstructions from simulated data exploring the consequences of different kinds of experimental sources of noise. The reconstruction from experimental data suffers from a strong artifact in the center of the particle. This could be reproduced with simulated data by adding experimental background to the diffraction patterns. In those simulations, the relative density of the artifact increases linearly with background strength. This suggests that the artifact originates from the Fourier transform of the relatively flat background, concentrating all power in a central feature of limited extent. We support these findings by significantly reducing the artifact through background removal before the phase-retrieval step. Large amounts of blurring in the diffraction patterns were also found to introduce diffuse artifacts, which could easily be mistaken as biologically relevant features. Other sources of noise such as sample heterogeneity and variation of pulse energy did not significantly degrade the quality of the reconstructions. Larger data volumes, made possible by the recent inauguration of high repetition-rate XFELs, allow for increased signal-to-background ratio and provide a way to minimize these artifacts. The anticipated development of three-dimensional Fourier-volume-assembly algorithms which are background aware is an alternative and complementary solution, which maximizes the use of data.

SUBMITTER: Lundholm IV

PROVIDER: S-EPMC6126651 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging.

Lundholm Ida V IV Sellberg Jonas A JA Ekeberg Tomas T Hantke Max F MF Okamoto Kenta K van der Schot Gijs G Andreasson Jakob J Barty Anton A Bielecki Johan J Bruza Petr P Bucher Max M Carron Sebastian S Daurer Benedikt J BJ Ferguson Ken K Hasse Dirk D Krzywinski Jacek J Larsson Daniel S D DSD Morgan Andrew A Mühlig Kerstin K Müller Maria M Nettelblad Carl C Pietrini Alberto A Reddy Hemanth K N HKN Rupp Daniela D Sauppe Mario M Seibert Marvin M Svenda Martin M Swiggers Michelle M Timneanu Nicusor N Ulmer Anatoli A Westphal Daniel D Williams Garth G Zani Alessandro A Faigel Gyula G Chapman Henry N HN Möller Thomas T Bostedt Christoph C Hajdu Janos J Gorkhover Tais T Maia Filipe R N C FRNC

IUCrJ 20180901 Pt 5

Diffraction before destruction using X-ray free-electron lasers (XFELs) has the potential to determine radiation-damage-free structures without the need for crystallization. This article presents the three-dimensional reconstruction of the Melbournevirus from single-particle X-ray diffraction patterns collected at the LINAC Coherent Light Source (LCLS) as well as reconstructions from simulated data exploring the consequences of different kinds of experimental sources of noise. The reconstruction ...[more]

PMID: 30224956

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Project description:Traditional histology is the gold standard for tissue studies, but it is intrinsically reliant on two-dimensional (2D) images. Study of volumetric tissue samples such as whole hearts produces a stack of misaligned and distorted 2D images that need to be reconstructed to recover a congruent volume with the original sample's shape. In this paper, we develop a mathematical framework called Transformation Diffusion (TD) for stack alignment refinement as a solution to the heat diffusion equation. This general framework does not require contour segmentation, is independent of the registration method used, and is trivially parallelizable. After the first stack sweep, we also replace registration operations by operations in the space of transformations, several orders of magnitude faster and less memory-consuming. Implementing TD with operations in the space of transformations produces our Transformation Diffusion Reconstruction (TDR) algorithm, applicable to general transformations that are closed under inversion and composition. In particular, we provide formulas for translation and affine transformations. We also propose an Approximated TDR (ATDR) algorithm that extends the same principles to tensor-product B-spline transformations. Using TDR and ATDR, we reconstruct a full mouse heart at pixel size 0.92µm×0.92µm, cut 10µm thick, spaced 20µm (84G). Our algorithms employ only local information from transformations between neighboring slices, but the TD framework allows theoretical analysis of the refinement as applying a global Gaussian low-pass filter to the unknown stack misalignments. We also show that reconstruction without an external reference produces large shape artifacts in a cardiac specimen while still optimizing slice-to-slice alignment. To overcome this problem, we use a pre-cutting blockface imaging process previously developed by our group that takes advantage of Brewster's angle and a polarizer to capture the outline of only the topmost layer of wax in the block containing embedded tissue for histological sectioning.

Dataset Information

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging.

Publications

Considerations for three-dimensional image reconstruction from experimental data in coherent diffractive imaging.

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