Total generalized variation regularization formulti-modal electron tomography

Richard Huber, Georg Haberfehlner, Martin Holler, Gerald Kothleitner, Kristian Bredies

Research output: Contribution to journalArticlepeer-review


In multi-modal electron tomography, tilt series of several signals such as X-ray spectra, electron energylossspectra, annular dark-field, or bright-field data are acquired at the same time in a transmission electronmicroscope and subsequently reconstructed in three dimensions. However, the acquired data areoften incomplete and suffer from noise, and generally each signal is reconstructed independently of allother signals, not taking advantage of correlation between different datasets. This severely limits both theresolution and validity of the reconstructed images. In this paper, we show how image quality in multimodalelectron tomography can be greatly improved by employing variational modeling and multichannelregularization techniques. To achieve this aim, we employ a coupled Total Generalized Variation(TGV) regularization that exploits correlation between different channels. In contrast to other regularizationmethods, coupled TGV regularization allows to reconstruct both hard transitions and gradualchanges inside each sample, and links different channels at the level of first and higher order derivatives.This favors similar interface positions for all reconstructions, thereby improving the image quality for alldata, in particular, for 3D elemental maps. We demonstrate the joint multi-channel TGV reconstructionon tomographic energy-dispersive X-ray spectroscopy (EDXS) and high-angle annular dark field (HAADF)data, but the reconstruction method is generally applicable to all types of signals used in electron tomography,as well as all other types of projection-based tomographies.
Original languageEnglish
Pages (from-to)5617-5632
Number of pages16
Issue number12
Publication statusPublished - 28 Feb 2019

ASJC Scopus subject areas

  • Materials Science(all)

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