Phase-contrast THz-CT for non-destructive testing
Simon Hubmer1, Ronny Ramlau1,2
1Johann Radon Institue Linz, Austria; 2Johannes Kepler University Linz, Austria
In this talk, we consider the imaging problem of THz computed tomography (THz-CT), in particular for the non-destructive testing of extruded plastic profiles. We derive a general nonlinear mathematical model describing a full THz tomography experiment, and consider several approximations connecting THz tomography with standard computerized tomography and the Radon transform. The employed models are based on geometrical optics, and contain both the THz signal amplitude and the phase. We consider several reconstruction approaches using the corresponding phase-contrast sinograms, and compare them both qualitiatively and quantitatively on experimental data obtained from 3D printed plastic profiles which were scanned with a THz time-domain spectrometer in transmission geometry.
Diffraction tomography for a generalized incident beam wave
Noemi Naujoks
University of Vienna, Austria
The mathematical imaging problem of diffraction tomography is an
inverse scattering technique used to find the material properties of an object. Here,
the object is exposed to a certain form of radiation and the scattered wave is
recorded. In conventional diffraction tomography, the incident wave is assumed
to be a monochromatic plane wave arriving from a fixed direction of propagation.
However, this plane wave excitation does not necessarily correspond to measurement
setups used in practice: There, the size of the emitting device is limited and therefore
cannot produce plane waves. Besides, it is common to emit focused beams to achieve
a better resolution in the far field. In this talk, I will present our recent results that
allow diffraction tomography to be applied to these realistic illumination scenarios.
We use a new forward model, that incorporates individually generated incident
fields. Based on this, a new reconstruction algorithm is developed.
Bias-free localizations in cryo-single molecule localization microscopy
Fabian Hinterer
Johannes Kepler University Linz, Austria
Single molecule localization microscopy (SMLM) has the potential to resolve structural details of biological samples at the nanometer length scale. Compared to room temperature experiments, SMLM performed under cryogenic temperature achieves higher photon yields and, hence, higher localization precision. However, to fully exploit the resolution it is crucial to account for the anisotropic emission characteristics of fluorescence dipole emitters with fixed orientation. In this talk, I will present recent advances along this avenue.
Uncertainty-aware blob detection in astronomical imaging
Fabian Parzer1, Prashin Jethwa1, Alina Boecker2,3, Mayte Alfaro-Cuello4,5, Otmar Scherzer1,6,7, Glenn van de Ven1
1University of Vienna, Austria; 2Max-Planck Institut für Astronomie, Germany; 3Instituto de Astrofisica de Canarias, Spain; 4Universidad Central de Chile, Chile; 5Space Telescope Science Institute, USA; 6Johann Radon Institute for Computational and Applied Mathematics, Linz, Austria; 7Christian Doppler Laboratory for Mathematical Modeling and Simulation of Next Generations of Ultrasound Devices, Vienna, Austria
Blob detection, i. e. detection of blob-like shapes in an image, is a common problem in astronomy. A difficulty arises when the image of interest has to be recovered from noisy measurements, and thus comes with uncertainties. Formulating the reconstruction of the image as a Bayesian inverse problem, we propose an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection. It combines ideas from scale-space theory, statistics and variational regularization to identify salient blobs in uncertain images. The proposed method is illustrated on a problem from stellar dynamics: the identification of components in a stellar distribution recovered from integrated-light spectra. This talk is based on our recent preprint [1].
[1] F. Parzer, P. Jethwa, A. Boecker, M. Alfaro-Cuello, O. Scherzer, G. van de Ven. Uncertainty-Aware Blob Detection with an Application to Integrated-Light Stellar Population Recoveries, arXiv:2208.05881, 2022.
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