Inverse problems in X-ray phase retrieval and tomography (4 April 2019)

- 08:30 → 09:00
  
  registration with breakfast 30m
- 09:00 → 10:40
  invited talks: Phase retrieval
  - 09:10
    
    Doga Gursoy, Northwestern University & Argonne National Lab. 30m
    
    Ptycho-tomography: An emerging lensless microscopy technique for imaging materials at the nanoscale
    
    The increasing availability of coherent light sources at short wavelengths from the extreme ultraviolet to the hard x-ray regime has paved the way for widespread use of coherent diffraction imaging (CDI) techniques in the past decade. In contrast to classical microscopy, CDI does not require an objective lens between the sample and the detector, therefore it can provide spatial resolution with no lens-imposed limitations. In contrast to classical CDI, imaging of wide field-of-views can be achieved by performing CDI in scanning mode. This approach is commonly known as “ptychography” following the work of Hegerl and Hoppe on electron microscopy in 1969. However while ptychography with current and future synchrotron-based X-ray sources is very versatile, the main advantage of its diffraction-limited spatial resolution is diminished by the inherently limited temporal resolution in obtaining 3D images. In this talk I will talk on a set of new computational methods that can yield superior reconstructions for high-speed or photon-limited imaging conditions when implemented as an integral part of the imaging setup.
  - 09:40
    
    Max Langer, Creatis, INSA Lyon, France 30m
    
    Some methodological contributions to X-ray phase contrast imaging
    
    I present some of our methodological contributions to X-ray phase contrast imaging. Phase retrieval often suffers from noise in the low spatial frequencies. We proposed a prior on the imaged object based on an attenuation image to regularise the low spatial frequencies in the retrieved phase. For the experimental setup, we showed that spreading the imaging dose over several propagation distances can improve reconstructed image quality for a given imaging dose. When using multiple propagation distances, we showed that the choice of registration algorithm can have an effect on the reconstructed image quality. Finally, I will give a first preview of a phase retrieval code under development, which aim is to facilitate development and reimplementation of phase retrieval algorithms and the handling of data from different sources.
  - 10:10
    
    Marcus Carlsson, Lund University 30m
    
    PhaseLift, possibilities and challenges.
    
    The phase retrieval problem is ill posed, and most phase retrieval algorithms stabilize inversion via additional assumptions on the support of the object. These algorithms come without guarantee of convergence, and it is a problem in practice. PhaseLift is a new (2013) algorithm which is based on a completely different approach, and it stabilizes the inversion by the use of e.g. masks. I will describe this algorithm, describe drawbacks (slow) possible improvements (work in progress with D. Gerosa) and ultimately ask the audience; is this a potential candidate for processing of synchrotron data?
- 10:40 → 11:00
  
  coffee 20m
- 11:00 → 12:10
  invited talks: tomographic reconstruction
  - 11:00
    
    Per Christian Hansen, DTU Compute 35m
    
    Convergence and Non-Convergence of Algebraic Iterative Reconstruction Methods
    
    Algebraic Iterative Reconstruction methods – such as ART (Kaczmarz), SART, and SIRT produce good results for underdetermined problems, and they can easily incorporate non-negativity and box constraints.
    When AIR methods are implemented on GPU-accelerated systems with a focus on computational efficiency, different computational schemes are used for the forward projection and the backprojection. In the algebraic “language” of the AIR methods, this means that the backprojection matrix B is not the transpose AT of the forward projection matrix A. The use of B  AT has two consequences: the accuracy (compared to when using AT) deteriorates, and the iteration may fail to converge.
    In this talk we illustrate these issues with recent theoretical and computational results, and we present a novel approach to “fixing” the non-convergence with only a small computational overhead.
    
    This is joint work with Tommy Elfving from Linköping University, Michiel Hochstenbach from TU Eindhoven, as well as Yiqiu Dong and Nicolai Riis from DTU Compute.
    
    • T. Elfving and P. C. Hansen, Unmatched projector/backprojector pairs: perturbation and convergence analysis, SIAM J. Sci. Comput., 40 (2018), pp. A573–A591, doi: 10.1137/17M1133828.
    • Y. Dong, P. C. Hansen, M. E. Hochstenbach, and N. A. B. Riis, Fixing Nonconvergence of algebraic iterative reconstruction methods with an unmatched backprojector; submitted to SIAM J. Sci. Comput.
  - 11:35
    
    Viktor Nikitin, Lund University 35m
    
    Fast reconstruction in dynamic tomography
- 12:10 → 12:30
  plenary discsussion: Initiation to discussions
  - 12:10
    
    Brainstorming on themes to discuss in groups in the afternoon 20m
- 12:30 → 13:30
  
  lunch at LINXS 1h
- 13:30 → 15:15
  talks with tutorials / hands-on: tools in phase retrieval and tomography
  - 13:30
    
    Daniel Pelt, CWI Amsterdam 35m
    
    Improving Tomographic Reconstruction and Analysis Using Mixed-Scale Dense Convolutional Neural Networks
    
    In tomography, acquired projection data are often limited in one or more ways due to unavoidable experimental constraints, leading to inaccurate images. Using machine learning to improve image quality in tomography is a recently proposed solution, for which promising results have been shown. In this talk, I will present the use of Mixed-Scale Dense convolutional neural networks to improve tomographic reconstruction from (severely) limited data, and present a new software package for training and applying such networks in practice.
  - 14:05
    
    Jonas Adler, KTH Stockholm 35m
    
    ODL for Machine learning based tomographic image reconstruction
    
    ODL is a python library for inverse problems developed jointly by several research groups and companies. We introduce ODL and how it can be used for variational reconstruction in inverse problems and further demonstrate how ODL can be used as a building block in learned iterative reconstruction methods and for learned optimization.
  - 14:40
    
    Doga Gursoy 35m
    
    TomoPy and related tools
- 15:15 → 15:30
  
  fika 15m
- 15:30 → 17:00
  
  discussion corners: (A) phasing - (B) tomography - (C) deep learning
- 17:00 → 18:00
  
  plenary discsussion: report from groups and conclusions
- 18:00 → 19:30
  
  sandwich dinner and refreshment at LINXS 1h 30m
- 19:30 → 19:35
  
  The End 5m

Inverse problems in X-ray phase retrieval and tomography

Workshop room, 5th floor

LINXS