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BEGIN:VEVENT
SUMMARY:Doga Gursoy\, Northwestern University & Argonne National Lab.
DTSTART;VALUE=DATE-TIME:20190404T071000Z
DTEND;VALUE=DATE-TIME:20190404T074000Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-694@lindico453.srv.lu.se
DESCRIPTION:Ptycho-tomography: An emerging lensless microscopy technique f
 or imaging materials at the nanoscale\n\nThe increasing availability of co
 herent light sources at short wavelengths from the extreme ultraviolet to 
 the hard x-ray regime has paved the way for widespread use of coherent dif
 fraction imaging (CDI) techniques in the past decade. In contrast to class
 ical microscopy\, CDI does not require an objective lens between the sampl
 e and the detector\, therefore it can provide spatial resolution with no l
 ens-imposed limitations. In contrast to classical CDI\, imaging of wide fi
 eld-of-views can be achieved by performing CDI in scanning mode. This appr
 oach 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 diminishe
 d 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 yiel
 d superior reconstructions for high-speed or photon-limited imaging condit
 ions when implemented as an integral part of the imaging setup.\n\nhttps:/
 /lindico453.srv.lu.se/event/120/contributions/694/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/694/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Max Langer\, Creatis\, INSA Lyon\, France
DTSTART;VALUE=DATE-TIME:20190404T074000Z
DTEND;VALUE=DATE-TIME:20190404T081000Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-692@lindico453.srv.lu.se
DESCRIPTION:Some methodological contributions to X-ray phase contrast imag
 ing\n\nI present some of our methodological contributions to X-ray phase c
 ontrast imaging. Phase retrieval often suffers from noise in the low spati
 al frequencies. We proposed a prior on the imaged object based on an atten
 uation image to regularise the low spatial frequencies in the retrieved ph
 ase. For the experimental setup\, we showed that spreading the imaging dos
 e over several propagation distances can improve reconstructed image quali
 ty for a given imaging dose. When using multiple propagation distances\, w
 e showed that the choice of registration algorithm can have an effect on t
 he reconstructed image quality. Finally\, I will give a first preview of a
  phase retrieval code under development\, which aim is to facilitate devel
 opment and reimplementation of phase retrieval algorithms and the handling
  of data from different sources.\n\nhttps://lindico453.srv.lu.se/event/120
 /contributions/692/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/692/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Marcus Carlsson\, Lund University
DTSTART;VALUE=DATE-TIME:20190404T081000Z
DTEND;VALUE=DATE-TIME:20190404T084000Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-693@lindico453.srv.lu.se
DESCRIPTION:PhaseLift\, possibilities and challenges.\n\nThe phase retriev
 al problem is ill posed\, and most phase retrieval algorithms stabilize in
 version via additional assumptions on the support of the object. These alg
 orithms come without guarantee of convergence\, and it is a problem in pra
 ctice. 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. ma
 sks. I will describe this algorithm\, describe drawbacks (slow) possible i
 mprovements (work in progress with D. Gerosa) and ultimately ask the audie
 nce\; is this a potential candidate for processing of synchrotron data?\n\
 nhttps://lindico453.srv.lu.se/event/120/contributions/693/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/693/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Doga Gursoy
DTSTART;VALUE=DATE-TIME:20190404T124000Z
DTEND;VALUE=DATE-TIME:20190404T131500Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-716@lindico453.srv.lu.se
DESCRIPTION:TomoPy and related tools\n\nhttps://lindico453.srv.lu.se/event
 /120/contributions/716/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/716/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Brainstorming on themes to discuss in groups in the afternoon
DTSTART;VALUE=DATE-TIME:20190404T101000Z
DTEND;VALUE=DATE-TIME:20190404T103000Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-715@lindico453.srv.lu.se
DESCRIPTION:https://lindico453.srv.lu.se/event/120/contributions/715/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/715/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daniel Pelt\, CWI Amsterdam
DTSTART;VALUE=DATE-TIME:20190404T113000Z
DTEND;VALUE=DATE-TIME:20190404T120500Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-713@lindico453.srv.lu.se
DESCRIPTION:Improving Tomographic Reconstruction and Analysis Using Mixed-
 Scale Dense Convolutional Neural Networks\n\nIn tomography\, acquired proj
 ection data are often limited in one or more ways due to unavoidable exper
 imental 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 tom
 ographic reconstruction from (severely) limited data\, and present a new s
 oftware package for training and applying such networks in practice.\n\nht
 tps://lindico453.srv.lu.se/event/120/contributions/713/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/713/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jonas Adler\, KTH Stockholm
DTSTART;VALUE=DATE-TIME:20190404T120500Z
DTEND;VALUE=DATE-TIME:20190404T124000Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-714@lindico453.srv.lu.se
DESCRIPTION:ODL for Machine learning based tomographic image reconstructio
 n\n\nODL is a python library for inverse problems developed jointly by sev
 eral research groups and companies. We introduce ODL and how it can be use
 d for variational reconstruction in inverse problems and further demonstra
 te how ODL can be used as a building block in learned iterative reconstruc
 tion methods and for learned optimization.\n\nhttps://lindico453.srv.lu.se
 /event/120/contributions/714/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/714/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Per Christian Hansen\, DTU Compute
DTSTART;VALUE=DATE-TIME:20190404T090000Z
DTEND;VALUE=DATE-TIME:20190404T093500Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-699@lindico453.srv.lu.se
DESCRIPTION:Convergence and Non-Convergence of Algebraic Iterative Reconst
 ruction Methods\n\nAlgebraic 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 constrai
 nts.\nWhen AIR methods are implemented on GPU-accelerated systems with a f
 ocus on computational efficiency\, different computational schemes are use
 d for the forward projection and the backprojection. In the algebraic “l
 anguage” 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) de
 teriorates\, and the iteration may fail to converge.\nIn this talk we illu
 strate these issues with recent theoretical and computational results\, an
 d we present a novel approach to “fixing” the non-convergence with onl
 y a small computational overhead.\n\nThis 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.\n\n•	T. Elfving an
 d P. C. Hansen\, Unmatched projector/backprojector pairs: perturbation and
  convergence analysis\, SIAM J. Sci. Comput.\, 40 (2018)\, pp. A573–A591
 \, doi: 10.1137/17M1133828.\n•	Y. Dong\, P. C. Hansen\, M. E. Hochstenba
 ch\, and N. A. B. Riis\, Fixing Nonconvergence of algebraic iterative reco
 nstruction methods with an unmatched backprojector\; submitted to SIAM J. 
 Sci. Comput.\n\nhttps://lindico453.srv.lu.se/event/120/contributions/699/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/699/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Viktor Nikitin\, Lund University
DTSTART;VALUE=DATE-TIME:20190404T093500Z
DTEND;VALUE=DATE-TIME:20190404T101000Z
DTSTAMP;VALUE=DATE-TIME:20260524T221812Z
UID:indico-contribution-120-696@lindico453.srv.lu.se
DESCRIPTION:Fast reconstruction in dynamic tomography\n\nhttps://lindico45
 3.srv.lu.se/event/120/contributions/696/
LOCATION:LINXS Workshop room\, 5th floor
URL:https://lindico453.srv.lu.se/event/120/contributions/696/
END:VEVENT
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