Accelerated Convergent Motion Compensated Image Reconstruction

Motion correction aims to prevent motion artefacts which may be caused by respiration, heartbeat, or head movements for example. In a preliminary step, the measured data is divided in gates corresponding to motion states, and displacement maps from a reference state to each motion state are estimated. One common technique to perform motion correction is the motion compensated image reconstruction framework, where the displacement maps are integrated into the forward model corresponding to gated data. For standard algorithms, the computational cost per iteration increases linearly with the number of gates. In order to accelerate the reconstruction, we propose the use of a randomized and convergent algorithm whose per iteration computational cost scales constantly with the number of gates. We show improvement on theoretical rates of convergence and observe the predicted speed-up on two synthetic datasets corresponding to rigid and non-rigid motion

Resolving gravitational microlensing events with long-baseline optical interferometry. Prospects for the ESO Very Large Telescope Interferometer

Until now, the detailed interpretation of the observed microlensing events has suffered from the fact that the physical parameters of the phenomenon cannot be uniquely determined from the available astronomical measurements, i.e. the photometric lightcurves. The situation will change in the near-future with the availability of long-baseline, sensitive optical interferometers, which should be able to resolve the images of the lensed objects into their components. For this, it will be necessary to achieve a milliarcsecond resolution on sources with typical magnitudes K \ga 12. Indeed, brighter events have never been observed up to now by micro-lensing surveys. We discuss the possibilities opened by the use of long baseline interferometry in general, and in particular for one such facility, the ESO VLT Interferometer, which will attain the required performance. We discuss the expected accuracy and limiting magnitude of such measurements. On the basis of the database of the events detected by the OGLE experiment, we estimate the number of microlenses that could be available for measurements by the VLTI. We find that at least several tens of events could be observed each year. In conjunction with the photometric data, our ability to measure the angular separation between the microlensed images will enable a direct and unambiguous determination of both their masses and locations.Comment: Accepted for publication in Astronomy & Astrophysic

Accelerated Convergent Motion Compensated Image Reconstruction

Motion correction aims to prevent motion artefacts which may be caused by respiration, heartbeat, or head movements for example. In a preliminary step, the measured data is divided in gates corresponding to motion states, and displacement maps from a reference state to each motion state are estimated. One common technique to perform motion correction is the motion compensated image reconstruction framework, where the displacement maps are integrated into the forward model corresponding to gated data. For standard algorithms, the computational cost per iteration increases linearly with the number of gates. In order to accelerate the reconstruction, we propose the use of a randomized and convergent algorithm whose per iteration computational cost scales constantly with the number of gates. We show improvement on theoretical rates of convergence and observe the predicted speed-up on two synthetic datasets corresponding to rigid and non-rigid motion

Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI

The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems. In this paper we prove its almost sure convergence for convex but not necessarily strongly convex functionals. We also look into its application to parallel Magnetic Resonance Imaging reconstruction in order to test performance of SPDHG. Our numerical results show that for a range of settings SPDHG converges significantly faster than its deterministic counterpart.</p

Un cas d’instabilité disciplinaire : des Sciences Naturelles aux Sciences de la Vie et de la Terre

L’enseignement de la biologie géologie a connu en France d’importants changements dans les années 80-90. Entre 1981 à 1992, la discipline scolaire des sciences naturelles change plusieurs fois d’appellation avant d’adopter sa dénomination contemporaine de Sciences de la Vie et de la Terre (SVT). Cet article s’intéresse à ce cas d’instabilité disciplinaire en interrogeant les transformations diachroniques des contenus d’enseignement prescrits. La méthodologie articule un regard didactique sur l’évolution des programmes (analyses quantitative et qualitative des transformations curriculaire) à un regard socio-historique sur leurs coulisses d’écriture (travail d’archives). 

Sonochemistry: Scope, Limitations… and Artifacts

Heterogeneous sonochemistry, generally described as the most useful aspect of sonochemistry, suffers from the difficulty of defining the experimental conditions for the reference (silent) reaction. Clearly the use of an efficient agitation system for the silent reaction strongly reduces the value of the so-called sonochemical effect, which becomes in some cases, less than 1 (anti-sonochemical effect!).The 'cleaning' effect of ultrasound is extremely efficient to expel micro-crystals from the surface of an electrode which is simultaneously an immersion ultrasonic horn. By sending out-of-phase electric pulses and acoustic pulses, new nano materials (metals, alloys, semiconductors, oxides) are easily prepared in high purity

Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI

The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems. In this paper we prove its almost sure convergence for convex but not necessarily strongly convex functionals. We also look into its application to parallel Magnetic Resonance Imaging reconstruction in order to test performance of SPDHG. Our numerical results show that for a range of settings SPDHG converges significantly faster than its deterministic counterpart