PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities

We present the PyHST2 code which is in service at ESRF for phase-contrast and absorption tomography. This code has been engineered to sustain the high data flow typical of the third generation synchrotron facilities (10 terabytes per experiment) by adopting a distributed and pipelined architecture. The code implements, beside a default filtered backprojection reconstruction, iterative reconstruction techniques with a-priori knowledge. These latter are used to improve the reconstruction quality or in order to reduce the required data volume and reach a given quality goal. The implemented a-priori knowledge techniques are based on the total variation penalisation and a new recently found convex functional which is based on overlapping patches. We give details of the different methods and their implementations while the code is distributed under free license. We provide methods for estimating, in the absence of ground-truth data, the optimal parameters values for a-priori techniques

A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography

X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing L-1 norm of the patch basis functions coefficients, and a coefficient multiplying the L-2 norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography

A Dictionary Learning Approach with Overlap for the Low Dose Computed Tomography Reconstruction and Its Vectorial Application to Differential Phase Tomography

X-ray based Phase-Contrast Imaging (PCI) techniques have been demonstrated to enhance the visualization of soft tissues in comparison to conventional imaging methods. Nevertheless the delivered dose as reported in the literature of biomedical PCI applications often equals or exceeds the limits prescribed in clinical diagnostics. The optimization of new computed tomography strategies which include the development and implementation of advanced image reconstruction procedures is thus a key aspect. In this scenario, we implemented a dictionary learning method with a new form of convex functional. This functional contains in addition to the usual sparsity inducing and fidelity terms, a new term which forces similarity between overlapping patches in the superimposed regions. The functional depends on two free regularization parameters: a coefficient multiplying the sparsity-inducing L-1 norm of the patch basis functions coefficients, and a coefficient multiplying the L-2 norm of the differences between patches in the overlapping regions. The solution is found by applying the iterative proximal gradient descent method with FISTA acceleration. The gradient is computed by calculating projection of the solution and its error backprojection at each iterative step. We study the quality of the solution, as a function of the regularization parameters and noise, on synthetic data for which the solution is a-priori known. We apply the method on experimental data in the case of Differential Phase Tomography. For this case we use an original approach which consists in using vectorial patches, each patch having two components: one per each gradient component. The resulting algorithm, implemented in the European Synchrotron Radiation Facility tomography reconstruction code PyHST, has proven to be efficient and well-adapted to strongly reduce the required dose and the number of projections in medical tomography

The Cosmic Ray Lepton Puzzle

Recent measurements of cosmic ray electrons and positrons by PAMELA, ATIC, Fermi and HESS have revealed interesting excesses and features in the GeV-TeV range. Many possible explanations have been suggested, invoking one or more nearby primary sources such as pulsars and supernova remnants, or dark matter. Based on the output of the TANGO in PARIS --Testing Astroparticle with the New GeV/TeV Observations in Positrons And electRons : Identifying the Sources-- workshop held in Paris in May 2009, we review here the latest experimental results and we discuss some virtues and drawbacks of the many theoretical interpretations proposed so far.Comment: 6 pages, 3 figures, Extended version of the proceeding of the annual meeting of the French Astronomical & Astrophysical Society (sf2a

FROM 3D IMAGING OF STRUCTURES TO DIFFUSIVE PROPERTIES OF ANISOTROPIC CELLULAR MATERIALS

International audienceThis paper deals with diffusive properties phenomena in metallic foams. We have developed a 3D morphological tool to extract geometrical characteristics of the media from X-ray images. The anisotropy of the geometry of each phase is observed and the relationship between microstructure and effective properties is analyzed. We emphasize on geometrical tortuosity determination and impact on con¬ductive transport tensor. The conductive heat transfers are computed on a vertex-edge network to determine directional effective conductivities by solving the energy equa¬tion on this network. We realize a systematic study carried on a wide range of different Nickel foam samples. Finally, we propose a simple model of effective diffusion prop¬erties dependence on tortuosity and porosity

Small scale structure in diffuse molecular gas from repeated FUSE and visible spectra of HD 34078

We present preliminary results from an ongoing program devoted to a study of small scale structure in the spatial distribution of molecular gas. Our work is based on multi-epoch FUSE and visible observations of HD34078. A detailed comparison of H2, CH and CH+ absorption lines is performed. No short term variations are seen (except for highly excited H2) but long-term changes in N(CH) are clearly detected when comparing our data to spectra taken about 10 years ago.Comment: 4 pages, 2 figures, To appear in the Proceedings of the XVII IAP Colloquium "Gaseous Matter in Galaxies and Intergalactic Space

Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition

International audienceThe transcriptions used to train an Automatic Speech Recognition (ASR) system may contain errors. Usually, either a quality control stage discards transcriptions with too many errors, or the noisy transcriptions are used as is. We introduce Lead2Gold, a method to train an ASR system that exploits the full potential of noisy transcriptions. Based on a noise model of transcription errors, Lead2Gold searches for better transcriptions of the training data with a beam search that takes this noise model into account. The beam search is differentiable and does not require a forced alignment step, thus the whole system is trained end-to-end. Lead2Gold can be viewed as a new loss function that can be used on top of any sequence-to-sequence deep neural network. We conduct proof-of-concept experiments on noisy transcriptions generated from letter corruptions with different noise levels. We show that Lead2Gold obtains a better ASR accuracy than a competitive baseline which does not account for the (artificially-introduced) transcription noise