Measurement of the e ffective lifetime of the B0 s meson using the flavour speci fic decay Bs → D-s π + at the LHCb experiment.

This thesis presents a measurement of the effective B0s decay width, ΓFS, from a single exponential fit to the flavour-specific decay channel B0s → D-s π +. This measurement is based on an integrated luminosity of 340 pb-1 recorded by LHCb in 2011 at a center of mass energy of 7TeV. The dataset is divided into two exclusive selections. B0s → D-s (( ϕ →K-K+) π-)π + only has a significant background contribution arising from combinatorial background, and the modelling of this is determined entirely by the data. B0s → D-s ((K-K* (892)0 → K+ π-))π + has a larger contribution from combinatoric and mis-identified background and provides an alternative measurement. A simultaneous fit for the effective B0s decay width is performed to both the datasets leading to the result: ΓFS = 0:668 ± 0:017 ± 0:031 ps-1 The result is then combined with information from the LHCb B0s → J/ψØ analysis leading to an improved measurement of the average B0 s decay width: Γs = 0:666 ± 0:010 ± 0:031 ps-

Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-10-01, epub 2021-10-21Publication status: PublishedHere we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (~ 1 keV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic K-edge. Conventionally, because the photons detected at each pixel are distributed across as many as 200 energy channels, energy-selective images are characterised by low count-rates and poor signal-to-noise ratio. This means high X-ray exposures, long scan times and high doses are required to image unique spectral markers. Here, we achieve high quality energy-dispersive tomograms from low dose, noisy datasets using a dedicated iterative reconstruction algorithm. This exploits the spatial smoothness and inter-channel structural correlation in the spectral domain using two carefully chosen regularisation terms. For a multi-phase phantom, a reduction in scan time of 36 times is demonstrated. Spectral analysis methods including K-edge subtraction and absorption step-size fitting are evaluated for an ex vivo, single (iodine)-stained biological sample, where low chemical concentration and inhomogeneous distribution can affect soft tissue segmentation and visualisation. The reconstruction algorithms are available through the open-source Core Imaging Library. Taken together, these tools offer new capabilities for visualisation and elemental mapping, with promising applications for multiply-stained biological specimens

Crystalline phase discriminating neutron tomography using advanced reconstruction methods

Time-of-flight neutron imaging offers complementary attenuation contrast to X-ray computed tomography (CT), coupled with the ability to extract additional information from the variation in attenuation as a function of neutron energy (time of flight) at every point (voxel) in the image. In particular Bragg edge positions provide crystallographic information and therefore enable the identification of crystalline phases directly. Here we demonstrate Bragg edge tomography with high spatial and spectral resolution. We propose a new iterative tomographic reconstruction method with a tailored regularisation term to achieve high quality reconstruction from low-count data, where conventional filtered back-projection (FBP) fails. The regularisation acts in a separated mode for spatial and spectral dimensions and favours characteristic piece-wise constant and piece-wise smooth behaviour in the respective dimensions. The proposed method is compared against FBP and a state-of-the-art regulariser for multi-channel tomography on a multi-material phantom. The proposed new regulariser which accommodates specific image properties outperforms both conventional and state-of-the-art methods and therefore facilitates Bragg edge fitting at the voxel level. The proposed method requires significantly shorter exposure to retrieve features of interest. This in turn facilitates more efficient usage of expensive neutron beamline time and enables the full utilisation of state-of-the-art high resolution detectors

Core Imaging Library - Part II:multichannel reconstruction for dynamic and spectral tomography

The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL’s capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots

An improved segmentation method for multi-material beam hardening correction in industrial x-ray computed tomography

status: publishe

A directional regularization method for the limited-angle Helsinki Tomography Challenge using the Core Imaging Library (CIL)

This article presents the algorithms developed by the Core Imaging Library (CIL) developer team for the Helsinki Tomography Challenge 2022. The challenge focused on reconstructing 2D phantom shapes from limited-angle computed tomography (CT) data. The CIL team designed and implemented five reconstruction methods using CIL (https://ccpi.ac.uk/cil/), an open-source Python package for tomographic imaging. The CIL team adopted a model-based reconstruction strategy, unique to this challenge with all other teams relying on deep-learning techniques. The CIL algorithms showcased exceptional performance, with one algorithm securing the third place in the competition. The best-performing algorithm employed careful CT data pre-processing and an optimization problem with single-sided directional total variation regularization combined with isotropic total variation and tailored lower and upper bounds. The reconstructions and segmentations achieved high quality for data with angular ranges down to 50 degrees, and in some cases acceptable performance even at 40 and 30 degrees. This study highlights the effectiveness of model-based approaches in limited-angle tomography and emphasizes the importance of proper algorithmic design leveraging on available prior knowledge to overcome data limitations. Finally, this study highlights the flexibility of CIL for prototyping and comparison of different optimization methods

A simulation-based study on the influence of the x-ray spectrum on the performance of multi-material beam hardening correction algorithms

© 2018 IOP Publishing Ltd. Beam hardening artefacts caused by the polychromatic nature of the x-ray spectra are known to deteriorate the reconstructed image quality in multi-material industrial computed tomography. A variety of beam hardening correction (BHC) algorithms exist. Most of these methods rest on the x-ray spectra to a certain extent, which means their performance may be hindered if the spectral information is not accurate. The dependence of these methods on the spectral information, however, has not been benchmarked. This work addresses the need for such investigation by applying two sets of spectra - (1) a set of the true spectra used to produce the radiography, and (2) a set of approximated spectra acquired from simulation - to three multi-material BHC algorithms of different types. The algorithms are a segmentation based linearisation algorithm, a dual-energy algorithm, and an iterative reconstruction algorithm. Our objective in this study is to estimate the dependence of these three algorithms on spectral information. For comparable accuracy, multiple metrics are employed to quantify the performance of the methods in terms of artefact presence and dimensional metrology. The results show that under the same initial conditions, dual-energy appears to be the most sensitive one to the spectral change. Contrariwise, the segmentation method is least spectrally sensitive. The iterative method is stable over the spectral change, but performs poorly in dimensional metrology.status: publishe

dkazanc/TomoPhantom: TomoPhantom v.3.0

<ul> <li><pre><code>Totally refactored with Ctypes into two separate packages: C library + Python</code></pre> </li> <li><pre><code>Cython Wrappers removed</code></pre> </li> <li><pre><code>120 tests added!</code></pre> </li> <li><pre><code>All API exposed in TomoP2D and TomoP3D as Python functions (not Cython)</code></pre> </li> <li><pre><code>NEW [Documentation](https://dkazanc.github.io/TomoPhantom/) page with sphinx</code></pre> </li> <li><pre><code>Demos updated</code></pre> see some <a href="https://github.com/dkazanc/TomoPhantom/blob/master/CHANGES.md">changes</a> with respect to API.</li> </ul&gt