Realization of the one-dimensional anisotropic XY model in a Tb(III)-W(V) chain compound

We report the magnetic behavior of the one-dimensional (1D) cyanido-bridged chain complex [Tb(pzam) 3(H 2O)M(CN) 8] •H 2O, where M = W(V). The system shows qualitatively similar magnetic behavior with its already reported M = Mo(V): a broad anomaly in the specific heat ascribed to the magnetic interactions, a transition to three-dimensional magnetic order at T C = 1.15 K, and comparable magnetization and susceptibility. However, substituting the Mo(V) ion by the larger W(V) causes a drastic change in the symmetry of the Tb(III) g tensor, whereby the magnetic interaction between the Tb(III) and M(V) changes from Ising type into an anisotropic XY exchange. We analyze the data in terms of theoretical predictions for the 1D XYZ Hamiltonian and we find an excellent agreement between the theory and experimental data (J x = 1.89 K, J y = 2J x, J z = 0). © 2012 American Physical Society.This research was supported by a Veni grant from the Netherlands Organization for Scientific Research (NWO) to S.T.. We acknowledge Spanish MINECO for Grants MAT2009-13977-C03 and CSD2007-00010.Peer Reviewe

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

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'

SIRF: Synergistic Image Reconstruction Framework

The combination of positron emission tomography (PET) with magnetic resonance (MR) imaging opens the way to more accurate diagnosis and improved patient management. At present, the data acquired by PET-MR scanners are essentially processed separately, but the opportunity to improve accuracy of the tomographic reconstruction via synergy of the two imaging techniques is an active area of research. In this paper, we present Release 2.1.0 of the CCP-PETMR Synergistic Image Reconstruction Framework (SIRF) software suite, providing an open-source software platform for efficient implementation and validation of novel reconstruction algorithms. SIRF provides user-friendly Python and MATLAB interfaces built on top of C++ libraries. SIRF uses advanced PET and MR reconstruction software packages and tools. Currently, for PET this is Software for Tomographic Image Reconstruction (STIR); for MR, Gadgetron and ISMRMRD; and for image registration tools, NiftyReg. The software aims to be capable of reconstructing images from acquired scanner data, whilst being simple enough to be used for educational purposes

First results of the CUORICINO experiment

Preliminary results on double beta decay (DBD) of 130 Te, obtained in the first run of the CUORICINO experiment are presented. The set-up consists of an array of 62 crystals of TeO 2 operating as bolometers in a deep underground dilution unit at a temperature of about 10 mK. Due to a total mass of about 41 kg, CUORICINO represents by far the most massive running cryogenic mass to search for rare events. The achieved lower limit on the neutrinoless DBD is 5.5⋅10 23 years, that corresponds to a limit on the Majorana effective mass between 0.37 and 1.9 eV. Performances of the detectors together with the sensitivity estimation are discussed

TomoPhantom, a software package to generate 2D–4D analytical phantoms for CT image reconstruction algorithm benchmarks

In the field of computerized tomographic imaging, many novel reconstruction techniques are routinely tested using simplistic numerical phantoms, e.g. the well-known Shepp–Logan phantom. These phantoms cannot sufficiently cover the broad spectrum of applications in CT imaging where, for instance, smooth or piecewise-smooth 3D objects are common. TomoPhantom provides quick access to an external library of modular analytical 2D/3D phantoms with temporal extensions. In TomoPhantom, quite complex phantoms can be built using additive combinations of geometrical objects, such as, Gaussians, parabolas, cones, ellipses, rectangles and volumetric extensions of them. Newly designed phantoms are better suited for benchmarking and testing of different image processing techniques. Specifically, tomographic reconstruction algorithms which employ 2D and 3D scanning geometries, can be rigorously analyzed using the software. TomoPhantom also provides a capability of obtaining analytical tomographic projections which further extends the applicability of software towards more realistic, free from the “inverse crime” testing. All core modules of the package are written in the C-OpenMP language and wrappers for Python and MATLAB are provided to enable easy access. Due to C-based multi-threaded implementation, volumetric phantoms of high spatial resolution can be obtained with computational efficiency. Keywords: Phantoms, Tomography, Image reconstruction, Iterative methods, Open-sourc

CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms

Iterative reconstruction algorithms are often needed to help solve ill-posed inverse problems in computed tomography (CT), especially cases when tomographic projection data are corrupt, noisy or angularly undersampled. Model-based iterative methods can be adapted to fit the measurement characteristics of the data (e.g. noise statistics) and expectations regarding the reconstructed object (e.g. morphology). The prior information is usually introduced in the form of a regulariser, making the inversion task well-posed.The CCPi-Regularisation toolkit provides a set of variational regularisers (denoisers) which can be embedded in a plug-and-play fashion into proximal splitting methods for image reconstruction. CCPi-RGL comes with algorithms that can satisfy various prior expectations of the reconstructed object, for example being piecewise-constant or piecewise-smooth in nature. The toolkit is written in C language and exploits parallelism with OpenMP directives and the CUDA API; and is wrapped for the Python and MATLAB environments. This paper introduces the toolkit and gives recommendations for selecting a suitable prior model. Keywords: X-ray CT, Iterative methods, Model-based, Regularisation, Denoising, Primal–dual, Big-dat