Crystalline phase discriminating neutron tomography using advanced reconstruction methods

Time-of-flight (ToF) neutron imaging offers complementary attenuation contrast to x-ray computed tomography, coupled with the ability to extract additional information from the variation in attenuation as a function of neutron energy (ToF) 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 exposures 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.

[1]  Davide Micieli,et al.  Time-of-Flight Neutron Imaging on IMAT@ISIS: A New User Facility for Materials Science , 2018, J. Imaging.

[2]  J. Banhart,et al.  Neutron imaging in materials science , 2011 .

[3]  K. Perez Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 2014 .

[4]  Philip J. Withers,et al.  Sparsity seeking total generalized variation for undersampled tomographic reconstruction , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[5]  Kevin M. Holt,et al.  Total Nuclear Variation and Jacobian Extensions of Total Variation for Vector Fields , 2014, IEEE Transactions on Image Processing.

[6]  Martin Turner,et al.  CCPi-Regularisation toolkit for computed tomographic image reconstruction with proximal splitting algorithms , 2019, SoftwareX.

[7]  Philip J. Withers,et al.  Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction , 2021, Scientific reports.

[8]  Michael E. Fitzpatrick,et al.  Modelling of an imaging beamline at the ISIS pulsed neutron source , 2013 .

[9]  Rafidah Zainon,et al.  Toward quantifying the composition of soft tissues by spectral CT with Medipix3. , 2012, Medical physics.

[10]  Philip J. Withers,et al.  Joint image reconstruction method with correlative multi-channel prior for x-ray spectral computed tomography , 2018 .

[11]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[12]  G. Anton,et al.  Quantitative Material Reconstruction in CT with Spectroscopic X-ray Pixel Detectors -- a Simulation Study , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[13]  F. Doty Neutron Imaging , 2022 .

[14]  P. Withers,et al.  Quantitative X-ray tomography , 2014 .

[15]  G. A. Schlapper,et al.  Neutron tomography investigations at the Missouri University Research Reactor , 1977 .

[16]  Richard Huber,et al.  Coupled regularization with multiple data discrepancies , 2017, Inverse problems.

[17]  E. Adalsteinsson,et al.  Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI. , 2016, Magnetic resonance imaging.

[18]  Feng Yang,et al.  Material Decomposition in X-ray Spectral CT Using Multiple Constraints in Image Domain , 2019, Journal of Nondestructive Evaluation.

[19]  Karl Kunisch,et al.  Total Generalized Variation , 2010, SIAM J. Imaging Sci..

[20]  C. McCollough,et al.  Quantitative imaging of element composition and mass fraction using dual-energy CT: three-material decomposition. , 2009, Medical physics.

[21]  Stephen P. Boyd,et al.  Graph Implementations for Nonsmooth Convex Programs , 2008, Recent Advances in Learning and Control.

[22]  John Banhart,et al.  Neutron Bragg-edge-imaging for strain mapping under in situ tensile loading , 2011 .

[23]  Jakob Sauer Jørgensen,et al.  Sparse Image Reconstruction in Computed Tomography , 2013 .

[24]  Philip J. Withers,et al.  Engineering applications of Bragg-edge neutron transmission , 2002 .

[25]  K. An,et al.  Applying neutron transmission physics and 3D statistical full-field model to understand 2D Bragg-edge imaging , 2018 .

[26]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[27]  Jan Sijbers,et al.  The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography. , 2015, Ultramicroscopy.

[28]  E. Sidky,et al.  Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT , 2009, 0904.4495.

[29]  Mark R. Daymond,et al.  Strain imaging by Bragg edge neutron transmission , 2002 .

[30]  Kris Thielemans,et al.  Core Imaging Library - Part I: a versatile Python framework for tomographic imaging , 2021, Philosophical Transactions of the Royal Society A.

[31]  Jonas Adler,et al.  EDS tomographic reconstruction regularized by total nuclear variation joined with HAADF-STEM tomography. , 2018, Ultramicroscopy.

[32]  Eberhard Lehmann,et al.  Energy-selective neutron transmission imaging at a pulsed source , 2007 .

[33]  Philip J. Withers,et al.  Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography , 2021, Philosophical Transactions of the Royal Society A.

[34]  Alexander Liptak,et al.  Developments towards Bragg edge imaging on the IMAT beamline at the ISIS pulsed neutron and muon source: BEAn software , 2019, Journal of Physics Communications.

[35]  J. McPhate,et al.  Energy-Resolving Neutron Transmission Radiography at the ISIS Pulsed Spallation Source With a High-Resolution Neutron Counting Detector , 2008, IEEE Transactions on Nuclear Science.

[36]  Philip J. Withers,et al.  Using pulsed neutron transmission for crystalline phase imaging and analysis , 2005 .

[37]  Jason McPhate,et al.  Optimization of Timepix count rate capabilities for the applications with a periodic input signal , 2014 .

[38]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[39]  M. Strobl,et al.  Bragg-edge attenuation spectra at voxel level from 4D wavelength-resolved neutron tomography , 2020, Journal of Applied Crystallography.

[40]  K. Bredies,et al.  Infimal convolution of total generalized variation functionals for dynamic MRI , 2017, Magnetic resonance in medicine.

[41]  A. Tremsin,et al.  Cross-sectional imaging of quenched region in a steel rod using energy-resolved neutron tomography , 2019, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment.

[42]  Kristian Bredies,et al.  Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer , 2017, IEEE Transactions on Medical Imaging.

[43]  T. Springer,et al.  The use of neutron optical devices on beam-hole experiments on beam-hole experiments , 1963 .

[44]  Kristian Bredies,et al.  Total generalized variation regularization for multi-modal electron tomography. , 2019, Nanoscale.

[45]  Jan Sijbers,et al.  Fast and flexible X-ray tomography using the ASTRA toolbox. , 2016, Optics express.

[46]  K J Batenburg,et al.  Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs). , 2011, Journal of structural biology.

[47]  John Banhart,et al.  Advances in neutron radiography and tomography , 2009 .

[48]  J. Schlomka,et al.  Experimental feasibility of multi-energy photon-counting K-edge imaging in pre-clinical computed tomography , 2008, Physics in medicine and biology.

[49]  Patrick J La Rivière,et al.  Joint reconstruction of multi-channel, spectral CT data via constrained total nuclear variation minimization , 2014, Physics in medicine and biology.

[50]  Vladimir Luzin,et al.  Bragg-edge neutron transmission strain tomography for in situ loadings , 2016 .

[51]  Ryan R. Dehoff,et al.  Characterization of Crystallographic Structures Using Bragg-Edge Neutron Imaging at the Spallation Neutron Source , 2017, J. Imaging.

[52]  E. Roessl,et al.  K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors , 2007, Physics in medicine and biology.

[53]  J. V. Vallerga,et al.  High Resolution Photon Counting With MCP-Timepix Quad Parallel Readout Operating at $> 1~{\rm KHz}$ Frame Rates , 2013, IEEE Transactions on Nuclear Science.

[54]  Mirko Boin,et al.  nxs: a program library for neutron cross section calculations , 2012 .

[55]  D. Penumadu,et al.  3D Mapping of Crystallographic Phase Distribution using Energy‐Selective Neutron Tomography , 2014, Advanced materials.

[56]  T. Marrow,et al.  Application of neutron imaging to detect and quantify fatigue cracking , 2019, International Journal of Mechanical Sciences.

[57]  Nikolay Kardjilov,et al.  Validation of Bragg edge experiments by Monte Carlo simulations for quantitative texture analysis , 2011 .

[58]  Xiaochuan Pan,et al.  An algorithm for constrained one-step inversion of spectral CT data , 2015, Physics in medicine and biology.

[59]  Kenichi Watanabe,et al.  Characterization of a neutron sensitive MCP/Timepix detector for quantitative image analysis at a pulsed neutron source , 2017 .