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. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.

[1]  Matthias Joachim Ehrhardt,et al.  Motion estimation and correction for simultaneous PET/MR using SIRF and CIL , 2021, Philosophical Transactions of the Royal Society A.

[2]  J. S. Jørgensen,et al.  Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction , 2021, Scientific Reports.

[3]  William R B Lionheart,et al.  Crystalline phase discriminating neutron tomography using advanced reconstruction methods , 2021, Journal of Physics D: Applied Physics.

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

[5]  Alexander Meaney,et al.  Gel phantom data for dynamic X-ray tomography , 2020, 2003.02841.

[6]  S. Siltanen,et al.  Sparse dynamic tomography: a shearlet-based approach for iodine perfusion in plant stems , 2020, Inverse Problems.

[7]  M. Hintermüller,et al.  Dualization and Automatic Distributed Parameter Selection of Total Generalized Variation via Bilevel Optimization , 2020, Numerical Functional Analysis and Optimization.

[8]  Simon R. Arridge,et al.  Solving inverse problems using data-driven models , 2019, Acta Numerica.

[9]  C. Schönlieb,et al.  Tomographic reconstruction with spatially varying parameter selection , 2018, Inverse Problems.

[10]  Jakob S Jørgensen,et al.  New software protocols for enabling laboratory based temporal CT. , 2018, The Review of scientific instruments.

[11]  Pawel Markiewicz,et al.  Faster PET reconstruction with non-smooth priors by randomization and preconditioning , 2018, Physics in medicine and biology.

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

[13]  David Atkinson,et al.  SIRF: Synergistic Image Reconstruction Framework , 2017, 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[14]  Antonin Chambolle,et al.  Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications , 2017, SIAM J. Optim..

[15]  Andreas Hauptmann,et al.  A variational reconstruction method for undersampled dynamic x-ray tomography based on physical motion models , 2017, 1705.06079.

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

[17]  Manuchehr Soleimani,et al.  TIGRE: a MATLAB-GPU toolbox for CBCT image reconstruction , 2016 .

[18]  Pawel Markiewicz,et al.  PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets , 2016, IEEE Transactions on Medical Imaging.

[19]  Marta M. Betcke,et al.  Multicontrast MRI Reconstruction with Structure-Guided Total Variation , 2015, SIAM J. Imaging Sci..

[20]  P. Withers,et al.  3D chemical imaging in the laboratory by hyperspectral X-ray computed tomography , 2015, Scientific Reports.

[21]  Tao Zhang,et al.  A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning , 2015, Comput. Math. Methods Medicine.

[22]  Andreas Langer,et al.  Automated Parameter Selection for Total Variation Minimization in Image Restoration , 2015, Journal of Mathematical Imaging and Vision.

[23]  Carola-Bibiane Schönlieb,et al.  Bilevel Parameter Learning for Higher-Order Total Variation Regularisation Models , 2015, Journal of Mathematical Imaging and Vision.

[24]  Michael Möller,et al.  Collaborative Total Variation: A General Framework for Vectorial TV Models , 2015, SIAM J. Imaging Sci..

[25]  Matti Lassas,et al.  Dynamic multi-source X-ray tomography using a spacetime level set method , 2015, J. Comput. Phys..

[26]  Xiaochuan Pan,et al.  Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization. , 2015, Medical physics.

[27]  Gabriele Steidl,et al.  First order algorithms in variational image processing , 2014, ArXiv.

[28]  David Atkinson,et al.  Joint reconstruction of PET-MRI by exploiting structural similarity , 2014, Inverse Problems.

[29]  Ville Kolehmainen,et al.  Multiresolution Parameter Choice Method for Total Variation Regularized Tomography , 2014, SIAM J. Imaging Sci..

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

[31]  J. Aujol,et al.  Some proximal methods for Poisson intensity CBCT and PET , 2012 .

[32]  Raymond H. Chan,et al.  Parameter selection for total-variation-based image restoration using discrepancy principle , 2012, IEEE Transactions on Image Processing.

[33]  E. Sidky,et al.  Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle–Pock algorithm , 2011, Physics in medicine and biology.

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

[35]  Yiqiu Dong,et al.  Automated Regularization Parameter Selection in Multi-Scale Total Variation Models for Image Restoration , 2011, Journal of Mathematical Imaging and Vision.

[36]  Luca Zanni,et al.  A discrepancy principle for Poisson data , 2010 .

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

[38]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[39]  C. McCollough,et al.  Radiation dose reduction in computed tomography: techniques and future perspective. , 2009, Imaging in medicine.

[40]  B. Münch,et al.  Stripe and ring artifact removal with combined wavelet--Fourier filtering. , 2009, Optics express.

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

[42]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

[43]  Jiayu Song,et al.  Sparseness prior based iterative image reconstruction for retrospectively gated cardiac micro-CT. , 2007, Medical physics.

[44]  Jacques A. de Guise,et al.  A method for modeling noise in medical images , 2004, IEEE Transactions on Medical Imaging.

[45]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[46]  Pierre Grangeat,et al.  Dynamic X-ray computed tomography , 2003, Proc. IEEE.

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

[48]  D. Calvetti,et al.  Tikhonov regularization and the L-curve for large discrete ill-posed problems , 2000 .

[49]  Per Christian Hansen,et al.  Analysis of Discrete Ill-Posed Problems by Means of the L-Curve , 1992, SIAM Rev..

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

[51]  V. A. Morozov,et al.  Methods for Solving Incorrectly Posed Problems , 1984 .

[52]  S J Riederer,et al.  Relative properties of tomography, K-edge imaging, and K-edge tomography. , 1977, Medical physics.

[53]  Casper O. da Costa-Luis,et al.  Motion estimation and correction for simultaneous PET/MR using SIRF , 2021 .

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

[55]  Anders Kaestner,et al.  Sparse-view Reconstruction of Dynamic Processes by Neutron Tomography , 2017 .

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

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

[58]  Simon R. Arridge,et al.  Vector-Valued Image Processing by Parallel Level Sets , 2014, IEEE Transactions on Image Processing.