Registration-based multi-orientation tomography.

We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and experiments show that the solutions computed using algebraic methods based on multiple acquisitions can achieve a dramatic improvement in the reconstruction quality, even when each acquisition generates a reduced number of projections. The approach does not require any advanced setup components making it ideal for laboratory-based X-ray tomography.

[1]  F. Boas,et al.  CT artifacts: Causes and reduction techniques , 2012 .

[2]  Christian Winne,et al.  ORBIT - open X-ray scanner for image-guided interventional surgery - development of concept , 2011, GI-Jahrestagung.

[3]  Marc Kachelrieß,et al.  Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network , 2018, Journal of Nondestructive Evaluation.

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

[5]  J H Siewerdsen,et al.  Task-driven orbit design and implementation on a robotic C-arm system for cone-beam CT , 2017, Medical Imaging.

[6]  R. Drai,et al.  X-rays tomographic reconstruction images using proximal methods based on L1 Norm and TV regularization , 2017 .

[7]  P M Joseph,et al.  The effects of scatter in x-ray computed tomography. , 1982, Medical physics.

[8]  D. Mastronarde.  Dual-axis tomography: an approach with alignment methods that preserve resolution. , 1997, Journal of structural biology.

[9]  Emiliano Schena,et al.  Emerging clinical applications of computed tomography , 2015, Medical devices.

[10]  Jeffrey A. Fessler.  Penalized weighted least-squares image reconstruction for positron emission tomography , 1994, IEEE Trans. Medical Imaging.

[11]  Daniel L. Rubin,et al.  Volumetric Image Registration From Invariant Keypoints , 2017, IEEE Transactions on Image Processing.

[12]  E. Casteele.  Model-based approach for beam hardening correction and resolution measurements in microtomography , 2004 .

[13]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[14]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[15]  Jan Flusser,et al.  Moment Invariants in Image Analysis , 2007 .

[16]  Wei Xu,et al.  On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs , 2010, Comput. Methods Programs Biomed..

[17]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[18]  W. Marsden.  I and J , 2012 .

[19]  M. Flower,et al.  Automated CT marker segmentation for image registration in radionuclide therapy. , 2001, Physics in medicine and biology.

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  Kees Joost Batenburg,et al.  A Novel Tomographic Reconstruction Method Based on the Robust Student's t Function For Suppressing Data Outliers , 2017, IEEE Transactions on Computational Imaging.

[22]  Thomas Boudier,et al.  Multiple‐axis tomography: applications to basal bodies from Paramecium tetraurelia , 2006, Biology of the cell.

[23]  M. Osanai,et al.  Photon starvation artifacts of X-ray CT: their true cause and a solution , 2012, Radiological Physics and Technology.

[24]  T. Sauer,et al.  Metal artifact reduction by fusion of CT scans from different positions using the unfiltered backprojection , 2018 .

[25]  M. P. Morigi,et al.  Application of X-ray Computed Tomography to Cultural Heritage diagnostics , 2010 .

[26]  M. Koseki,et al.  Development of a multi-axis X-ray CT for highly accurate inspection of electronic devices , 2017 .

[27]  C. McCollough,et al.  CT scanner x-ray spectrum estimation from transmission measurements. , 2011, Medical physics.

[28]  Anton du Plessis,et al.  Comparison of medical and industrial X-ray computed tomography for non-destructive testing , 2016 .

[29]  Peter B. Noël,et al.  Object Specific Trajectory Optimization for Industrial X-ray Computed Tomography , 2016, Scientific Reports.

[30]  David R. Haynor,et al.  Nonrigid multimodality image registration , 2001, SPIE Medical Imaging.

[31]  Marc Kachelrieß,et al.  Simulation-based artifact correction (SBAC) for metrological computed tomography , 2017 .

[32]  Rolf Clackdoyle,et al.  Cone-beam reconstruction using the backprojection of locally filtered projections , 2005, IEEE Transactions on Medical Imaging.

[33]  Shiyu Xu,et al.  Deep residual learning enabled metal artifact reduction in CT , 2018, Medical Imaging.

[34]  Marc Kachelrieß,et al.  Segmentation-free empirical beam hardening correction for CT. , 2015, Medical physics.

[35]  Toru Kano,et al.  A new metal artifact reduction algorithm based on a deteriorated CT image. , 2016, Journal of X-ray science and technology.

[36]  A. Dell'Acqua,et al.  Geant4—a simulation toolkit , 2003 .

[37]  Dominique Bernard,et al.  3D imaging in material science: Application of X-ray tomography , 2010 .

[38]  B. Metscher.  Biological applications of X-ray microtomography: imaging micro- anatomy, molecular expression and organismal diversity , 2013 .

[39]  Bruce R Whiting,et al.  Statistical reconstruction for x-ray computed tomography using energy-integrating detectors , 2007, Physics in medicine and biology.

[40]  M. Defrise,et al.  A filtered-backprojection cone-beam algorithm for general orbits with implementation details for the two-orthogonal-circles orbit , 1993, 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference.