Abstract

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.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

J. Maier, S. Sawall, M. Knaup, and M. Kachelrieß, “Deep scatter estimation (dse): Accurate real-time scatter estimation for x-ray ct using a deep convolutional neural network,” J. Nondestruct. Eval. 37, 57 (2018).
[Crossref]

2017 (3)

J. Maier, C. Leinweber, S. Sawall, H. Stoschus, F. Ballach, T. Müller, M. Hammer, R. Christoph, and M. Kachelrieß, “Simulation-based artifact correction (sbac) for metrological computed tomography,” Meas. Sci. Technol. 28, 065011 (2017).
[Crossref]

D. Kazantsev, F. Bleichrodt, T. van Leeuwen, A. Kaestner, P. Withers, K. J. Batenburg, and P. D. Lee, “A novel tomographic reconstruction method based on the robust student’s t function for suppressing data outliers,” IEEE Trans. Comput. Imaging 3, 682–693 (2017).
[Crossref]

B. Rister, M. A. Horowitz, and D. L. Rubin, “Volumetric image registration from invariant keypoints,” IEEE Trans. Image Process. 26, 4900–4910 (2017).
[Crossref] [PubMed]

2016 (4)

W. van Aarle, W. Jan Palenstijn, J. Cant, E. Janssens, F. Bleichrodt, A. Dabravolski, J. De Beenhouwer, K. Batenburg, and J. Sijbers, “Fast and flexible x-ray tomography using the astra toolbox,” Opt. Express 24, 25129–25147 (2016).
[Crossref] [PubMed]

A. Fischer, T. Lasser, M. Schrapp, J. Stephan, and P. Noël, “Object specific trajectory optimization for industrial x-ray computed tomography,” Sci. Reports 6, 19135 (2016).
[Crossref]

T. Kano and M. Koseki, “A new metal artifact reduction algorithm based on a deteriorated ct image,” J. X-Ray Sci. Technol. 24, 901–912 (2016).
[Crossref]

A. Du Plessis, S. Gerhard le Roux, and A. Guelpa, “Comparison of medical and industrial x-ray computed tomography for non-destructive testing,” Case Stud. Nondestruct. Test. Eval. 6, 17–25 (2016).
[Crossref]

2015 (2)

C. Liguori, G. Frauenfelder, C. Massaroni, P. Saccomandi, F. Giurazza, F. Pitocco, R. Marano, and E. Schena, “Emerging clinical applications of computed tomography,” Med. Devices: Evid. Res. 8, 265–278 (2015).

S. Schüller, S. Sawall, K. Stannigel, M. Hulsbusch, J. Ulrici, E. Hell, and M. Kachelrieß, “Segmentation-free empirical beam hardening correction for ct,” Med. physics 42, 794–803 (2015).
[Crossref]

2013 (1)

B. Metscher, “Biological applications of x-ray microtomography: imaging microanatomy, molecular expression and organismal diversity,” Microsc Anal (Am Ed) 27, 13–16 (2013).

2012 (2)

I. Mori, Y. Machida, M. Osanai, and K. Iinuma, “Photon starvation artifacts of x-ray ct: Their true cause and a solution,” Radiol. Phys. Technol. 6, 130–141 (2012).
[Crossref] [PubMed]

F. Boas and D. Fleischmann, “Ct artifacts: causes and reduction techniques,” Imaging Med. 4, 229–240 (2012).
[Crossref]

2011 (2)

X. Duan, J. Wang, L. Yu, S. Leng, and C. H McCollough, “Ct scanner x-ray spectrum estimation from transmission measurements,” Med. Phys. 38, 993–997 (2011).
[Crossref] [PubMed]

J. Flusser, “Moment invariants in image analysis,” Trans. Engin. Comput. Technol. 1, 3721–3726 (2011).

2010 (2)

L. Salvo, M. Suery, A. Marmottant, N. Limodin, and D. Bernard, “3d imaging in material science: Application of x-ray tomography,” Comptes Rendus Physique 11, 641–649 (2010).
[Crossref]

M. P. Morigi, F. Casali, M. Bettuzzi, R. Brancaccio, and V. D’ Errico, “Application of x-ray computed tomography to cultural heritage diagnostics,” Appl. Phys. A 100, 653–661 (2010).
[Crossref]

2009 (1)

F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on gpus,” Comput. Methods Programs Biomed. 98, 261–270 (2009).
[Crossref] [PubMed]

2007 (1)

G. Lasio, B. R Whiting, and J. F Williamson, “Statistical reconstruction for x-ray computed tomography using energy-integrating detectors,” Phys. Med. Biol. 52, 2247–2266 (2007).
[Crossref] [PubMed]

2006 (1)

C. Messaoudi, N. Garreau de Loubresse, T. Boudier, P. Dupuis-Williams, and S. Marco, “Multiple-axis tomography: Applications to basal bodies from paramecium tetraurelia,” Biol. cell / under auspices Eur. Cell Biol. Organ. 98, 415–425 (2006).
[Crossref]

2005 (1)

J. D. Pack, F. Noo, and R. Clackdoyle, “Cone-beam reconstruction using the backprojection of locally filtered projections,” IEEE Trans. Med. Imaging 24, 70–85 (2005).
[Crossref] [PubMed]

2003 (1)

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

2001 (1)

P. Papapavasileiou, G. Flux, M. A. Flower, and M. Guy, “Note: Automated ct marker segmentation for image registration in radionuclide therapy,” Phys. Med. Biol. 46, N269–N279 (2001).
[Crossref]

1998 (1)

D. N. Mastronarde, “Dual-axis tomography: An approach with alignment methods that preserve resolution,” J. Struct. Biol. 120, 343–352 (1998).
[Crossref] [PubMed]

1994 (1)

J. A. Fessler, “Penalized weighted least-squares image reconstruction for positron emission tomography,” IEEE Trans. Med. Imaging 13, 290–300 (1994).
[Crossref] [PubMed]

1984 (1)

1982 (1)

P. M. Joseph and R. D. Spital, “The effects of scatter in x-ray computed tomography,” Med. Phys. 9, 464–472 (1982).
[Crossref] [PubMed]

1979 (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
[Crossref]

Ackerman, M. J.

T. S. Yoo, M. J. Ackerman, and W. E. Lorensen, “Engineering and algorithm design for an image processing API: A technical report on itk – the insight toolkit,” in Proc. of Medicine Meets Virtual Reality, (2002), pp. 586–592.

Agard, D.

F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on gpus,” Comput. Methods Programs Biomed. 98, 261–270 (2009).
[Crossref] [PubMed]

Agostinelli, S.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Allag, A.

A. Allag, R. Drai, A. Benammar, and T. Boutkedjirt, “X-rays tomographic reconstruction images using proximal methods based on l1 norm and tv regularization,” in Procedia Computer Science, vol. 127 (ICDS2017, 2018), pp. 236–245.
[Crossref]

Allison, J.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Amako, K.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Apostolakis, J.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Araujo, H.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Arce, P.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Asai, M.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Axen, D.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Ballach, F.

J. Maier, C. Leinweber, S. Sawall, H. Stoschus, F. Ballach, T. Müller, M. Hammer, R. Christoph, and M. Kachelrieß, “Simulation-based artifact correction (sbac) for metrological computed tomography,” Meas. Sci. Technol. 28, 065011 (2017).
[Crossref]

Banerjee, S.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Barrand, G.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Batenburg, K.

Batenburg, K. J.

D. Kazantsev, F. Bleichrodt, T. van Leeuwen, A. Kaestner, P. Withers, K. J. Batenburg, and P. D. Lee, “A novel tomographic reconstruction method based on the robust student’s t function for suppressing data outliers,” IEEE Trans. Comput. Imaging 3, 682–693 (2017).
[Crossref]

Behner, F.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Bellagamba, L.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Benammar, A.

A. Allag, R. Drai, A. Benammar, and T. Boutkedjirt, “X-rays tomographic reconstruction images using proximal methods based on l1 norm and tv regularization,” in Procedia Computer Science, vol. 127 (ICDS2017, 2018), pp. 236–245.
[Crossref]

Bernard, D.

L. Salvo, M. Suery, A. Marmottant, N. Limodin, and D. Bernard, “3d imaging in material science: Application of x-ray tomography,” Comptes Rendus Physique 11, 641–649 (2010).
[Crossref]

Bettuzzi, M.

M. P. Morigi, F. Casali, M. Bettuzzi, R. Brancaccio, and V. D’ Errico, “Application of x-ray computed tomography to cultural heritage diagnostics,” Appl. Phys. A 100, 653–661 (2010).
[Crossref]

Bleichrodt, F.

D. Kazantsev, F. Bleichrodt, T. van Leeuwen, A. Kaestner, P. Withers, K. J. Batenburg, and P. D. Lee, “A novel tomographic reconstruction method based on the robust student’s t function for suppressing data outliers,” IEEE Trans. Comput. Imaging 3, 682–693 (2017).
[Crossref]

W. van Aarle, W. Jan Palenstijn, J. Cant, E. Janssens, F. Bleichrodt, A. Dabravolski, J. De Beenhouwer, K. Batenburg, and J. Sijbers, “Fast and flexible x-ray tomography using the astra toolbox,” Opt. Express 24, 25129–25147 (2016).
[Crossref] [PubMed]

Boas, F.

F. Boas and D. Fleischmann, “Ct artifacts: causes and reduction techniques,” Imaging Med. 4, 229–240 (2012).
[Crossref]

Boudier, T.

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[Crossref] [PubMed]

Osanai, M.

I. Mori, Y. Machida, M. Osanai, and K. Iinuma, “Photon starvation artifacts of x-ray ct: Their true cause and a solution,” Radiol. Phys. Technol. 6, 130–141 (2012).
[Crossref] [PubMed]

Otsu, N.

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
[Crossref]

Ouadah, S.

J. W. Sab, T. Eap, W. Cap, S. Ouadah, M. Jacobson, and J. H. Siewerdsen, “Task-driven orbit design and implementation on a robotic c-arm system for cone-beam ct,” in Proceedings of SPIE–the International Society for Optical Engineering, vol. 10132 (2017), pp. 10132–10137.

Pack, J. D.

J. D. Pack, F. Noo, and R. Clackdoyle, “Cone-beam reconstruction using the backprojection of locally filtered projections,” IEEE Trans. Med. Imaging 24, 70–85 (2005).
[Crossref] [PubMed]

Papapavasileiou, P.

P. Papapavasileiou, G. Flux, M. A. Flower, and M. Guy, “Note: Automated ct marker segmentation for image registration in radionuclide therapy,” Phys. Med. Biol. 46, N269–N279 (2001).
[Crossref]

Pitocco, F.

C. Liguori, G. Frauenfelder, C. Massaroni, P. Saccomandi, F. Giurazza, F. Pitocco, R. Marano, and E. Schena, “Emerging clinical applications of computed tomography,” Med. Devices: Evid. Res. 8, 265–278 (2015).

Rettenberger, S.

G. Herl, S. Rettenberger, J. Hiller, and T. Sauer, “Metal artifact reduction by fusion of ct scans from different positions using the unfiltered backprojection,” in Proceedings of 8th Conference on Industrial Computed Tomography, (2018), pp. 1–8.

Rister, B.

B. Rister, M. A. Horowitz, and D. L. Rubin, “Volumetric image registration from invariant keypoints,” IEEE Trans. Image Process. 26, 4900–4910 (2017).
[Crossref] [PubMed]

Rubin, D. L.

B. Rister, M. A. Horowitz, and D. L. Rubin, “Volumetric image registration from invariant keypoints,” IEEE Trans. Image Process. 26, 4900–4910 (2017).
[Crossref] [PubMed]

Sab, J. W.

J. W. Sab, T. Eap, W. Cap, S. Ouadah, M. Jacobson, and J. H. Siewerdsen, “Task-driven orbit design and implementation on a robotic c-arm system for cone-beam ct,” in Proceedings of SPIE–the International Society for Optical Engineering, vol. 10132 (2017), pp. 10132–10137.

Saccomandi, P.

C. Liguori, G. Frauenfelder, C. Massaroni, P. Saccomandi, F. Giurazza, F. Pitocco, R. Marano, and E. Schena, “Emerging clinical applications of computed tomography,” Med. Devices: Evid. Res. 8, 265–278 (2015).

Salvo, L.

L. Salvo, M. Suery, A. Marmottant, N. Limodin, and D. Bernard, “3d imaging in material science: Application of x-ray tomography,” Comptes Rendus Physique 11, 641–649 (2010).
[Crossref]

Sauer, T.

G. Herl, S. Rettenberger, J. Hiller, and T. Sauer, “Metal artifact reduction by fusion of ct scans from different positions using the unfiltered backprojection,” in Proceedings of 8th Conference on Industrial Computed Tomography, (2018), pp. 1–8.

Sawall, S.

J. Maier, S. Sawall, M. Knaup, and M. Kachelrieß, “Deep scatter estimation (dse): Accurate real-time scatter estimation for x-ray ct using a deep convolutional neural network,” J. Nondestruct. Eval. 37, 57 (2018).
[Crossref]

J. Maier, C. Leinweber, S. Sawall, H. Stoschus, F. Ballach, T. Müller, M. Hammer, R. Christoph, and M. Kachelrieß, “Simulation-based artifact correction (sbac) for metrological computed tomography,” Meas. Sci. Technol. 28, 065011 (2017).
[Crossref]

S. Schüller, S. Sawall, K. Stannigel, M. Hulsbusch, J. Ulrici, E. Hell, and M. Kachelrieß, “Segmentation-free empirical beam hardening correction for ct,” Med. physics 42, 794–803 (2015).
[Crossref]

Schena, E.

C. Liguori, G. Frauenfelder, C. Massaroni, P. Saccomandi, F. Giurazza, F. Pitocco, R. Marano, and E. Schena, “Emerging clinical applications of computed tomography,” Med. Devices: Evid. Res. 8, 265–278 (2015).

Schrapp, M.

A. Fischer, T. Lasser, M. Schrapp, J. Stephan, and P. Noël, “Object specific trajectory optimization for industrial x-ray computed tomography,” Sci. Reports 6, 19135 (2016).
[Crossref]

Schüller, S.

S. Schüller, S. Sawall, K. Stannigel, M. Hulsbusch, J. Ulrici, E. Hell, and M. Kachelrieß, “Segmentation-free empirical beam hardening correction for ct,” Med. physics 42, 794–803 (2015).
[Crossref]

Sedat, J.

F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on gpus,” Comput. Methods Programs Biomed. 98, 261–270 (2009).
[Crossref] [PubMed]

Siewerdsen, J. H.

J. W. Sab, T. Eap, W. Cap, S. Ouadah, M. Jacobson, and J. H. Siewerdsen, “Task-driven orbit design and implementation on a robotic c-arm system for cone-beam ct,” in Proceedings of SPIE–the International Society for Optical Engineering, vol. 10132 (2017), pp. 10132–10137.

Sijbers, J.

Spital, R. D.

P. M. Joseph and R. D. Spital, “The effects of scatter in x-ray computed tomography,” Med. Phys. 9, 464–472 (1982).
[Crossref] [PubMed]

Stannigel, K.

S. Schüller, S. Sawall, K. Stannigel, M. Hulsbusch, J. Ulrici, E. Hell, and M. Kachelrieß, “Segmentation-free empirical beam hardening correction for ct,” Med. physics 42, 794–803 (2015).
[Crossref]

Stephan, J.

A. Fischer, T. Lasser, M. Schrapp, J. Stephan, and P. Noël, “Object specific trajectory optimization for industrial x-ray computed tomography,” Sci. Reports 6, 19135 (2016).
[Crossref]

Stopp, F.

F. Stopp, M Kap, C. Winne, B Map, D. Jab, and E. Keeve, “Orbit - open x-ray scanner for image-guided interventional surgery - development of concept,” in Proceedings of Emerging Technologies for Medical Diagnosis and Therapy, (2011).

Stoschus, H.

J. Maier, C. Leinweber, S. Sawall, H. Stoschus, F. Ballach, T. Müller, M. Hammer, R. Christoph, and M. Kachelrieß, “Simulation-based artifact correction (sbac) for metrological computed tomography,” Meas. Sci. Technol. 28, 065011 (2017).
[Crossref]

Suery, M.

L. Salvo, M. Suery, A. Marmottant, N. Limodin, and D. Bernard, “3d imaging in material science: Application of x-ray tomography,” Comptes Rendus Physique 11, 641–649 (2010).
[Crossref]

Ulrici, J.

S. Schüller, S. Sawall, K. Stannigel, M. Hulsbusch, J. Ulrici, E. Hell, and M. Kachelrieß, “Segmentation-free empirical beam hardening correction for ct,” Med. physics 42, 794–803 (2015).
[Crossref]

van Aarle, W.

Van de Casteele, E.

E. Van de Casteele, “Model-based approach for beam hardening correction and resolution measurements in microtomography,” Ph.D. thesis, Antwerpen University (2004).

van Leeuwen, T.

D. Kazantsev, F. Bleichrodt, T. van Leeuwen, A. Kaestner, P. Withers, K. J. Batenburg, and P. D. Lee, “A novel tomographic reconstruction method based on the robust student’s t function for suppressing data outliers,” IEEE Trans. Comput. Imaging 3, 682–693 (2017).
[Crossref]

Vesselle, H.

D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellyn, and W. Eubank, “Nonrigid multimodality image registration,” in Proceedings of SPIE - The International Society for Optical Engineering, vol. 4322 (2001).

Wang, J.

X. Duan, J. Wang, L. Yu, S. Leng, and C. H McCollough, “Ct scanner x-ray spectrum estimation from transmission measurements,” Med. Phys. 38, 993–997 (2011).
[Crossref] [PubMed]

Wessel, K.

M. Käseberg, S. Melnik, K. Wessel, and E. Keeve, “Multi-gpu sart for arbitrary imaging trajectories,” in In Proceedings of The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, (2015), pp. 308–311.

Whiting, B. R

G. Lasio, B. R Whiting, and J. F Williamson, “Statistical reconstruction for x-ray computed tomography using energy-integrating detectors,” Phys. Med. Biol. 52, 2247–2266 (2007).
[Crossref] [PubMed]

Williamson, J. F

G. Lasio, B. R Whiting, and J. F Williamson, “Statistical reconstruction for x-ray computed tomography using energy-integrating detectors,” Phys. Med. Biol. 52, 2247–2266 (2007).
[Crossref] [PubMed]

Winne, C.

F. Stopp, M Kap, C. Winne, B Map, D. Jab, and E. Keeve, “Orbit - open x-ray scanner for image-guided interventional surgery - development of concept,” in Proceedings of Emerging Technologies for Medical Diagnosis and Therapy, (2011).

Withers, P.

D. Kazantsev, F. Bleichrodt, T. van Leeuwen, A. Kaestner, P. Withers, K. J. Batenburg, and P. D. Lee, “A novel tomographic reconstruction method based on the robust student’s t function for suppressing data outliers,” IEEE Trans. Comput. Imaging 3, 682–693 (2017).
[Crossref]

Xu, F.

F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on gpus,” Comput. Methods Programs Biomed. 98, 261–270 (2009).
[Crossref] [PubMed]

Xu, S.

S. Xu and H. Dang, “Deep residual learning enabled metal artifact reduction in ct,” in Proc. SPIE - Medical Imaging 2018: Physics of Medical Imaging, vol. 10573 (2018), pp. 132–137.

Xu, W.

F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on gpus,” Comput. Methods Programs Biomed. 98, 261–270 (2009).
[Crossref] [PubMed]

Yoo, T. S.

T. S. Yoo, M. J. Ackerman, and W. E. Lorensen, “Engineering and algorithm design for an image processing API: A technical report on itk – the insight toolkit,” in Proc. of Medicine Meets Virtual Reality, (2002), pp. 586–592.

Yu, L.

X. Duan, J. Wang, L. Yu, S. Leng, and C. H McCollough, “Ct scanner x-ray spectrum estimation from transmission measurements,” Med. Phys. 38, 993–997 (2011).
[Crossref] [PubMed]

Zhuang, T.

T. Zhuang, B. E. Nett, and G.-H. Chen, “Image reconstruction via filtered backprojection for cone-beam data from two orthogonal circles using an equal weighting scheme,” in In Proceedings of The 8th International Meeting on Fully Three-dimensional Image Reconstruction in Radiology and Nuclear Medicine, (2005), pp. 198–202.

Zschiesche, D.

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Appl. Phys. A (1)

M. P. Morigi, F. Casali, M. Bettuzzi, R. Brancaccio, and V. D’ Errico, “Application of x-ray computed tomography to cultural heritage diagnostics,” Appl. Phys. A 100, 653–661 (2010).
[Crossref]

Biol. cell / under auspices Eur. Cell Biol. Organ. (1)

C. Messaoudi, N. Garreau de Loubresse, T. Boudier, P. Dupuis-Williams, and S. Marco, “Multiple-axis tomography: Applications to basal bodies from paramecium tetraurelia,” Biol. cell / under auspices Eur. Cell Biol. Organ. 98, 415–425 (2006).
[Crossref]

Case Stud. Nondestruct. Test. Eval. (1)

A. Du Plessis, S. Gerhard le Roux, and A. Guelpa, “Comparison of medical and industrial x-ray computed tomography for non-destructive testing,” Case Stud. Nondestruct. Test. Eval. 6, 17–25 (2016).
[Crossref]

Comptes Rendus Physique (1)

L. Salvo, M. Suery, A. Marmottant, N. Limodin, and D. Bernard, “3d imaging in material science: Application of x-ray tomography,” Comptes Rendus Physique 11, 641–649 (2010).
[Crossref]

Comput. Methods Programs Biomed. (1)

F. Xu, W. Xu, M. Jones, B. Keszthelyi, J. Sedat, D. Agard, and K. Mueller, “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on gpus,” Comput. Methods Programs Biomed. 98, 261–270 (2009).
[Crossref] [PubMed]

IEEE Trans. Comput. Imaging (1)

D. Kazantsev, F. Bleichrodt, T. van Leeuwen, A. Kaestner, P. Withers, K. J. Batenburg, and P. D. Lee, “A novel tomographic reconstruction method based on the robust student’s t function for suppressing data outliers,” IEEE Trans. Comput. Imaging 3, 682–693 (2017).
[Crossref]

IEEE Trans. Image Process. (1)

B. Rister, M. A. Horowitz, and D. L. Rubin, “Volumetric image registration from invariant keypoints,” IEEE Trans. Image Process. 26, 4900–4910 (2017).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

J. D. Pack, F. Noo, and R. Clackdoyle, “Cone-beam reconstruction using the backprojection of locally filtered projections,” IEEE Trans. Med. Imaging 24, 70–85 (2005).
[Crossref] [PubMed]

J. A. Fessler, “Penalized weighted least-squares image reconstruction for positron emission tomography,” IEEE Trans. Med. Imaging 13, 290–300 (1994).
[Crossref] [PubMed]

IEEE Trans. Syst. Man. Cybern. (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62–66 (1979).
[Crossref]

Imaging Med. (1)

F. Boas and D. Fleischmann, “Ct artifacts: causes and reduction techniques,” Imaging Med. 4, 229–240 (2012).
[Crossref]

J. Nondestruct. Eval. (1)

J. Maier, S. Sawall, M. Knaup, and M. Kachelrieß, “Deep scatter estimation (dse): Accurate real-time scatter estimation for x-ray ct using a deep convolutional neural network,” J. Nondestruct. Eval. 37, 57 (2018).
[Crossref]

J. Opt. Soc. Am. A (1)

J. Struct. Biol. (1)

D. N. Mastronarde, “Dual-axis tomography: An approach with alignment methods that preserve resolution,” J. Struct. Biol. 120, 343–352 (1998).
[Crossref] [PubMed]

J. X-Ray Sci. Technol. (1)

T. Kano and M. Koseki, “A new metal artifact reduction algorithm based on a deteriorated ct image,” J. X-Ray Sci. Technol. 24, 901–912 (2016).
[Crossref]

Meas. Sci. Technol. (1)

J. Maier, C. Leinweber, S. Sawall, H. Stoschus, F. Ballach, T. Müller, M. Hammer, R. Christoph, and M. Kachelrieß, “Simulation-based artifact correction (sbac) for metrological computed tomography,” Meas. Sci. Technol. 28, 065011 (2017).
[Crossref]

Med. Devices: Evid. Res. (1)

C. Liguori, G. Frauenfelder, C. Massaroni, P. Saccomandi, F. Giurazza, F. Pitocco, R. Marano, and E. Schena, “Emerging clinical applications of computed tomography,” Med. Devices: Evid. Res. 8, 265–278 (2015).

Med. Phys. (2)

P. M. Joseph and R. D. Spital, “The effects of scatter in x-ray computed tomography,” Med. Phys. 9, 464–472 (1982).
[Crossref] [PubMed]

X. Duan, J. Wang, L. Yu, S. Leng, and C. H McCollough, “Ct scanner x-ray spectrum estimation from transmission measurements,” Med. Phys. 38, 993–997 (2011).
[Crossref] [PubMed]

Med. physics (1)

S. Schüller, S. Sawall, K. Stannigel, M. Hulsbusch, J. Ulrici, E. Hell, and M. Kachelrieß, “Segmentation-free empirical beam hardening correction for ct,” Med. physics 42, 794–803 (2015).
[Crossref]

Microsc Anal (Am Ed) (1)

B. Metscher, “Biological applications of x-ray microtomography: imaging microanatomy, molecular expression and organismal diversity,” Microsc Anal (Am Ed) 27, 13–16 (2013).

Nucl. Instruments Methods (1)

S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, and D. Zschiesche, “Geant4 - a simulation toolkit,” Nucl. Instruments Methods A506, 250–303 (2003).
[Crossref]

Opt. Express (1)

Phys. Med. Biol. (2)

G. Lasio, B. R Whiting, and J. F Williamson, “Statistical reconstruction for x-ray computed tomography using energy-integrating detectors,” Phys. Med. Biol. 52, 2247–2266 (2007).
[Crossref] [PubMed]

P. Papapavasileiou, G. Flux, M. A. Flower, and M. Guy, “Note: Automated ct marker segmentation for image registration in radionuclide therapy,” Phys. Med. Biol. 46, N269–N279 (2001).
[Crossref]

Radiol. Phys. Technol. (1)

I. Mori, Y. Machida, M. Osanai, and K. Iinuma, “Photon starvation artifacts of x-ray ct: Their true cause and a solution,” Radiol. Phys. Technol. 6, 130–141 (2012).
[Crossref] [PubMed]

Sci. Reports (1)

A. Fischer, T. Lasser, M. Schrapp, J. Stephan, and P. Noël, “Object specific trajectory optimization for industrial x-ray computed tomography,” Sci. Reports 6, 19135 (2016).
[Crossref]

Trans. Engin. Comput. Technol. (1)

J. Flusser, “Moment invariants in image analysis,” Trans. Engin. Comput. Technol. 1, 3721–3726 (2011).

Other (16)

R. Brent, Algorithms For Minimization Without Derivatives, vol. 19 (Prentice Hall,Englewood Cliffs2002).

S. Coban and et al., “Flex-ray lab: A highly flexible scanner for explorative tomography,” In preparation (2018). Computational Imaging Group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.

A. A. Goshtasby, 2-D and 3-D Image Registration For Medical, Remote Sensing, and Industrial Applications (Wiley, 2005).

D. Mattes, D. R. Haynor, H. Vesselle, T. K. Lewellyn, and W. Eubank, “Nonrigid multimodality image registration,” in Proceedings of SPIE - The International Society for Optical Engineering, vol. 4322 (2001).

T. S. Yoo, M. J. Ackerman, and W. E. Lorensen, “Engineering and algorithm design for an image processing API: A technical report on itk – the insight toolkit,” in Proc. of Medicine Meets Virtual Reality, (2002), pp. 586–592.

“Flexbox - high level tomographic reconstruction toolbox„,” https://github.com/cicwi/flexbox .

T. Zhuang, B. E. Nett, and G.-H. Chen, “Image reconstruction via filtered backprojection for cone-beam data from two orthogonal circles using an equal weighting scheme,” in In Proceedings of The 8th International Meeting on Fully Three-dimensional Image Reconstruction in Radiology and Nuclear Medicine, (2005), pp. 198–202.

M. Käseberg, S. Melnik, K. Wessel, and E. Keeve, “Multi-gpu sart for arbitrary imaging trajectories,” in In Proceedings of The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, (2015), pp. 308–311.

T. Kano and M. Koseki, “Development of a multi-axis x-ray ct for highly accurate inspection of electronic devices,” in Proceedings of 7th Conference on Industrial Computed Tomography, (2017), pp. 81–82.

G. Herl, S. Rettenberger, J. Hiller, and T. Sauer, “Metal artifact reduction by fusion of ct scans from different positions using the unfiltered backprojection,” in Proceedings of 8th Conference on Industrial Computed Tomography, (2018), pp. 1–8.

F. Stopp, M Kap, C. Winne, B Map, D. Jab, and E. Keeve, “Orbit - open x-ray scanner for image-guided interventional surgery - development of concept,” in Proceedings of Emerging Technologies for Medical Diagnosis and Therapy, (2011).

J. W. Sab, T. Eap, W. Cap, S. Ouadah, M. Jacobson, and J. H. Siewerdsen, “Task-driven orbit design and implementation on a robotic c-arm system for cone-beam ct,” in Proceedings of SPIE–the International Society for Optical Engineering, vol. 10132 (2017), pp. 10132–10137.

R. Clack and M. Defrise, “A filtered-backprojection cone-beam algorithm for general orbits with implementation details for the two-orthogonal-circles orbit,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, vol. 3 (IEEE1993), pp. 1590–1594.
[Crossref]

A. Allag, R. Drai, A. Benammar, and T. Boutkedjirt, “X-rays tomographic reconstruction images using proximal methods based on l1 norm and tv regularization,” in Procedia Computer Science, vol. 127 (ICDS2017, 2018), pp. 236–245.
[Crossref]

S. Xu and H. Dang, “Deep residual learning enabled metal artifact reduction in ct,” in Proc. SPIE - Medical Imaging 2018: Physics of Medical Imaging, vol. 10573 (2018), pp. 132–137.

E. Van de Casteele, “Model-based approach for beam hardening correction and resolution measurements in microtomography,” Ph.D. thesis, Antwerpen University (2004).

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Figures (5)

Fig. 1
Fig. 1 Flowchart of multi-orientation data acquisition and reconstruction approach.
Fig. 2
Fig. 2 Tomographic Reconstruction. (A) Rotation around an object with high density regions: zone adjacent to high-density regions is poorly reconstructed. (B) Addition of ’redundant’ data: reconstruction of the over-attenuated zone is now based on more observations
Fig. 3
Fig. 3 Simulated examples: FDK-based reconstructions of synthetic data. (A) phantom (horizontal and vertical slice): low density Nylon cylinder (∅18 mm) with a aluminum block (12 × 4 × 12 mm) in the center and two high density iron wires (∅1.2 mm); (B) simulation spectrum; (C) FDK reconstruction of monochromatic simulation (40keV photons); (D)–(F) FDK reconstructions of polychromatic simulations for three orthogonal rotation axes. The colorbar is given for the reconstructed linear attenuation coefficient in mm−1.
Fig. 4
Fig. 4 Comparison of the reconstruction methods: FDK fusion, SIRT, PWLS, Student’s-T. Top two rows - single rotation axis, second row - two rotation axes, third row - three rotation axes. The colorbar is given for the reconstructed linear attenuation coefficient in mm−1.
Fig. 5
Fig. 5 Comparison of reconstruction methods applied to experimental data. Columns (left to right) correspond to FDK reconstructions of data acquired for two rotation axes and a PWLS reconstruction applied to the combined data. The top two rows show slices and maximum projections of a reconstructed PCB. The middle two rows show reconstructions of a light bulb. The bottom two rows show two orthogonal slices of a reconstructed alkaline battery. Colorbars are given for the reconstructed linear attenuation coefficient in mm−1.

Tables (5)

Tables Icon

Algorithm 1 Volume registration

Tables Icon

Algorithm 2 Unregularized PWLS

Tables Icon

Algorithm 3 Multi-orientation PWLS

Tables Icon

Algorithm 4 Multi-orientation Student’s-t

Tables Icon

Table 1 Experimental settings

Equations (12)

Equations on this page are rendered with MathJax. Learn more.

p ( x ¯ , θ ¯ ) = μ ( x ¯ + t θ ¯ ) d t ,
p ( x ¯ , θ ¯ ) = log ( I out I in )
p ( x ¯ , θ ¯ ) = μ ( T x ¯ + s ¯ + t T θ ¯ ) d t ,
μ = arg min μ R μ p 2 2
μ = arg min μ ( R μ p ) T W 1 ( R μ p ) ,
μ = arg min μ f ( R μ p ) ,
f ( x ) = M log ( π σ ^ ) + log [ 1 + ( x σ ^ ) 2 ] ,
f ( x ) = x σ ^ 2 + x 2
σ ^ = arg min σ ( M log ( π σ ) + log [ 1 + ( x σ ) 2 ] ) ,
m i j k = ( x x 0 ) i ( y y 0 ) j ( z z 0 ) k μ ( x , y , z ) d x d y d z ,
C = [ m ^ 200 m ^ 110 m ^ 101 m ^ 110 m ^ 020 m ^ 011 m ^ 101 m ^ 011 m ^ 002 ]
μ 0 T k μ q 2 2 ,

Metrics