Abstract

We report a new type of moiré pattern caused by inhomogeneous detector sensitivity in computed tomography. Defects in one or a few detector bins or miscalibrated detectors induce well-known ring artifacts. When detector sensitivity is not homogenous over all detector bins, these ring artifacts occur everywhere as distributed rings in reconstructed images and may cause a moiré pattern when combined with insufficient view sampling, which induces a noise-like pattern or a subtle texture in the reconstructed images. Complete correction of the inhomogeneity in detectors can remove the pattern and improve image quality. This paper describes several properties of moiré patterns caused by detector sensitivity inhomogeneity.

© 2017 Optical Society of America

Full Article  |  PDF Article
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References

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2017 (1)

T. D. Pham, Y. Watanabe, M. Higuchi, and H. Suzuki, “Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography,” Sci. Rep. 7, 43209 (2017).
[PubMed]

2016 (2)

2015 (3)

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

K. Mala, V. Sadasivam, and S. Alagappan, “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification,” Appl. Soft Comput. 32, 80–86 (2015).

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

2014 (4)

2013 (2)

K. Taguchi and J. S. Iwanczyk, “Vision 20/20: Single photon counting x-ray detectors in medical imaging,” Med. Phys. 40(10), 100901 (2013).
[PubMed]

V. Saveljev and S. K. Kim, “Theoretical estimation of moiré effect using spectral trajectories,” Opt. Express 21(2), 1693–1712 (2013).
[PubMed]

2011 (1)

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
[PubMed]

2008 (2)

O. S. Al-Kadi and D. Watson, “Texture analysis of aggressive and nonaggressive lung tumor CE CT images,” IEEE Trans. Biomed. Eng. 55, 1822–1830 (2008).

S. Zhou, Y. Fu, X. Tang, S. Hu, W. Chen, and Y. Yang, “Fourier-based analysis of moiré fringe patterns of superposed gratings in alignment of nanolithography,” Opt. Express 16(11), 7869–7880 (2008).
[PubMed]

2006 (3)

A. L. Kwan, J. A. Seibert, and J. M. Boone, “An improved method for flat-field correction of flat panel x-ray detector,” Med. Phys. 33(2), 391–393 (2006).
[PubMed]

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

M. Boin and A. Haibel, “Compensation of ring artefacts in synchrotron tomographic images,” Opt. Express 14(25), 12071–12075 (2006).
[PubMed]

2004 (2)

J. Sijbers and A. Postnov, “Reduction of ring artefacts in high resolution micro-CT reconstructions,” Phys. Med. Biol. 49(247–N), 253 (2004).

J. F. Barrett and N. Keat, “Artifacts in CT: recognition and avoidance,” Radiographics 24(6), 1679–1691 (2004).
[PubMed]

1999 (1)

B. De Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, “Metal streak artifacts in X-ray computed tomography: a simulation study,” IEEE Trans. Nucl. Sci. 46, 691–696 (1999).

1997 (1)

G. R. Davis and J. C. Elliott, “X-ray microtomography scanner using time-delay integration for elimination of ring artefacts in the reconstructed image,” Nucl. Instrum. Meth. A 394, 157–162 (1997).

1995 (1)

M. Ito, M. Ohki, K. Hayashi, M. Yamada, M. Uetani, and T. Nakamura, “Trabecular texture analysis of CT images in the relationship with spinal fracture,” Radiology 194(1), 55–59 (1995).
[PubMed]

Alagappan, S.

K. Mala, V. Sadasivam, and S. Alagappan, “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification,” Appl. Soft Comput. 32, 80–86 (2015).

Alcock, S.

Al-Kadi, O. S.

O. S. Al-Kadi and D. Watson, “Texture analysis of aggressive and nonaggressive lung tumor CE CT images,” IEEE Trans. Biomed. Eng. 55, 1822–1830 (2008).

Baek, J.

Barrett, J. F.

J. F. Barrett and N. Keat, “Artifacts in CT: recognition and avoidance,” Radiographics 24(6), 1679–1691 (2004).
[PubMed]

Barthur, A.

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
[PubMed]

Berujon, S.

Boin, M.

Boone, J. M.

A. L. Kwan, J. A. Seibert, and J. M. Boone, “An improved method for flat-field correction of flat panel x-ray detector,” Med. Phys. 33(2), 391–393 (2006).
[PubMed]

Chang, C. H.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Chen, S. J.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Chen, W.

Ching, Y. T.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Chu, B. F.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

David, C.

Davis, G. R.

G. R. Davis and J. C. Elliott, “X-ray microtomography scanner using time-delay integration for elimination of ring artefacts in the reconstructed image,” Nucl. Instrum. Meth. A 394, 157–162 (1997).

De Man, B.

B. De Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, “Metal streak artifacts in X-ray computed tomography: a simulation study,” IEEE Trans. Nucl. Sci. 46, 691–696 (1999).

Dedelow, J. A.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Dupont, P.

B. De Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, “Metal streak artifacts in X-ray computed tomography: a simulation study,” IEEE Trans. Nucl. Sci. 46, 691–696 (1999).

Elliott, J. C.

G. R. Davis and J. C. Elliott, “X-ray microtomography scanner using time-delay integration for elimination of ring artefacts in the reconstructed image,” Nucl. Instrum. Meth. A 394, 157–162 (1997).

Erdal, B. S.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Flechsig, U.

Flood, T. A.

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

Fu, Y.

Ghosh, S.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Haibel, A.

Hayashi, K.

M. Ito, M. Ohki, K. Hayashi, M. Yamada, M. Uetani, and T. Nakamura, “Trabecular texture analysis of CT images in the relationship with spinal fracture,” Radiology 194(1), 55–59 (1995).
[PubMed]

Higuchi, M.

T. D. Pham, Y. Watanabe, M. Higuchi, and H. Suzuki, “Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography,” Sci. Rep. 7, 43209 (2017).
[PubMed]

Hodgdon, T.

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

Hu, S.

Hwang, D.

Ito, M.

M. Ito, M. Ohki, K. Hayashi, M. Yamada, M. Uetani, and T. Nakamura, “Trabecular texture analysis of CT images in the relationship with spinal fracture,” Radiology 194(1), 55–59 (1995).
[PubMed]

Iwanczyk, J. S.

K. Taguchi and J. S. Iwanczyk, “Vision 20/20: Single photon counting x-ray detectors in medical imaging,” Med. Phys. 40(10), 100901 (2013).
[PubMed]

Jianglong, W.

Junfei, L.

Kambadakone, A.

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
[PubMed]

Kameshima, T.

Katayama, T.

Kayser, Y.

Keat, N.

J. F. Barrett and N. Keat, “Artifacts in CT: recognition and avoidance,” Radiographics 24(6), 1679–1691 (2004).
[PubMed]

Kim, H. W.

Kim, S. K.

Kim, Y.

King, M.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Kv, R.

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
[PubMed]

Kwan, A. L.

A. L. Kwan, J. A. Seibert, and J. M. Boone, “An improved method for flat-field correction of flat panel x-ray detector,” Med. Phys. 33(2), 391–393 (2006).
[PubMed]

Lamb, L.

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

Lee, B.

Lee, H.

Lee, J. H.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Lee, W. J.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Lin, C. Y.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Mala, K.

K. Mala, V. Sadasivam, and S. Alagappan, “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification,” Appl. Soft Comput. 32, 80–86 (2015).

Marchal, G.

B. De Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, “Metal streak artifacts in X-ray computed tomography: a simulation study,” IEEE Trans. Nucl. Sci. 46, 691–696 (1999).

McInnes, M. D.

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

Miles, K. A.

K. A. Miles, “How to use CT texture analysis for prognostication of non-small cell lung cancer,” Cancer Imaging 16, 10 (2016).
[PubMed]

Nakamura, T.

M. Ito, M. Ohki, K. Hayashi, M. Yamada, M. Uetani, and T. Nakamura, “Trabecular texture analysis of CT images in the relationship with spinal fracture,” Radiology 194(1), 55–59 (1995).
[PubMed]

Narayanan, N.

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
[PubMed]

Nuyts, J.

B. De Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, “Metal streak artifacts in X-ray computed tomography: a simulation study,” IEEE Trans. Nucl. Sci. 46, 691–696 (1999).

Ohashi, H.

Ohki, M.

M. Ito, M. Ohki, K. Hayashi, M. Yamada, M. Uetani, and T. Nakamura, “Trabecular texture analysis of CT images in the relationship with spinal fracture,” Radiology 194(1), 55–59 (1995).
[PubMed]

Otterson, G. A.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Ozkan, E.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Pham, T. D.

T. D. Pham, Y. Watanabe, M. Higuchi, and H. Suzuki, “Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography,” Sci. Rep. 7, 43209 (2017).
[PubMed]

Postnov, A.

J. Sijbers and A. Postnov, “Reduction of ring artefacts in high resolution micro-CT reconstructions,” Phys. Med. Biol. 49(247–N), 253 (2004).

Qinwei, M.

Rutishauser, S.

Sadasivam, V.

K. Mala, V. Sadasivam, and S. Alagappan, “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification,” Appl. Soft Comput. 32, 80–86 (2015).

Saveljev, V.

Sawhney, K.

Schieda, N.

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

Seibert, J. A.

A. L. Kwan, J. A. Seibert, and J. M. Boone, “An improved method for flat-field correction of flat panel x-ray detector,” Med. Phys. 33(2), 391–393 (2006).
[PubMed]

Shaopeng, M.

Shetty, C. M.

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
[PubMed]

Shilo, K.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Sijbers, J.

J. Sijbers and A. Postnov, “Reduction of ring artefacts in high resolution micro-CT reconstructions,” Phys. Med. Biol. 49(247–N), 253 (2004).

Suetens, P.

B. De Man, J. Nuyts, P. Dupont, G. Marchal, and P. Suetens, “Metal streak artifacts in X-ray computed tomography: a simulation study,” IEEE Trans. Nucl. Sci. 46, 691–696 (1999).

Suzuki, H.

T. D. Pham, Y. Watanabe, M. Higuchi, and H. Suzuki, “Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography,” Sci. Rep. 7, 43209 (2017).
[PubMed]

Taguchi, K.

K. Taguchi and J. S. Iwanczyk, “Vision 20/20: Single photon counting x-ray detectors in medical imaging,” Med. Phys. 40(10), 100901 (2013).
[PubMed]

Tang, X.

Thornhill, R. E.

T. Hodgdon, M. D. McInnes, N. Schieda, T. A. Flood, L. Lamb, and R. E. Thornhill, “Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?” Radiology 276(3), 787–796 (2015).
[PubMed]

Tsai, C. H.

C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
[PubMed]

Uetani, M.

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[PubMed]

Wang, H.

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T. D. Pham, Y. Watanabe, M. Higuchi, and H. Suzuki, “Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography,” Sci. Rep. 7, 43209 (2017).
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E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
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Yabashi, M.

Yamada, M.

M. Ito, M. Ohki, K. Hayashi, M. Yamada, M. Uetani, and T. Nakamura, “Trabecular texture analysis of CT images in the relationship with spinal fracture,” Radiology 194(1), 55–59 (1995).
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Yang, X.

Yang, Y.

Yildiz, V. O.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
[PubMed]

Youqi, Z.

Zhao, W.

E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
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Zhou, S.

AJR Am. J. Roentgenol. (2)

C. M. Shetty, A. Barthur, A. Kambadakone, N. Narayanan, and R. Kv, “Computed radiography image artifacts revisited,” AJR Am. J. Roentgenol. 196(1), W37–W47 (2011).
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E. Ozkan, A. West, J. A. Dedelow, B. F. Chu, W. Zhao, V. O. Yildiz, G. A. Otterson, K. Shilo, S. Ghosh, M. King, R. D. White, and B. S. Erdal, “CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung,” AJR Am. J. Roentgenol. 205(5), 1016–1025 (2015).
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K. Mala, V. Sadasivam, and S. Alagappan, “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification,” Appl. Soft Comput. 32, 80–86 (2015).

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K. A. Miles, “How to use CT texture analysis for prognostication of non-small cell lung cancer,” Cancer Imaging 16, 10 (2016).
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IEEE Trans. Biomed. Eng. (1)

O. S. Al-Kadi and D. Watson, “Texture analysis of aggressive and nonaggressive lung tumor CE CT images,” IEEE Trans. Biomed. Eng. 55, 1822–1830 (2008).

IEEE Trans. Nucl. Sci. (1)

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C. Y. Lin, W. J. Lee, S. J. Chen, C. H. Tsai, J. H. Lee, C. H. Chang, and Y. T. Ching, “A study of grid artifacts formation and elimination in computed radiographic images,” J. Digit. Imaging 19(4), 351–361 (2006).
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K. Taguchi and J. S. Iwanczyk, “Vision 20/20: Single photon counting x-ray detectors in medical imaging,” Med. Phys. 40(10), 100901 (2013).
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M. Boin and A. Haibel, “Compensation of ring artefacts in synchrotron tomographic images,” Opt. Express 14(25), 12071–12075 (2006).
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J. Sijbers and A. Postnov, “Reduction of ring artefacts in high resolution micro-CT reconstructions,” Phys. Med. Biol. 49(247–N), 253 (2004).

Radiographics (1)

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Radiology (2)

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Sci. Rep. (1)

T. D. Pham, Y. Watanabe, M. Higuchi, and H. Suzuki, “Texture analysis and synthesis of malignant and benign mediastinal lymph nodes in patients with lung cancer on computed tomography,” Sci. Rep. 7, 43209 (2017).
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Figures (20)

Fig. 1
Fig. 1 Inhomogeneous detector sensitivity and resultant distributed ring artifacts. (a) and (c) are the sinogram and reconstructed image, respectively, for a uniform disk phantom when detector sensitivity is homogenous. (b) and (d) are the sinogram and reconstructed image, respectively, when detector sensitivity is inhomogeneous.
Fig. 2
Fig. 2 Severity of ring artifacts depending on their locations in the image.
Fig. 3
Fig. 3 Examples of moiré patterns. Overlays of line patterns (a) and ring patterns (b).
Fig. 4
Fig. 4 Example of the aliasing artifacts (streaks) due to an insufficient number of views. (a) is reconstructed from 1800 view projections, whereas (b) is reconstructed from 90 view projections. There are streaks in the peripheral area of the disk in (b). The squared patches were displayed with higher color contrast for better visualization of streaks.
Fig. 5
Fig. 5 Schematic for the interference structure between the distributed ring artifacts and aliasing artifacts due to an insufficient number of views.
Fig. 6
Fig. 6 Interference phenomena. (a) Only rings occur due to detector sensitivity variation when N is large. (b) rings and interference patterns occur when N is small. R denotes the radius for aliasing-free region (c) magnified image of the boxed area in (b) showing oscillating patterns in the azimuthal direction
Fig. 7
Fig. 7 Visualization of oscillating patterns in the azimuthal direction due to the interference between the detector sensitivity variation and the view-aliasing effect. (a) various oscillating patterns depending on r. (b)-(d) show 2-dimentional moiré patterns with different sets of Δθ and v.
Fig. 8
Fig. 8 Simulated projection data and its reconstruction. (a) is the projection data and (b) is the reconstructed image. (c) is the magnified image of the region of interest indicated by a box in (b).
Fig. 9
Fig. 9 (a) Sensitivity distribution for all detector elements and (b) simulated sensitivity values for each detector element along the detector location.
Fig. 10
Fig. 10 Reconstructed images with and without inhomogeneity in detector sensitivity. (a) is the reconstruction with homogeneous detector sensitivity showing uniform intensity inside the phantom. In contrast, (b) is the reconstruction with inhomogeneous detector sensitivity, which causes moiré patterns when combined with the aliasing effects due to an insufficient number of views. The number of projection views is the same for both images.
Fig. 11
Fig. 11 Downsampled reconstructed image with noise-like moiré patterns.
Fig. 12
Fig. 12 Reconstructed images when the object is placed at the center of rotation with (a) homogeneous and (b) inhomogeneous detector sensitivity.
Fig. 13
Fig. 13 Aliasing artifacts with different numbers of views: Left column: detector sensitivity is homogeneous and the number of views is (a) 4, (c) 8, (e) 18, and (g) 36. Right column: detector sensitivity is inhomogeneous and the number of views is (b) 4, (d) 8, (f) 18, and (h) 36.
Fig. 14
Fig. 14 Moiré patterns generated by actual sensitivity variation. (a) sensitivity values for all detector bins, (b) line images of detector sensitivity (a single backprojection), (c) actual CT reconstruction image, and (d) moiré patterns generated by backprojections of (b) over many angles.
Fig. 15
Fig. 15 The effect of the number of views on moiré patterns. Left: The number of views is 360. Right: the number of views is 3600.
Fig. 16
Fig. 16 Effect of ring artifact reduction on moiré patterns. The reconstructed images without correction (a) and its magnified view over the white boxed area (b). The corrected images with (c) MA method and (d) line-ratio method.
Fig. 17
Fig. 17 Noise-like moiré patterns observed in actual CT reconstructed images. (a) part of the reconstructed image without ring-reduction technique. Subtle underlying moiré patterns were transformed into noise-like dark and bright dots as indicated by arrows. (b) Reconstructed image with line-ratio method.
Fig. 18
Fig. 18 Noise-like moiré patterns observed in actual CT reconstructed images. (a) Reconstructed image without ring artifact reduction that contains noise-like moiré pattern, which degrades the overall image quality. (b) Reconstructed image with ring artifact correction, which produces a much less noisy image than (a).
Fig. 19
Fig. 19 Interfering moiré patterns observed in actual CT reconstructed images. (a) Original reconstructed image containing strong ring artifacts as indicated by arrows and oscillating patterns in the azimuthal direction as indicated by white ellipses. (b) Reconstructed image with ring artifact correction, which removes the oscillating patterns and strong rings. The number of views for (a) and (b) are the same.
Fig. 20
Fig. 20 Examples of interfering moiré patterns observed in actual CT reconstructed images. (a) Original reconstructed image without ring artifact correction. (b) Reconstructed image with ring artifact correction. Noise-like moiré patterns are not observable and the thin stripe indicated by an arrow is clearly noticeable in (b).

Tables (1)

Tables Icon

Table 1 Performance comparison of MA and line-ratio methods. ROI1 and ROI2 are the regions indicated by upper and bottom black boxes in Fig. 16(a), respectively.

Equations (11)

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g(s)=1+αcos(2πvs)
p(s,θ)=g(s)=1+αcos(2πvs)
f(x,y)= 0 π q(xcosθ+ysinθ,θ) dθ
f(x,y)=β 0 π cos(2πv(xcosθ+ysinθ)) dθ
f(x,y)=γ i=0 N1 cos(2πv(xcos(iΔθ)+ysin(iΔθ)) ).
N=πR v M ,
f( x 1 , y 1 )=f(rcos ϕ 1 ,rsin ϕ 1 ) =γ i=0 N1 cos(2πv(rcos ϕ 1 cos(iΔθ)+rsin ϕ 1 sin(iΔθ))) =γ i=0 N1 cos(2πvrcos ϕ 1 iΔθ))
f( x 2 , y 2 )=f(rcos ϕ 2 ,rsin ϕ 2 ) =γ i=0 N1 cos(2πv(rcos ϕ 2 cos(iΔθ)+rsin ϕ 2 sin(iΔθ))) =γ i=0 N1 cos(2πvrcos ϕ 2 iΔθ))
f( x 2 , y 2 )=γ i=0 N1 cos(2πv(rcos( ϕ 1 ΔϕiΔθ))
f( x 2 , y 2 )=γ i=0 N1 cos(2πvrcos( ϕ 1 (i+1)Δθ)) =γ i=0 N1 cos(2πvrcos( ϕ 1 i Δθ)) =γ i=0 N1 cos(2πvrcos( ϕ 1 iΔθ)) (NΔθ=π) =f( x 1 , y 1 )
f( x 2 , y 2 )=f(rcos(Δϕ),rsin(Δϕ))= i=0 N1 cos(2πvrcos(iΔθ+Δϕ))

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