ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Analysis of photon stochastic noise in X-ray microcopy

  • 1.

    National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China

  • 2.

    School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.03.011
  • Received Date: 10 July 2013
  • Accepted Date: 22 October 2013
  • Rev Recd Date: 22 October 2013
  • Publish Date: 30 March 2014
    Abstract

  • Abstract

    Stochastic photon noise is one of the most important factors which influence the quality of X-ray microscope images. The influence of the noise is described by the signal-to-noise ratio (SNR). Generally, increasing the number of photons yields better SNR, but inevitably with the side effect of a higher dose for the specimens. To find a good balance, one has to find a way to minimize the photon number while keeping an acceptable SNR, but conventional empirical Rose criterion is of little help, especially when applied to processed noised images. Here a concept of “probability-to-distinguish” was provided as an improvement on the Rose criterion. It can be used to find out the threshold of the photon number. The new method was applied to analyze noised images in image subtraction and compute the minimum photon number required. The conclusion is that objects can be distinguished from their background when the probability-to-distinguish is set at above 0.4.

    Abstract

    Stochastic photon noise is one of the most important factors which influence the quality of X-ray microscope images. The influence of the noise is described by the signal-to-noise ratio (SNR). Generally, increasing the number of photons yields better SNR, but inevitably with the side effect of a higher dose for the specimens. To find a good balance, one has to find a way to minimize the photon number while keeping an acceptable SNR, but conventional empirical Rose criterion is of little help, especially when applied to processed noised images. Here a concept of “probability-to-distinguish” was provided as an improvement on the Rose criterion. It can be used to find out the threshold of the photon number. The new method was applied to analyze noised images in image subtraction and compute the minimum photon number required. The conclusion is that objects can be distinguished from their background when the probability-to-distinguish is set at above 0.4.
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    • References(23)
  • [1]
    Rose A. The sensitivity performance of the human eye on an absolute scale[J]. JOSA, 1948, 38(2): 196-208.
    [2]
    Watts R, Wang Y, Winchester P A, et al. Rose model in MRI: Noise limitations on spatial resolution and implications for contrast enhanced MR angiography[C]//Proceedings of the 19th Annual Scientific Meeting of the Society of Magnetic Resonance in Medicine. Denvers, Color: Society of Magnetic Resonance in Medicine. 2000: 462.
    [3]
    Pelli D G, Farell B. Why use noise?[J]. JOSA A, 1999, 16(3): 647-653.
    [4]
    Godard P, Allain M, Chamard V, et al. Noise models for low counting rate coherent diffraction imaging[J]. Optics Express, 2012, 20(23): 25 914-25 934.
    [5]
    Gao Hongyi, Chen Jianwen, Lu Peixiang, et al. Soft X-ray microscopy[J]. Chinese Journal of Nature, 2001, 23(1): 33-39.
    高鸿奕, 陈建文, 陆培祥, 等. 软X 射线显微术[J]. 自然杂志, 2001, 23(1): 33-39.
    [6]
    Xie Xingshu. Soft X-ray microimaging using synchrotron radiation[J]. Physics Experimentation, 2001, 21(11): 3-6.
    谢行恕. 同步辐射软 X 射线显微成像[J]. 物理实验, 2001, 21(11): 3-6.
    [7]
    Michel T, Anton G, Bhnel M, et al. A fundamental method to determine the signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) for a photon counting pixel detector[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 568(2): 799-802.
    [8]
    Wagner R F, Brown D G. Unified SNR analysis of medical imaging systems[J]. Physics in Medicine and Biology, 1985, 30(6): 489.
    [9]
    Zhang Y, Ning R. Investigation of image noise in cone-beam CT imaging due to photon counting statistics with the Feldkamp algorithm by computer simulations[J]. Journal of X-ray Science and Technology, 2008, 16(2): 143-158.
    [10]
    Cunningham I A, Shaw R. Signal-to-noise optimization of medical imaging systems[J]. JOSA A, 1999, 16(3): 621-632.
    [11]
    Wunderlich A, Noo F. Evaluation of image noise in fan-beam X-ray computed tomography[C]// 30th Annual International IEEE EMBS Conference. IEEE, 2008: 2 713-2 716.
    [12]
    Seitz P, Muller A, Ruegsegger P. The influence of photon counting statistics on picture noise and reproducibility in quantitative computed tomography[J]. IEEE Transactions on Nuclear Science, 1985, 32(1): 1 162-1 168.
    [13]
    Bennett K E, Byer R L. Fan-beam-tomography noise theory[J]. JOSA A, 1986, 3(5): 624-633.
    [14]
    Badulescu P, Zacin R. A two-state switched-median filter[C]// International Semiconductor Conference, 2000（CAS 2000 Proceedings）. IEEE, 2000, 1: 289-292.
    [15]
    Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images[J]. IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing, 1999, 46(1): 78-80.
    [16]
    Abreu E, Lightstone M, Mitra S K, et al. A new efficient approach for the removal of impulse noise from highly corrupted images[J]. IEEE Transactions on Image Processing, 1996, 5(6): 1 012-1 025.
    [17]
    Frosio I, Borghese N A. Human Visual System modelling for real-time salt and pepper noise removal[M]//Biological and Artificial Intelligence Environments. Netherlands: Springer, 2005: 337-342.
    [18]
    Dahlbom M, Hoffman E J. Problems in signal-to-noise ratio for attenuation correction in high resolution PET[J]. IEEE Transactions on Nuclear Science, 1987, 34(1): 288-293.
    [19]
    Schade S R. Optical and photoelectric analog of the eye[J]. JOSA, 1956, 46(9): 721-738.
    [20]
    Edelstein W A, Glover G H, Hardy C J, et al. The intrinsic signal-to-noise ratio in NMR imaging[J]. Magnetic Resonance in Medicine, 1986, 3(4): 604-618.
    [21]
    Barlow H B. Retinal noise and absolute threshold[J]. JOSA, 1956, 46(8): 634-639.
    [22]
    Burgess A E. The Rose model, revisited[J]. JOSA A, 1999, 16(3): 633-646.
    [23]
    Pelli D G. Uncertainty explains many aspects of visual contrast detection and discrimination[J]. JOSA A, 1985, 2(9): 1 508-1 531.
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    [1]
    Rose A. The sensitivity performance of the human eye on an absolute scale[J]. JOSA, 1948, 38(2): 196-208.
    [2]
    Watts R, Wang Y, Winchester P A, et al. Rose model in MRI: Noise limitations on spatial resolution and implications for contrast enhanced MR angiography[C]//Proceedings of the 19th Annual Scientific Meeting of the Society of Magnetic Resonance in Medicine. Denvers, Color: Society of Magnetic Resonance in Medicine. 2000: 462.
    [3]
    Pelli D G, Farell B. Why use noise?[J]. JOSA A, 1999, 16(3): 647-653.
    [4]
    Godard P, Allain M, Chamard V, et al. Noise models for low counting rate coherent diffraction imaging[J]. Optics Express, 2012, 20(23): 25 914-25 934.
    [5]
    Gao Hongyi, Chen Jianwen, Lu Peixiang, et al. Soft X-ray microscopy[J]. Chinese Journal of Nature, 2001, 23(1): 33-39.
    高鸿奕, 陈建文, 陆培祥, 等. 软X 射线显微术[J]. 自然杂志, 2001, 23(1): 33-39.
    [6]
    Xie Xingshu. Soft X-ray microimaging using synchrotron radiation[J]. Physics Experimentation, 2001, 21(11): 3-6.
    谢行恕. 同步辐射软 X 射线显微成像[J]. 物理实验, 2001, 21(11): 3-6.
    [7]
    Michel T, Anton G, Bhnel M, et al. A fundamental method to determine the signal-to-noise ratio (SNR) and detective quantum efficiency (DQE) for a photon counting pixel detector[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 568(2): 799-802.
    [8]
    Wagner R F, Brown D G. Unified SNR analysis of medical imaging systems[J]. Physics in Medicine and Biology, 1985, 30(6): 489.
    [9]
    Zhang Y, Ning R. Investigation of image noise in cone-beam CT imaging due to photon counting statistics with the Feldkamp algorithm by computer simulations[J]. Journal of X-ray Science and Technology, 2008, 16(2): 143-158.
    [10]
    Cunningham I A, Shaw R. Signal-to-noise optimization of medical imaging systems[J]. JOSA A, 1999, 16(3): 621-632.
    [11]
    Wunderlich A, Noo F. Evaluation of image noise in fan-beam X-ray computed tomography[C]// 30th Annual International IEEE EMBS Conference. IEEE, 2008: 2 713-2 716.
    [12]
    Seitz P, Muller A, Ruegsegger P. The influence of photon counting statistics on picture noise and reproducibility in quantitative computed tomography[J]. IEEE Transactions on Nuclear Science, 1985, 32(1): 1 162-1 168.
    [13]
    Bennett K E, Byer R L. Fan-beam-tomography noise theory[J]. JOSA A, 1986, 3(5): 624-633.
    [14]
    Badulescu P, Zacin R. A two-state switched-median filter[C]// International Semiconductor Conference, 2000（CAS 2000 Proceedings）. IEEE, 2000, 1: 289-292.
    [15]
    Wang Z, Zhang D. Progressive switching median filter for the removal of impulse noise from highly corrupted images[J]. IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing, 1999, 46(1): 78-80.
    [16]
    Abreu E, Lightstone M, Mitra S K, et al. A new efficient approach for the removal of impulse noise from highly corrupted images[J]. IEEE Transactions on Image Processing, 1996, 5(6): 1 012-1 025.
    [17]
    Frosio I, Borghese N A. Human Visual System modelling for real-time salt and pepper noise removal[M]//Biological and Artificial Intelligence Environments. Netherlands: Springer, 2005: 337-342.
    [18]
    Dahlbom M, Hoffman E J. Problems in signal-to-noise ratio for attenuation correction in high resolution PET[J]. IEEE Transactions on Nuclear Science, 1987, 34(1): 288-293.
    [19]
    Schade S R. Optical and photoelectric analog of the eye[J]. JOSA, 1956, 46(9): 721-738.
    [20]
    Edelstein W A, Glover G H, Hardy C J, et al. The intrinsic signal-to-noise ratio in NMR imaging[J]. Magnetic Resonance in Medicine, 1986, 3(4): 604-618.
    [21]
    Barlow H B. Retinal noise and absolute threshold[J]. JOSA, 1956, 46(8): 634-639.
    [22]
    Burgess A E. The Rose model, revisited[J]. JOSA A, 1999, 16(3): 633-646.
    [23]
    Pelli D G. Uncertainty explains many aspects of visual contrast detection and discrimination[J]. JOSA A, 1985, 2(9): 1 508-1 531.
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