Acta Optica Sinica, Volume. 43, Issue 20, 2034001(2023)

Denoising Algorithm of Multi-Pinhole Collimated X-Ray Fluorescence CT Based on Noise Level Estimation

Ruge Zhao1, Peng Feng1,2、*, Yan Luo1, Song Zhang1, Peng He1,2, and Yanan Liu3、**
Author Affiliations
  • 1Key Lab of Optoelectronic Technology & Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
  • 2ICT NDT Engineering Research Center, Ministry of Education, Chongqing University, Chongqing 400044, China
  • 3School of Electronics and Information Engineering, Chongqing Technology and Business Institute, Chongqing 400032, China
  • show less
    References(21)

    [1] Manohar N, Cho S H. Quality of micro-CT images acquired from simultaneous micro-CT and benchtop X-ray fluorescence computed tomography (XFCT): a preliminary Monte Carlo study[C](2014).

    [2] Manohar N, Reynoso F J, Diagaradjane P et al. Quantitative imaging of gold nanoparticle distribution in a tumor-bearing mouse using benchtop X-ray fluorescence computed tomography[J]. Scientific Reports, 6, 22079(2016).

    [3] Hanson A L. The calculation of scattering cross sections for polarized X-rays[J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 243, 583-598(1986).

    [4] Deng B, Yang Q, Du G H et al. The progress of X-ray fluorescence computed tomography at SSRF[J]. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions With Materials and Atoms, 305, 5-8(2013).

    [5] Feng P, Cong W X, Wei B et al. Analytic comparison between X-ray fluorescence CT and K-edge CT[J]. IEEE Transactions on Bio-Medical Engineering, 61, 975-985(2014).

    [6] Jones B L, Manohar N, Reynoso F et al. Experimental demonstration of benchtop X-ray fluorescence computed tomography (XFCT) of gold nanoparticle-loaded objects using lead- and tin-filtered polychromatic cone-beams[J]. Physics in Medicine and Biology, 57, N457-N467(2012).

    [7] Jung S, Lee J M, Cho H et al. Compton background elimination for in vivo X-ray fluorescence imaging of gold nanoparticles using convolutional neural network[J]. IEEE Transactions on Nuclear Science, 67, 2311-2320(2020).

    [8] La Riviere P J, Vargas P A. Monotonic penalized-likelihood image reconstruction for X-ray fluorescence computed tomography[J]. IEEE Transactions on Medical Imaging, 25, 1117-1129(2006).

    [9] Yang Q, Deng B A, Lü W W et al. Fast and accurate X-ray fluorescence computed tomography imaging with the ordered-subsets expectation maximization algorithm[J]. Journal of Synchrotron Radiation, 19, 210-215(2012).

    [10] Rezaeifar B, Wolfs C J A, Lieuwes N G et al. A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting[J]. Physics in Medicine & Biology, 67, 144003(2022).

    [11] Lun M C, Cong W X, Arifuzzaman M et al. Focused X-ray luminescence imaging system for small animals based on a rotary gantry[J]. Journal of Biomedical Optics, 26, 036004(2021).

    [12] Wolterink J M, Leiner T, Viergever M A et al. Generative adversarial networks for noise reduction in low-dose CT[J]. IEEE Transactions on Medical Imaging, 36, 2536-2545(2017).

    [13] Zhang K, Zuo W M, Chen Y J et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2017).

    [14] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

    [15] Dabov K, Foi A, Katkovnik V et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing, 16, 2080-2095(2007).

    [16] Guo J, Feng P, Deng L Z et al. Optimization of detection angle for pinhole X-ray fluorescence computed tomography[J]. Acta Optica Sinica, 40, 0111017(2020).

    [17] Luo Y, Feng P, Guo J et al. Simulation research of multi-pinhole collimated L-shell XFCT imaging system[J]. IEEE Access, 8, 180273-180279(2020).

    [18] Li Y Y, Shaker K, Larsson J C et al. A library of potential nanoparticle contrast agents for X-ray fluorescence tomography bioimaging[J]. Contrast Media & Molecular Imaging, 2018, 8174820(2018).

    [19] Luo Y, Feng P, Zhao R G et al. Simulation research of potential contrast agents for X-ray fluorescence CT with photon counting detectors[J]. Frontiers in Physics, 9, 686988(2021).

    [20] Zhong H, Ma K, Zhou Y. Modified BM3D algorithm for image denoising using nonlocal centralization prior[J]. Signal Processing, 106, 342-347(2015).

    [21] Chen H J, Dai S K. Improved block-based image noise estimation algorithm[J]. Journal of Computer Applications, 34, 2014-2017(2014).

    Tools

    Get Citation

    Copy Citation Text

    Ruge Zhao, Peng Feng, Yan Luo, Song Zhang, Peng He, Yanan Liu. Denoising Algorithm of Multi-Pinhole Collimated X-Ray Fluorescence CT Based on Noise Level Estimation[J]. Acta Optica Sinica, 2023, 43(20): 2034001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: X-Ray Optics

    Received: Mar. 15, 2023

    Accepted: May. 19, 2023

    Published Online: Oct. 23, 2023

    The Author Email: Feng Peng (coe-fp@cqu.edu.cn), Liu Yanan (2030329861@qq.com)

    DOI:10.3788/AOS230679

    Topics