Universal reconstruction method for x-ray scattering tensor tomography based on wavefront modulation

被引：1
|
作者：
Lautizi, Ginevra [1 ,2 ]
Studer, Alain [3 ]
Zdora, Marie-Christine [4 ,5 ]
De Marco, Fabio [1 ,2 ]
Kim, Jisoo [6 ]
Di Trapani, Vittorio [1 ,2 ]
Marone, Federica [4 ]
Thibault, Pierre [1 ,2 ]
Stampanoni, Marco [4 ,5 ]
机构：
[1] Univ Trieste, Dept Phys, Trieste, Italy
[2] Elettra Sincrotrone Trieste, Basovizza, Italy
[3] Paul Scherrer Inst, Data Proc Dev & Consulting Grp, CH-5232 Villigen, Switzerland
[4] Paul Scherrer Inst, Photon Sci Div, CH-5232 Villigen, Switzerland
[5] Swiss Fed Inst Technol, Inst Biomed Engn, CH-8092 Zurich, Switzerland
[6] Korea Res Inst Stand & Sci, Adv Instrumentat Inst, Daejeon, South Korea
来源：
PHYSICAL REVIEW APPLIED | 2024年 / 22卷 / 02期
基金：
欧洲研究理事会;
关键词：
CONTRAST;
D O I：
10.1103/PhysRevApplied.22.024031
中图分类号：
O59 [应用物理学];
学科分类号：
摘要：
We present a versatile method for full-field x-ray scattering tensor tomography that is based on energy conservation and is applicable to data obtained using different wavefront modulators. Using this algorithm, we pave the way for speckle-based tensor tomography. The proposed model relies on a mathematical approach that allows tuning spatial resolution and signal sensitivity. We present the application of the algorithm to three different imaging modalities and demonstrate its potential for applications of x-ray directional dark-field imaging.
引用
收藏
页数：7
相关论文
共 50 条
  • [31] Wavefront transformation and the modulation transfer function of x-ray multilayer mirrors
    Artioukov, IA
    Fechtchenko, RM
    Vinogradov, AV
    JOURNAL OF OPTICS A-PURE AND APPLIED OPTICS, 2002, 4 (03): : 233 - 236
  • [32] Reconstruction of x-ray penumbral images based on a heuristic method
    Nozaki, S
    Chen, YW
    Nakao, Z
    Shiraga, H
    IMAGE PROCESSING: ALGORITHMS AND SYSTEMS, 2002, 4667 : 303 - 311
  • [33] Incorporating a Noise Reduction Technique Into X-Ray Tensor Tomography
    Seyyedi, Saeed
    Wieczorek, Matthias
    Pfeiffer, Franz
    Lasser, Tobias
    IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, 2018, 4 (01): : 137 - 146
  • [34] LOW-RANK TENSOR REGRESSION FOR X-RAY TOMOGRAPHY
    Jantre, Sanket R.
    Di, Zichao Wendy
    2021 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2021, : 2833 - 2837
  • [35] Image reconstruction algorithm to X-ray diffraction tomography
    Barroso, RC
    Lopes, RT
    Gonçalves, OD
    de Assis, JT
    NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 1999, 422 (1-3): : 718 - 723
  • [36] Adaptive image reconstruction applied to X-ray tomography
    Artemiev, VM
    Naumov, AO
    Tillack, GR
    MATERIALPRUFUNG, 1998, 40 (09): : 342 - 345
  • [37] Hyperspectral image reconstruction for x-ray fluorescence tomography
    Guersoy, Doga
    Bicer, Tekin
    Lanzirotti, Antonio
    Newville, Matthew G.
    De Carlo, Francesco
    OPTICS EXPRESS, 2015, 23 (07): : 9014 - 9023
  • [38] X-ray diffraction tomography: Image reconstruction and applications
    Grant, JA
    Davis, JR
    Morgan, MJ
    Wells, P
    TRENDS IN NDE SCIENCE AND TECHNOLOGY - PROCEEDINGS OF THE 14TH WORLD CONFERENCE ON NDT (14TH WCNDT), VOLS 1-5, 1996, : 1427 - 1430
  • [39] Formal estimation of reconstruction quality in X-ray tomography
    Anikonov, DS
    Prokhorov, IV
    DOKLADY MATHEMATICS, 2005, 71 (02) : 289 - 292
  • [40] Quality of the reconstruction in x-ray phase contrast tomography
    Arhatari, B. D.
    Peele, A. G.
    Nugent, K. A.
    De Carlo, F.
    REVIEW OF SCIENTIFIC INSTRUMENTS, 2006, 77 (06):
12345