Optimising 4D imaging of fast-oscillating structures using X-ray microtomography with retrospective gating.
Fast-oscillating multiscale structures
Motion blur limitation
Retrospective gating
Synchrotron X-ray microtomography
Tomographic simulation
Vibration testing
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 Sep 2024
03 Sep 2024
Historique:
received:
04
06
2024
accepted:
26
07
2024
medline:
4
9
2024
pubmed:
4
9
2024
entrez:
3
9
2024
Statut:
epublish
Résumé
Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials.
Identifiants
pubmed: 39227377
doi: 10.1038/s41598-024-68684-1
pii: 10.1038/s41598-024-68684-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20499Informations de copyright
© 2024. The Author(s).
Références
Ford, N. L. et al. Prospective respiratory-gated micro-CT of free breathing rodents. Med. Phys. 32, 2888–2898. https://doi.org/10.1118/1.2013007 (2005).
doi: 10.1118/1.2013007
pubmed: 16266103
Lovric, G. et al. Tomographic in vivo microscopy for the study of lung physiology at the alveolar level. Sci. Rep. 7, 12545. https://doi.org/10.1038/s41598-017-12886-3 (2017).
doi: 10.1038/s41598-017-12886-3
pubmed: 28970505
pmcid: 5624921
Dejea, H. et al. A tomographic microscopy-compatible Langendorff system for the dynamic structural characterization of the cardiac cycle. Front. Cardiovasc. Med. 9, 1023483. https://doi.org/10.3389/fcvm.2022.1023483 (2022).
doi: 10.3389/fcvm.2022.1023483
pubmed: 36620622
pmcid: 9815149
Maiditsch, I. P., Ladich, F., Heß, M., Schlepütz, C. M. & Schulz-Mirbach, T. Revealing sound-induced motion patterns in fish hearing structures in 4D: A standing wave tube-like setup designed for high-resolution time-resolved tomography. J. Exp. Biol. 225, jeb243614. https://doi.org/10.1242/jeb.243614 (2022).
doi: 10.1242/jeb.243614
pubmed: 34904652
pmcid: 8778803
Schmeltz, M. et al. The human middle ear in motion: 3D visualization and quantification using dynamic synchrotron-based X-ray imaging. Commun. Biol. 7, 157. https://doi.org/10.1038/s42003-023-05738-6 (2024).
doi: 10.1038/s42003-023-05738-6
pubmed: 38326549
pmcid: 10850498
Titze, I. R. Principles of voice production 2nd edn. (National Center for Voice and Speech, 2000).
Luizard, P. et al. Flow-induced oscillations of vocal-fold replicas with tuned extensibility and material properties. Sci. Rep. 13, 22658. https://doi.org/10.1038/s41598-023-48080-x (2023).
doi: 10.1038/s41598-023-48080-x
pubmed: 38114547
pmcid: 10730560
Timcke, R. Die Synchron-Stroboskopie von menschlichen Stimmlippen bzw. ähnlichen Schallquellen und Messung der Offnungszeit [Synchronous stroboscopy of the vocal cords in man and analogous sources of sound and the duration of opening]. Laryngol. Rhinol. Otol. 35, 331–5 (1956).
Švec, J. G. & Schutte, H. K. Videokymography: High-speed line scanning of vocal fold vibration. J. Voice 10, 201–205. https://doi.org/10.1016/S0892-1997(96)80047-6 (1996).
doi: 10.1016/S0892-1997(96)80047-6
pubmed: 8734395
Schutte, H. K., Svec, J. G. & Sram, F. First results of clinical application of videokymography. Laryngoscope 108, 1206–10. https://doi.org/10.1097/00005537-199808000-00020 (1998).
doi: 10.1097/00005537-199808000-00020
pubmed: 9707245
Deliyski, D. D. & Hillman, R. E. State of the art laryngeal imaging: Research and clinical implications. Curr. Opin. Otolaryngol. Head Neck Surg. 18, 147–152. https://doi.org/10.1097/MOO.0b013e3283395dd4 (2010).
doi: 10.1097/MOO.0b013e3283395dd4
pubmed: 20463479
pmcid: 2931794
Baken, R. & Orlikoff, R. F. Clinical measurement of speech and voice (Singular, 2000).
Ziethe, A., Patel, R., Kunduk, M., Eysholdt, U. & Graf, S. Clinical analysis methods of voice disorders. Curr. Bioinform. 6, 270–285. https://doi.org/10.2174/157489311796904682 (2011).
doi: 10.2174/157489311796904682
Andrade-Miranda, G., Stylianou, Y., Deliyski, D. D., Godino-Llorente, J. I. & Henrich Bernardoni, N. Laryngeal image processing of vocal folds motion. Appl. Sci. 10, 1556. https://doi.org/10.3390/app10051556 (2020).
doi: 10.3390/app10051556
Bailly, L. et al. 3D multiscale imaging of human vocal folds using synchrotron X-ray microtomography in phase retrieval mode. Sci. Rep. 8, 14003. https://doi.org/10.1038/s41598-018-31849-w (2018).
doi: 10.1038/s41598-018-31849-w
pubmed: 30228304
pmcid: 6143640
Olbinado, M. P., Vagovič, P., Yashiro, W. & Momose, A. Demonstration of stroboscopic X-ray talbot interferometry using polychromatic synchrotron and laboratory X-ray sources. Appl. Phys. Express 6, 096601. https://doi.org/10.7567/APEX.6.096601 (2013).
doi: 10.7567/APEX.6.096601
Schulz-Mirbach, T. et al. In-situ visualization of sound-induced otolith motion using hard X-ray phase contrast imaging. Sci. Rep. 8, 3121. https://doi.org/10.1038/s41598-018-21367-0 (2018).
doi: 10.1038/s41598-018-21367-0
pubmed: 29449570
pmcid: 5814409
Schulz-Mirbach, T. et al. Auditory chain reaction: Effects of sound pressure and particle motion on auditory structures in fishes. PLoS ONE 15, e0230578. https://doi.org/10.1371/journal.pone.0230578 (2020).
doi: 10.1371/journal.pone.0230578
pubmed: 32218605
pmcid: 7100961
García-Moreno, F. et al. Tomoscopy: Time-resolved tomography for dynamic processes in materials. Adv. Mater. 33, 2104659. https://doi.org/10.1002/adma.202104659 (2021).
doi: 10.1002/adma.202104659
dos Santos Rolo, T., Ershov, A., van de Kamp, T. & Baumbach, T. In vivo X-ray cine-tomography for tracking morphological dynamics. Proc. Natl. Acad. Sci. 111, 3921–3926. https://doi.org/10.1073/pnas.1308650111 (2014).
doi: 10.1073/pnas.1308650111
pubmed: 24594600
pmcid: 3964127
Mokso, R. et al. Four-dimensional in vivo X-ray microscopy with projection-guided gating. Sci. Rep. 5, 8727. https://doi.org/10.1038/srep08727 (2015).
doi: 10.1038/srep08727
pubmed: 25762080
pmcid: 4356984
Maire, E., Le Bourlot, C., Adrien, J., Mortensen, A. & Mokso, R. 20 Hz X-ray tomography during an in situ tensile test. Int. J. Fract. 200, 3–12. https://doi.org/10.1007/s10704-016-0077-y (2016).
doi: 10.1007/s10704-016-0077-y
Laurencin, T. et al. 3D real-time and in situ characterisation of fibre kinematics in dilute non-Newtonian fibre suspensions during confined and lubricated compression flow. Compos. Sci. Technol. 134, 258–266. https://doi.org/10.1016/j.compscitech.2016.09.004 (2016).
doi: 10.1016/j.compscitech.2016.09.004
Ferré Sentis, D. et al. 3D in situ observations of the compressibility and pore transport in Sheet Moulding Compounds during the early stages of compression moulding. Compos. A Appl. Sci. Manuf. 92, 51–61. https://doi.org/10.1016/j.compositesa.2016.10.031 (2017).
doi: 10.1016/j.compositesa.2016.10.031
Laurencin, T. et al. 3D real time and in situ observation of the fibre orientation during the plane strain flow of concentrated fibre suspensions. J. Nonnewton. Fluid Mech. 312, 104978. https://doi.org/10.1016/j.jnnfm.2022.104978 (2023).
doi: 10.1016/j.jnnfm.2022.104978
Amedewovo, L. et al. Deconsolidation of carbon fiber-reinforced PEKK laminates: 3D real-time in situ observation with synchrotron X-ray microtomography. Compos. A Appl. Sci. Manuf. 177, 107917. https://doi.org/10.1016/j.compositesa.2023.107917 (2024).
doi: 10.1016/j.compositesa.2023.107917
Keall, P. J., Kini, V. R., Vedam, S. S. & Mohan, R. Potential radiotherapy improvements with respiratory gating. Australas. Phys. Eng. Sci. Med. 25, 1–6. https://doi.org/10.1007/BF03178368 (2002).
doi: 10.1007/BF03178368
pubmed: 12049470
Drangova, M., Ford, N. L., Detombe, S. A., Wheatley, A. R. & Holdsworth, D. W. Fast retrospectively gated quantitative four-dimensional (4D) cardiac micro computed tomography imaging of free-breathing mice. Invest. Radiol. 42, 85–94. https://doi.org/10.1097/01.rli.0000251572.56139.a3 (2007).
doi: 10.1097/01.rli.0000251572.56139.a3
pubmed: 17220726
Fardin, L. et al. Imaging atelectrauma in ventilator-induced lung injury using 4D X-ray microscopy. Sci. Rep. 11, 4236. https://doi.org/10.1038/s41598-020-77300-x (2021).
doi: 10.1038/s41598-020-77300-x
pubmed: 33608569
pmcid: 7895928
Badea, C., Hedlund, L. W. & Johnson, G. A. Micro-CT with respiratory and cardiac gating: Micro-CT with respiratory and cardiac gating. Med. Phys. 31, 3324–3329. https://doi.org/10.1118/1.1812604 (2004).
doi: 10.1118/1.1812604
pubmed: 15651615
Badea, C. T., Fubara, B., Hedlund, L. W. & Johnson, G. A. 4-D micro-CT of the mouse heart. Mol. Imaging 4, 153535002005041. https://doi.org/10.1162/15353500200504187 (2005).
doi: 10.1162/15353500200504187
Guo, X., Johnston, S. M., Qi, Y., Johnson, G. A. & Badea, C. T. 4D micro-CT using fast prospective gating. Phys. Med. Biol. 57, 257–271. https://doi.org/10.1088/0031-9155/57/1/257 (2012).
doi: 10.1088/0031-9155/57/1/257
pubmed: 22156062
pmcid: 3388603
Murrie, R. P. et al. Real-time in vivo imaging of regional lung function in a mouse model of cystic fibrosis on a laboratory X-ray source. Sci. Rep. 10, 447. https://doi.org/10.1038/s41598-019-57376-w (2020).
doi: 10.1038/s41598-019-57376-w
pubmed: 31949224
pmcid: 6965186
Manzke, R., Köhler, Th., Nielsen, T., Hawkes, D. & Grass, M. Automatic phase determination for retrospectively gated cardiac CT: Automatic phase determination for retrospectively gated cardiac CT. Med. Phys. 31, 3345–3362. https://doi.org/10.1118/1.1791351 (2004).
doi: 10.1118/1.1791351
pubmed: 15651618
Hu, J., Haworth, S. T., Molthen, R. C. & Dawson, C. A. Dynamic small animal lung imaging via a postacquisition respiratory gating technique using micro-cone beam computed tomography1. Acad. Radiol. 11, 961–970. https://doi.org/10.1016/j.acra.2004.05.019 (2004).
doi: 10.1016/j.acra.2004.05.019
pubmed: 15350577
Sonke, J.-J., Zijp, L., Remeijer, P. & van Herk, M. Respiratory correlated cone beam CT: Respiratory correlated cone beam CT. Med. Phys. 32, 1176–1186. https://doi.org/10.1118/1.1869074 (2005).
doi: 10.1118/1.1869074
pubmed: 15895601
Ford, N. L., Wheatley, A. R., Holdsworth, D. W. & Drangova, M. Optimization of a retrospective technique for respiratory-gated high speed micro-CT of free-breathing rodents. Phys. Med. Biol. 52, 5749–5769. https://doi.org/10.1088/0031-9155/52/19/002 (2007).
doi: 10.1088/0031-9155/52/19/002
pubmed: 17881798
Walker, S. M. et al. In vivo time-resolved microtomography reveals the mechanics of the blowfly flight motor. PLoS Biol. 12, e1001823. https://doi.org/10.1371/journal.pbio.1001823 (2014).
doi: 10.1371/journal.pbio.1001823
pubmed: 24667677
pmcid: 3965381
Fardin, L. In-vivo dynamic 3D phase-contrast microscopy: A novel tool to investigate the mechanisms of ventilator induced lung injury. Ph.D. thesis, Université Grenoble Alpes (2019).
Schuler, J., Neuendorf, L. M., Petersen, K. & Kockmann, N. Micro-computed tomography for the 3D time-resolved investigation of monodisperse droplet generation in a co-flow setup. AIChE J. https://doi.org/10.1002/aic.17111 (2021).
doi: 10.1002/aic.17111
Paganin, D., Mayo, S. C., Gureyev, T. E., Miller, P. R. & Wilkins, S. W. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. J. Microsc. 206, 33–40. https://doi.org/10.1046/j.1365-2818.2002.01010.x (2002).
doi: 10.1046/j.1365-2818.2002.01010.x
pubmed: 12000561
Wu, Y., Takano, H. & Momose, A. Time-resolved x-ray stroboscopic phase tomography using Talbot interferometer for dynamic deformation measurements. Rev. Sci. Instrum. 92, 043702. https://doi.org/10.1063/5.0030811 (2021).
doi: 10.1063/5.0030811
pubmed: 34243370
Lovric, G., Mokso, R., Schlepütz, C. M. & Stampanoni, M. A multi-purpose imaging endstation for high-resolution micrometer-scaled sub-second tomography. Physica Med. 32, 1771–1778. https://doi.org/10.1016/j.ejmp.2016.08.012 (2016).
doi: 10.1016/j.ejmp.2016.08.012
Mokso, R. et al. GigaFRoST: The gigabit fast readout system for tomography. J. Synchrotron Radiat. 24, 1250–1259. https://doi.org/10.1107/S1600577517013522 (2017).
doi: 10.1107/S1600577517013522
pubmed: 29091068
pmcid: 5665295
Maire, E. & Withers, P. J. Quantitative X-ray tomography. Int. Mater. Rev. 59, 1–43. https://doi.org/10.1179/1743280413Y.0000000023 (2014).
doi: 10.1179/1743280413Y.0000000023
Dubsky, S., Hooper, S. B., Siu, K. K. W. & Fouras, A. Synchrotron-based dynamic computed tomography of tissue motion for regional lung function measurement. J. R. Soc. Interface 9, 2213–2224. https://doi.org/10.1098/rsif.2012.0116 (2012).
doi: 10.1098/rsif.2012.0116
pubmed: 22491972
pmcid: 3405755
Hoshino, M., Uesugi, K. & Yagi, N. 4D x-ray phase contrast tomography for repeatable motion of biological samples. Rev. Sci. Instrum. 87, 093705. https://doi.org/10.1063/1.4962405 (2016).
doi: 10.1063/1.4962405
pubmed: 27782563
Matsubara, M. et al. Dynamic observation of a damping material using micro X-ray computed tomography coupled with a phase-locked loop. Polym. Test. https://doi.org/10.1016/j.polymertesting.2022.107810 (2022).
doi: 10.1016/j.polymertesting.2022.107810
Tekawade, A. et al. Time-resolved 3D imaging of two-phase fluid flow inside a steel fuel injector using synchrotron X-ray tomography. Sci. Rep. 10, 8674. https://doi.org/10.1038/s41598-020-65701-x (2020).
doi: 10.1038/s41598-020-65701-x
pubmed: 32457398
pmcid: 7250894
Herrmann, J., Hoffman, E. A. & Kaczka, D. W. Frequency-selective computed tomography: Applications during periodic thoracic motion. IEEE Trans. Med. Imaging 36, 1722–1732. https://doi.org/10.1109/TMI.2017.2694887 (2017).
doi: 10.1109/TMI.2017.2694887
pubmed: 28436852
pmcid: 5639881
Nakamura, M. et al. Impact of motion velocity on four-dimensional target volumes: A phantom study: Impact of motion velocity on 4D target volumes. Med. Phys. 36, 1610–1617. https://doi.org/10.1118/1.3110073 (2009).
doi: 10.1118/1.3110073
pubmed: 19544777
Li, T. et al. Four-dimensional cone-beam computed tomography using an on-board imager. Med. Phys. 33, 3825–3833. https://doi.org/10.1118/1.2349692 (2006).
doi: 10.1118/1.2349692
pubmed: 17089847
Yamamoto, T., Langner, U., Loo, B. W., Shen, J. & Keall, P. J. Retrospective analysis of artifacts in four-dimensional CT images of 50 abdominal and thoracic radiotherapy patients. Int. J. Radiat. Oncol. Biol. Phys. 72, 1250–1258. https://doi.org/10.1016/j.ijrobp.2008.06.1937 (2008).
doi: 10.1016/j.ijrobp.2008.06.1937
pubmed: 18823717
pmcid: 2583232
Rueckel, J., Stockmar, M., Pfeiffer, F. & Herzen, J. Spatial resolution characterization of a X-ray microCT system. Appl. Radiat. Isot. 94, 230–234. https://doi.org/10.1016/j.apradiso.2014.08.014 (2014).
doi: 10.1016/j.apradiso.2014.08.014
pubmed: 25233529
McCullough, E. C. et al. Performance evaluation and quality assurance of computed tomography scanners, with illustrations from the EMI, ACTA, and delta scanners. Radiology 120, 173–188. https://doi.org/10.1148/120.1.173 (1976).
doi: 10.1148/120.1.173
pubmed: 935444
García-Moreno, F. et al. Using X-ray tomoscopy to explore the dynamics of foaming metal. Nat. Commun. 10, 3762. https://doi.org/10.1038/s41467-019-11521-1 (2019).
doi: 10.1038/s41467-019-11521-1
pubmed: 31434878
pmcid: 6704127
Stamati, O. et al. Spam: Software for practical analysis of materials. J. Open Source Softw. 5, 2286. https://doi.org/10.21105/joss.02286 (2020).
doi: 10.21105/joss.02286
Stamati, O. et al. Advanced analysis of the bias-extension of woven fabrics with X-ray microtomography and Digital Volume Correlation. Compos. A Appl. Sci. Manuf. 175, 107748. https://doi.org/10.1016/j.compositesa.2023.107748 (2023).
doi: 10.1016/j.compositesa.2023.107748
Barrett, J. F. & Keat, N. Artifacts in CT: Recognition and avoidance. Radiographics 24, 1679–1691. https://doi.org/10.1148/rg.246045065 (2004).
doi: 10.1148/rg.246045065
pubmed: 15537976
Depriester, D. et al. Individual fibre separation in 3D fibrous materials imaged by X-ray tomography. J. Microsc. 286, 220–239. https://doi.org/10.1111/jmi.13096 (2022).
doi: 10.1111/jmi.13096
pubmed: 35244940
O’Brien, R. T., Cooper, B. J. & Keall, P. J. Optimizing 4D cone beam computed tomography acquisition by varying the gantry velocity and projection time interval. Phys. Med. Biol. 58, 1705–1723. https://doi.org/10.1088/0031-9155/58/6/1705 (2013).
doi: 10.1088/0031-9155/58/6/1705
pubmed: 23429168
Park, J. C. et al. Four dimensional digital tomosynthesis using on-board imager for the verification of respiratory motion. PLoS ONE 9, e115795. https://doi.org/10.1371/journal.pone.0115795 (2014).
doi: 10.1371/journal.pone.0115795
pubmed: 25541710
pmcid: 4277366
Withers, P. J. et al. X-ray computed tomography. Nat. Rev. Methods Primers 1, 18. https://doi.org/10.1038/s43586-021-00015-4 (2021).
doi: 10.1038/s43586-021-00015-4
Tengattini, A. & Andò, E. Kalisphera: An analytical tool to reproduce the partial volume effect of spheres imaged in 3D. Meas. Sci. Technol. 26, 095606. https://doi.org/10.1088/0957-0233/26/9/095606 (2015).
doi: 10.1088/0957-0233/26/9/095606
Palenstijn, W., Batenburg, K. & Sijbers, J. Performance improvements for iterative electron tomography reconstruction using graphics processing units (GPUs). J. Struct. Biol. 176, 250–253. https://doi.org/10.1016/j.jsb.2011.07.017 (2011).
doi: 10.1016/j.jsb.2011.07.017
pubmed: 21840398
van Aarle, W. et al. The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography. Ultramicroscopy 157, 35–47. https://doi.org/10.1016/j.ultramic.2015.05.002 (2015).
doi: 10.1016/j.ultramic.2015.05.002
pubmed: 26057688
van Aarle, W. et al. Fast and flexible X-ray tomography using the ASTRA toolbox. Opt. Express 24, 25129. https://doi.org/10.1364/OE.24.025129 (2016).
doi: 10.1364/OE.24.025129
pubmed: 27828452
King, A. et al. Tomography and imaging at the PSICHE beam line of the SOLEIL synchrotron. Rev. Sci. Instrum. 87, 093704. https://doi.org/10.1063/1.4961365 (2016).
doi: 10.1063/1.4961365
pubmed: 27782575
King, A. et al. Recent tomographic imaging developments at the PSICHE beamline. Integrating Mater. Manuf. Innov. 8, 551–558. https://doi.org/10.1007/s40192-019-00155-2 (2019).
doi: 10.1007/s40192-019-00155-2
Mirone, A., Brun, E., Gouillart, E., Tafforeau, P. & Kieffer, J. The PyHST2 hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities. Nucl. Instrum. Methods Phys. Res. Sect. B 324, 41–48. https://doi.org/10.1016/j.nimb.2013.09.030 (2014).
doi: 10.1016/j.nimb.2013.09.030
Rodgers, J. L. & Nicewander, W. A. Thirteen ways to look at the correlation coefficient. Am. Stat. 42, 59. https://doi.org/10.2307/2685263 (1988) arXiv:2685263 .
doi: 10.2307/2685263
Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9, 62–66. https://doi.org/10.1109/TSMC.1979.4310076 (1979).
doi: 10.1109/TSMC.1979.4310076
Rietzel, E., Pan, T. & Chen, G. T. Y. Four-dimensional computed tomography: Image formation and clinical protocol: 4D computed tomography. Med. Phys. 32, 874–889. https://doi.org/10.1118/1.1869852 (2005).
doi: 10.1118/1.1869852
pubmed: 15895570
Cooper, B. J., O’Brien, R. T., Balik, S., Hugo, G. D. & Keall, P. J. Respiratory triggered 4D cone-beam computed tomography: A novel method to reduce imaging dose: RT 4DCBCT: A novel method reducing imaging dose. Med. Phys. 40, 041901. https://doi.org/10.1118/1.4793724 (2013).
doi: 10.1118/1.4793724
pubmed: 23556895
pmcid: 3612115
Hoshino, M., Uesugi, K., Yagi, N. & Tsukube, T. Improvement of scanning procedure for 4D-X-ray phase tomography. Microscopy and Microanalysis. 24(S2), 130–131. https://doi.org/10.1017/S1431927618013041 (2018).
Pfaff, J. et al. In Situ chamber for studying battery failure using high-speed synchrotron radiography. J. Synchrotron Radiat. 30, 192–199. https://doi.org/10.1107/S1600577522010244 (2023).
doi: 10.1107/S1600577522010244
pubmed: 36601937
pmcid: 9814060
Pelt, D., Batenburg, K. & Sethian, J. Improving tomographic reconstruction from limited data using mixed-scale dense convolutional neural networks. J. Imaging 4, 128. https://doi.org/10.3390/jimaging4110128 (2018).
doi: 10.3390/jimaging4110128
Hendriksen, A. A., Pelt, D. M. & Batenburg, K. J. Noise2Inverse: Self-supervised deep convolutional denoising for tomography. IEEE Trans. Comput. Imaging 6, 1320–1335. https://doi.org/10.1109/TCI.2020.3019647 (2020).
doi: 10.1109/TCI.2020.3019647
Liu, Z. et al. TomoGAN: Low-dose synchrotron x-ray tomography with generative adversarial networks: Discussion. J. Opt. Soc. Am. A 37, 422. https://doi.org/10.1364/JOSAA.375595 (2020).
doi: 10.1364/JOSAA.375595