# In situ X-ray computed tomography

In situ X-ray computed tomography (CT) reconstructs three-dimensional images of a sample from two-dimensional X-ray radiographs while the sample is being loaded, heated, reacted, or otherwise processed, so that internal structural evolution is tracked rather than inferred from before-and-after snapshots. It differs from conventional ex situ CT of the same material in that the specimen stays inside its functional environment, a load frame, furnace, electrochemical cell, or flow reactor, during scanning, and the experiment yields a time series of volumes instead of a single static image.<sup>[1](https://www.nature.com/articles/s43586-021-00015-4)</sup> In the battery literature the closely related term operando denotes imaging during actual electrochemical operation.<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup>

| Key fact | Detail |
|---|---|
| What is measured | Time-resolved 3D maps of internal structure, typically via X-ray attenuation, with micrometer-scale spatial resolution<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup> |
| Fastest synchrotron 3D rate | 1000 tomograms per second at 8.2 µm spatial resolution (TOMCAT, Swiss Light Source)<sup>[3](https://pmc.ncbi.nlm.nih.gov/articles/PMC11468671/)</sup> |
| Fastest laboratory CT | Full CT acquisition in 132 ms with a rotating-anode source and sCMOS detector<sup>[4](https://discovery.ucl.ac.uk/id/eprint/10198777/1/Endrizzi_131520O%20%282%29.pdf)</sup> |
| Typical synchrotron resolution | 0.2–5.0 µm for samples of 0.8–20.0 mm (BAMline, BESSY II)<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup> |
| Typical laboratory scan time | At least 30 min for full metal-specimen scans, motivating interrupted loading protocols<sup>[5](https://link.springer.com/article/10.1007/s40799-024-00715-y)</sup> |
| Nano-scale option | 27 nm voxel size and 360 nm spatial resolution demonstrated on a Li-O₂ cell at 8 keV with Zernike phase contrast<sup>[6](https://u-picardie.hal.science/hal-03842611v1/document)</sup> |
| Main trade-off | Photon flux buys speed but raises radiation-damage risk in sensitive materials<sup>[7](https://pubs.rsc.org/en/content/articlehtml/2025/ta/d5ta03471j)</sup> |

## How it works

A CT scanner acquires radiographs of the sample at many angles over a rotation, typically 180°, and a reconstruction algorithm converts the projection set into a 3D map. In parallel-beam geometry the standard method is filtered backprojection, which redistributes each attenuated projection across the reconstruction grid and sums over angles.<sup>[1](https://www.nature.com/articles/s43586-021-00015-4)</sup> With monochromatic synchrotron radiation the reconstruction yields the quantitative linear attenuation coefficient µ(x,y,z) and avoids beam-hardening artifacts.<sup>[8](https://www.ndt.net/article/wcndt2004/pdf/materials_characterization/784_baumbach.pdf)</sup> [Iterative reconstruction](https://www.edgepedia.org/iterative-reconstruction) algorithms are an alternative that can work with fewer projections, at the cost of much larger computational power and longer reconstruction time than filtered backprojection.<sup>[9](https://doi.org/10.1016/j.ejmp.2012.01.003)</sup>

Contrast comes primarily from differences in X-ray attenuation between phases. Where phases attenuate similarly, propagation-based phase contrast helps: the Paganin single-distance phase-retrieval method, reported by D. Paganin and colleagues in the Journal of Microscopy in 2002, reconstructs a 3D phase volume from a single radiograph per angle and is described as the most widely used phase-retrieval method in propagation-based phase-contrast CT.<sup>[10](https://doi.org/10.1046/j.1365-2818.2002.01010.x)</sup> Phase-contrast imaging can be up to 1000× more sensitive than absorption contrast for phases with similar attenuation, although it is less suited to fast real-time capture of evolving features.<sup>[11](https://www.mdpi.com/1996-1944/14/9/2374)</sup>

## How it is done

**Sample environment first.** The cell or rig must be X-ray transparent over the full rotation range while imposing the desired load, temperature, gas, or electrochemical conditions. Laboratory load stages, for example, hold metal micro-tensile specimens while surface strain is tracked from radiographs of a high-X-ray-contrast speckle powder bonded to the surface.<sup>[5](https://link.springer.com/article/10.1007/s40799-024-00715-y)</sup>

**Scan timing depends on scan speed.** Because full laboratory CT scans of metal specimens take at least 30 min, in situ testing usually pauses loading while a volume is collected, with 2D projections captured at roughly 0.3 Hz to 1 Hz for surface strain measurement.<sup>[5](https://link.springer.com/article/10.1007/s40799-024-00715-y)</sup> Fast synchrotron imaging instead runs continuously. Synchronization matters: in operando battery tomography, cells are held in open-circuit mode with several minutes of relaxation after charge or discharge so that material redistribution during scanning does not create artifacts.<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup>

**Reconstruction and analysis.** Pipelines such as TOMCAT's deliver full volumes seconds to minutes after acquisition; its GigaFRoST detector sustains data rates up to 7.7 GB/s, collecting tens to hundreds of TB of raw data per day, and Paganin phase retrieval is part of the pipeline for improving signal-to-noise in sub-second experiments.<sup>[12](https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2019.00346/full)</sup> Deformation between successive volumes is quantified with digital volume correlation, the 3D extension of digital image correlation; applied to time-lapse synchrotron CT of sintering copper powder, it quantified 3D particle movements and local strain.<sup>[13](https://journals.sagepub.com/doi/10.1177/0309324714527588)</sup> Machine-learning segmentation is increasingly used: a "data worsener" pipeline that degrades high-quality laboratory scans to mimic synchrotron artifacts such as rings and cupping trained networks that segment poor-quality in situ data 1–2 orders of magnitude faster than manual segmentation.<sup>[14](https://arxiv.org/abs/2504.19200)</sup>

**Sample environments.** Loading rigs include the ESRF TomoPress, which applies axial loads up to 500 N with micrometer spatial resolution, humidity, temperature and wetting control, and online force monitoring; a representative ID19 protocol used 35 keV pink-beam imaging, 1500 projections over 180°, and PyHST filtered backprojection.<sup>[15](https://mdpi-res.com/d_attachment/instruments/instruments-04-00011/article_deploy/instruments-04-00011-v3.pdf?version=1592915485)</sup> Geological cells push pressure and temperature: the Sleipnir fluid-flow cell operates at confining pressure up to 20 MPa, pore pressure up to 15 MPa, and 20–200 °C with tomograms in under 60 s, while the internally heated Heitt Mjölnir triaxial cell reaches over 30 MPa confining pressure, 500 MPa differential stress, and 350 °C with 3D data in under 10 s.<sup>[16](https://www.ndt.net/article/ctc2022/papers/ICT2022_paper_id248.pdf)</sup> TOMCAT's environments include tri-axial deformation rigs, flow cells, humidity chambers, and electrochemical cells for in situ battery and fuel cell operation.<sup>[12](https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2019.00346/full)</sup>

## Origin

The method grew out of two strands. Fast synchrotron radiography of metal foaming under thermal load, reported by John Banhart and colleagues in [Applied Physics Letters](https://www.edgepedia.org/applied-physics-letters) in 2001, allowed the internal foam structure to be studied during expansion for the first time in such experiments.<sup>[17](https://doi.org/10.1063/1.1350422)</sup> By 2004, synchrotron microtomography at ESRF beamline ID19 was applied in situ to early foaming stages of powder-metallurgical aluminum foam, quantifying pore nucleation with spatial resolution down to one micrometer.<sup>[8](https://www.ndt.net/article/wcndt2004/pdf/materials_characterization/784_baumbach.pdf)</sup> Direct 3D visualization of microstructural evolution during sintering was reported by Dominique Bernard and colleagues in Acta Materialia in 2004.<sup>[18](https://doi.org/10.1016/j.actamat.2004.09.027)</sup> Fast microtomography using high-energy synchrotron radiation was reported by Marco Di Michiel and colleagues in the Review of Scientific Instruments in 2005.<sup>[19](https://doi.org/10.1063/1.1884194)</sup>

On the analysis side, digital volume correlation for 3D strain mapping from X-ray tomography was reported by B. K. Bay and colleagues in Experimental Mechanics in 1999.<sup>[20](https://doi.org/10.1007/bf02323555)</sup> In vivo X-ray cine-tomography for tracking morphological dynamics was reported by Tomy dos Santos Rolo and colleagues in the [Proceedings of the National Academy of Sciences](https://www.edgepedia.org/proceedings-of-the-national-academy-of-sciences) in 2014.<sup>[3](https://pmc.ncbi.nlm.nih.gov/articles/PMC11468671/)</sup> 20 Hz tomography during an in situ tensile test was reported by Eric Maire and colleagues in the International Journal of Fracture in 2016.<sup>[21](https://doi.org/10.1007/s10704-016-0077-y)</sup> In-operando high-speed tomography of lithium-ion batteries during thermal runaway was reported by Donal P. Finegan and colleagues in [Nature Communications](https://www.edgepedia.org/nature-communications) in 2015.<sup>[22](https://doi.org/10.1038/ncomms7924)</sup>

## Variants

**Laboratory versus synchrotron.** [Laboratory](https://www.edgepedia.org/laboratory) exposure times have fallen from days in the 1990s to as low as 20 ms, enabling scan times under a minute, and one laboratory setup reached 3.4 ms exposures for more dynamic 4D CT.<sup>[23](https://beta.iopscience.iop.org/article/10.1088/1361-6501/ac354a)</sup> A rotating-anode micro-CT system with an sCMOS detector has demonstrated full CT acquisition in 132 ms, and 4D imaging of an expanding polymer foam at 2 Hz (510 ms per CT) with 42 µm voxels.<sup>[4](https://discovery.ucl.ac.uk/id/eprint/10198777/1/Endrizzi_131520O%20%282%29.pdf)</sup> For the same spatial resolution, laboratory units acquire about 100 times more slowly than synchrotrons.<sup>[11](https://www.mdpi.com/1996-1944/14/9/2374)</sup>

**Synchrotron tomoscopy.** Time-resolved tomography, quantified in tomograms per second (tps), reached 1000 tps with 1 ms temporal resolution and 7.6 µm resolution at 100 tps (8.2 µm at 1000 tps) at the TOMCAT beamline using the GigaFRoST camera, running for minutes without interruption.<sup>[3](https://pmc.ncbi.nlm.nih.gov/articles/PMC11468671/)</sup> Diffraction-limited storage rings enhance temporal resolution up to one hundred times for a given sample at the same spatial resolution.<sup>[24](https://pmc.ncbi.nlm.nih.gov/articles/PMC11371062/)</sup> [Synchrotron](https://www.edgepedia.org/synchrotron) beamlines offer 0.2–5.0 µm resolution for 0.8–20.0 mm samples, roughly a factor of 5 better than conventional X-ray sources.<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup>

**Nano-CT.** Phase-contrast nano-tomography reaches 27 nm voxels and 360 nm resolution but is restricted to small volumes, from a few micrometers to several hundred micrometers.<sup>[6](https://u-picardie.hal.science/hal-03842611v1/document)</sup>

## Applications

**Batteries** are a major application. In operando imaging showed macroscopic β-sulfur dendrites forming at the end of the charge step of Li/S cells at 0.1 C, and ex situ µ-tomography visualized sulfur dendrites deep inside the carbon cathode.<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup> [In situ](https://www.edgepedia.org/in-situ) synchrotron CT during drying of slurry-cast electrodes links drying dynamics to microstructure and predicts mud crack formation in an energy-intensive, previously empirical manufacturing step.<sup>[25](https://pubs.rsc.org/en/content/articlelanding/2026/eb/d5eb00201j)</sup>

**Metal additive manufacturing** benefits from operando X-ray imaging that elucidates melt pool behavior, keyhole dynamics, and pore formation mechanisms in laser-based powder bed fusion and directed energy deposition.<sup>[26](https://www.nature.com/articles/s43246-024-00699-7)</sup> **Solidification** studies face cooling rates above 10³ K/s, demanding sub-micron resolution and frame rates near 10⁶ Hz; a laser-based in situ solidification kit at TOMCAT used 1–5 ms exposures with 1–3 µm pixel resolution.<sup>[11](https://www.mdpi.com/1996-1944/14/9/2374)</sup> **Sintering and composites** were early targets, from copper-particle sintering to creep damage evolution.<sup>[27](https://onlinelibrary.wiley.com/doi/10.1002/adem.200700254)</sup> **High-temperature electrorefining** has been tracked in 4D, with titanium electrode morphology followed in molten salt at 500 °C at current densities of 0.3 and 0.6 A cm⁻².<sup>[28](https://www.science.org/doi/10.1126/sciadv.abm5678)</sup>

## Limitations and alternatives

Synchrotron in situ tomograms commonly show ring artifacts, beam-hardening cupping, motion-induced blurring, and reduced signal-to-noise from short exposures and specimen movement.<sup>[14](https://arxiv.org/abs/2504.19200)</sup> Dead angles that obscure X-rays during rotation produce bifurcated streak artifacts, which total-variation reconstruction and machine-learning in-painting can mitigate.<sup>[6](https://u-picardie.hal.science/hal-03842611v1/document)</sup> In laboratory systems, focal spot size limits speed: a rule of thumb is that spot size grows by about 1 micron per Watt of source power, and blur becomes noticeable when spot size exceeds voxel size.<sup>[23](https://beta.iopscience.iop.org/article/10.1088/1361-6501/ac354a)</sup>

[Radiation damage](https://www.edgepedia.org/radiation-damage) is a central constraint. In a Li-O₂ nano-XCT experiment, a gas-liquid interphase appeared and propagated during discharge, attributed partly to local electrolyte evaporation from the X-ray beam.<sup>[6](https://u-picardie.hal.science/hal-03842611v1/document)</sup> In nano-tomography of battery materials, the high photon flux needed to minimize motion artifacts during rapid scanning increases beam-induced damage risk in sensitive materials.<sup>[7](https://pubs.rsc.org/en/content/articlehtml/2025/ta/d5ta03471j)</sup> Beam damage appears to significantly affect reaction mechanisms in operando battery studies and should be considered more routinely.<sup>[29](https://royalsocietypublishing.org/rsta/article-pdf/doi/10.1098/rsta.2022.0350/249912/rsta.2022.0350.pdf)</sup> Lithium interacts very weakly with X-rays, making conclusive CT of lithium batteries challenging; neutron tomography is proposed as a complementary technique because it is sensitive to light elements.<sup>[2](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)</sup>

Machine learning is changing both acquisition and analysis. A deep-learning reconstruction method produces high-quality volumes from 101 projections instead of the conventional 1001 in laboratory in situ tensile CT of additively manufactured [Inconel 718](https://www.edgepedia.org/inconel-718), raising sampling from a few scans per experiment to over 20 scans at different load steps and enabling observation of pore growth, coalescence, and shear band formation.<sup>[30](https://www.nist.gov/publications/accelerating-situ-x-ray-tomography-using-sparse-projections-and-deep-learning)</sup>

## References

1. [X-ray computed tomography | Nature Reviews Methods Primers](https://www.nature.com/articles/s43586-021-00015-4)
2. [In operando x-ray tomography for next-generation batteries: a systematic approach to monitor reaction product distribution and transport processes](https://beta.iopscience.iop.org/article/10.1088/0022-3727/49/40/404001)
3. [Tomoscopy: Time-Resolved Tomography for Dynamic Processes in Materials](https://pmc.ncbi.nlm.nih.gov/articles/PMC11468671/)
4. [Sub-second Dynamic X-ray Micro-CT and Fast Phase-sensitive Multi-contrast Micro-CT with a Laboratory Source](https://discovery.ucl.ac.uk/id/eprint/10198777/1/Endrizzi_131520O%20%282%29.pdf)
5. [A Technique for In-Situ Displacement and Strain Measurement with Laboratory-Scale X-Ray Computed Tomography](https://link.springer.com/article/10.1007/s40799-024-00715-y)
6. [In situ nano-XCT of a lithium-oxygen battery with Zernike phase contrast (Tu-cell)](https://u-picardie.hal.science/hal-03842611v1/document)
7. [GenAI-enhanced 4D nano-tomography for advanced battery microstructure analysis (Journal of Materials Chemistry A)](https://pubs.rsc.org/en/content/articlehtml/2025/ta/d5ta03471j)
8. [High Resolution Microtomography and Fast Radiography for Real-Time and In-Situ Characterization of Porous Microstructures](https://www.ndt.net/article/wcndt2004/pdf/materials_characterization/784_baumbach.pdf)
9. [Marcel Beister, Daniel Kolditz, Willi A. Kalender (2012). Iterative reconstruction methods in X-ray CT. Physica Medica.](https://doi.org/10.1016/j.ejmp.2012.01.003)
10. [D. Paganin and colleagues (2002). Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy.](https://doi.org/10.1046/j.1365-2818.2002.01010.x)
11. [Progress on In Situ and Operando X-ray Imaging of Solidification Processes](https://www.mdpi.com/1996-1944/14/9/2374)
12. [Time Resolved in situ X-Ray Tomographic Microscopy Unraveling Dynamic Processes in Geologic Systems](https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2019.00346/full)
13. [Combining X-ray microtomography and three-dimensional digital volume correlation to track microstructure evolution during sintering of copper powder](https://journals.sagepub.com/doi/10.1177/0309324714527588)
14. [Leveraging Modified Ex Situ Tomography Data for Segmentation of In Situ Synchrotron X-Ray Computed Tomography](https://arxiv.org/abs/2504.19200)
15. [The TomoPress: a loading device for in situ synchrotron microtomography (Instruments, MDPI)](https://mdpi-res.com/d_attachment/instruments/instruments-04-00011/article_deploy/instruments-04-00011-v3.pdf?version=1592915485)
16. [4-dimensional in-situ/in-operando µ-CT imaging of geological processes at elevated temperatures and pressures](https://www.ndt.net/article/ctc2022/papers/ICT2022_paper_id248.pdf)
17. [John Banhart and colleagues (2001). Metal foam evolution studied by synchrotron radioscopy. Applied Physics Letters.](https://doi.org/10.1063/1.1350422)
18. [Dominique Bernard and colleagues (2004). First direct 3D visualisation of microstructural evolutions during sintering through X-ray computed microtomography. Acta Materialia.](https://doi.org/10.1016/j.actamat.2004.09.027)
19. [Marco Di Michiel and colleagues (2005). Fast microtomography using high energy synchrotron radiation. Review of Scientific Instruments.](https://doi.org/10.1063/1.1884194)
20. [B. K. Bay and colleagues (1999). Digital volume correlation: Three-dimensional strain mapping using X-ray tomography. Experimental Mechanics.](https://doi.org/10.1007/bf02323555)
21. [Eric Maire and colleagues (2016). 20 Hz X-ray tomography during an in situ tensile test. International Journal of Fracture.](https://doi.org/10.1007/s10704-016-0077-y)
22. [Donal P. Finegan and colleagues (2015). In-operando high-speed tomography of lithium-ion batteries during thermal runaway. Nature Communications.](https://doi.org/10.1038/ncomms7924)
23. [Review of high-speed imaging with lab-based x-ray computed tomography](https://beta.iopscience.iop.org/article/10.1088/1361-6501/ac354a)
24. [New opportunities for time-resolved imaging using diffraction-limited storage rings (ForMAX, MAX IV)](https://pmc.ncbi.nlm.nih.gov/articles/PMC11371062/)
25. [Predicting the formation of mud cracks in Li-ion battery electrodes during the drying process with in situ X-ray computed tomography (EES Batteries, RSC)](https://pubs.rsc.org/en/content/articlelanding/2026/eb/d5eb00201j)
26. [Advancements in operando X-ray techniques for metal additive manufacturing](https://www.nature.com/articles/s43246-024-00699-7)
27. [In-Situ Synchrotron X-Ray Microtomography Studies of Microstructure and Damage Evolution in Engineering Materials](https://onlinelibrary.wiley.com/doi/10.1002/adem.200700254)
28. [A 4D x-ray computer microtomography for high-temperature electrochemistry](https://www.science.org/doi/10.1126/sciadv.abm5678)
29. [Recent developments in X-ray diffraction/scattering computed tomography for materials science](https://royalsocietypublishing.org/rsta/article-pdf/doi/10.1098/rsta.2022.0350/249912/rsta.2022.0350.pdf)
30. [Accelerating in situ X-ray tomography using sparse projections and deep learning (NIST)](https://www.nist.gov/publications/accelerating-situ-x-ray-tomography-using-sparse-projections-and-deep-learning)

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*Topic: Encyclopedia › Physical world and mathematics › Physics › Physics methods, practice, and community › X-ray imaging and tomography*

*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*

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License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license