X-ray Computed Tomography for Materials and Life Science

Webinar summary

Multiscale structure analysis is the process of investigating a material, product, or biological specimen across more than one length scale in order to understand how large-scale structure, intermediate features, and microscopic details relate to one another. Many real-world troubleshooting problems are not confined to one obvious cause. A failed product, for example, may have an issue at the component level, within an internal subassembly, or in the fine structure of the material itself. A complete investigation often requires moving from a broad view of the intact object to progressively higher-resolution views of specific regions or features.

X-ray computed tomography is well suited to this kind of work because it produces three-dimensional internal data without physically sectioning the sample in most cases. A CT scan collects many two-dimensional X-ray projections as the sample rotates, then reconstructs those projections into a digital 3D volume. Once reconstructed, the sample can be virtually sliced, segmented, measured, and inspected from any direction. This makes CT especially useful as a top-down technique: it can show where a problem is located, help determine which scale matters most, and sometimes provide enough information to solve the problem without additional destructive testing.

A respirator example illustrates the value of this approach. A malfunctioning respirator could have a valve alignment problem, a defect in the filter cartridge, an issue with the distribution of filter layers, or a problem with the particles inside the filter media. These possible causes exist at very different scales, from millimeter-scale valve geometry to micron-scale particle porosity. CT can first image the entire respirator to identify gross structural issues, then focus on the valve and filter cartridge at higher resolution, and finally examine individual particles to evaluate morphology, coatings, density differences, and internal pores. Because the resulting data are three-dimensional, the analysis is not limited to a single cross section; internal distributions, voids, density variations, and spatial relationships can be evaluated throughout the volume.

Choosing the right CT system depends mainly on the relationship between field of view, resolution, sample size, feature size, and material composition. A larger field of view allows more of the sample to be scanned at once, but usually at lower resolution. Higher resolution generally requires a smaller field of view or positioning the sample closer to the X-ray source. In cone-beam CT systems, moving the sample closer to the detector provides a larger field of view with less magnification, while moving it closer to the source increases magnification and improves voxel size but reduces the amount of sample captured. Parallel-beam systems work differently because the field of view is more constrained by the beam and optics; higher-magnification lenses can improve resolution, but they further reduce the imaging area.

Material composition is just as important as geometry. CT contrast depends on how strongly different parts of the sample absorb X-rays, which is influenced by density, atomic number, and X-ray energy. Since the density and chemistry of the sample usually cannot be changed, X-ray energy becomes the main adjustable factor. Low-density or low-Z materials may require different conditions than metals, ceramics, or dense composites. A whole respirator scan may require a higher-energy source and a larger field of view, while a small particle scan may require a different system optimized for micron-scale imaging. The practical question is not simply “Which CT system has the best resolution?” but “Which system can image the relevant feature size in the sample size and composition that matter for this problem?”

Sample preparation and mounting strongly affect scan quality. CT is flexible because it does not require a single standardized preparation method, but that flexibility means the user must think carefully about how the sample occupies space during rotation. The relevant sample size is not always the physical length of the object; it is the full rotational envelope needed to keep the sample inside the field of view. An irregularly shaped or tilted sample may require a much larger imaging volume than expected, reducing achievable resolution. Mounting strategy can therefore determine whether a scan reaches the desired resolution.

Several strategies can extend what is possible. If a large object cannot fit within the desired high-resolution field of view, multiple overlapping scans can be collected along the vertical axis and stitched together. This allows a larger volume to be reconstructed at a higher resolution than would be possible in a single scan. Multiple small samples can also be stacked and scanned efficiently, provided the mounting keeps them stable and within the usable field of view.

Different scales introduce different practical challenges. Small, light samples are especially sensitive to motion. A sample that shifts only a few microns during a high-resolution scan can produce blurred or distorted reconstructions, even if that same amount of movement would be invisible in a lower-resolution scan. Good mounting, stabilization, and vibration control are therefore critical when working near the resolution limit. Large, dense, or highly absorbing samples present a different problem: they can produce metal artifacts, dark streaks, beam-hardening effects, and regions where too little signal reaches the detector. These artifacts are not limited to metals; they can occur whenever X-ray attenuation is strong. Mitigating them requires appropriate X-ray energy, filtering, exposure settings, scan geometry, and realistic expectations about what features can be recovered.

The same multiscale CT workflow applies across many fields. In additive manufacturing, CT can reveal pores, cracks, lack-of-fusion defects, trapped powder, and incomplete melting inside printed parts. Higher-resolution scans of the feedstock powder can then be used to evaluate particle morphology, size distribution, and internal porosity, all of which can influence print quality. In batteries, a full-cell scan can locate larger structural defects such as electrode delamination, while a higher-resolution region-of-interest scan can quantify local delamination thickness, pore structure, and electrode packing without cutting open the cell. In life science, a whole-organism scan can show overall skeletal or anatomical structure, while a targeted high-resolution scan of a skull or other region can reveal taxonomically important details such as tooth shape. In carbon-fiber-reinforced polymers, lower-resolution scans can show the arrangement of composite layers, while higher-resolution scans can examine fiber-polymer bonding, cracks, and debonding.

Once CT data are reconstructed, segmentation becomes central to quantitative analysis. Individual phases, particles, pores, fibers, or defects must be identified as sets of voxels. After segmentation, their volume, size distribution, morphology, and spatial relationships can be measured. When particles touch or overlap, watershed segmentation and related methods can help separate individual objects. Software tools such as Dragonfly can support this type of 3D segmentation, measurement, and correlation.

Even when CT does not completely eliminate destructive preparation, it still adds significant value. In some cases, a sample must be cut down simply to fit the instrument or to reach the resolution needed for the feature of interest. That preparation is often less destructive than traditional polishing, sectioning, or repeated physical slicing, and the reward is a complete 3D dataset rather than a single 2D section. CT data can also guide follow-up techniques such as SEM, EDS, nanoindentation, or spectroscopy by showing exactly where to section or analyze, reducing guesswork and sampling bias.

Correlation between low-resolution overview scans and high-resolution region-of-interest scans can be done by keeping the sample in place and changing imaging conditions, or by aligning datasets afterward using visible internal features. When the sample contains recognizable layers, pores, defects, or boundaries, the datasets can be overlaid and registered visually or with software assistance. Alignment becomes harder when the structure is highly uniform and lacks distinctive reference features.

Overall, multiscale X-ray CT provides a practical way to move from broad inspection to targeted high-resolution analysis while preserving three-dimensional context. Its strength is not that one scan answers every question, but that CT can connect scales: the whole object, the region of interest, and the fine structural feature can all be examined in a related 3D framework. That makes it valuable for troubleshooting, quality control, failure analysis, research, and process optimization across materials science, manufacturing, batteries, composites, filtration systems, and biological specimens.