X-ray computed tomography (CT) is a non-destructive technique for inspecting and analyzing internal structures of samples. In general, x-rays are absorbed or scattered by the sample as the X-rays travel through the sample. The X-rays not absorbed or scattered away are transmitted through the sample and then are detected by a detector system. The image formed at the detector system is known as an x-ray projection. Tomographic volume data sets are reconstructed from a series of these projections at different angles via standard CT reconstruction algorithms. X-ray tomography systems often present these tomographic volumes in two-dimensional, cross-sectional images or “slices” of the tomographic volume data sets.
Some x-ray tomography systems utilize polychromatic X-ray beams to generate the x-ray projections. Polychromatic x-rays sources include x-ray tubes (laboratory sources) or white synchrotron beams, or accelerator-based sources. The advantage of polychromatic x-ray beams is that they are typically more powerful that monochromatic beams for a given source since lossy energy filters are not necessary.
When using a polychromatic beam, the transmitted x-ray intensity is generally no longer proportional to the material thickness since lower energies are absorbed first as the beam traverses the object. As a result, a phenomenon known as beam hardening (BH) often occurs when polychromatic beams are used to generate x-ray projections. Beam hardening is associated with a change in transmitted X-ray spectrum towards higher X-ray energies as the X-rays pass through the sample.
Beam hardening often yields artifacts in tomographic reconstructions with polychromatic X-rays. Typical artifacts generated as a consequence of beam hardening include cupping artifacts and streak artifacts. Beam hardening can be corrected effectively for objects that consist only of one material by linearization of the absorption versus material thickness. This method is not effective for mixed material objects, especially objects containing both low density (low-Z) materials and high density (high-Z) materials, however. Because high-Z elements such as metals or elements with an atomic number higher than 18, such as Iron (Fe) and Gold (Au), absorb much more X-rays than low-Z elements, such as Silicon (Si), Carbon (C), Nitrogen (N) and Oxygen (O), metal element structures typically produce more serious beam hardening artifacts when exposed to X-rays. These artifacts are also known as ‘metal artifacts’. In addition, factors other than beam hardening can cause the creation of metal artifacts such as x-ray scattering, Poisson noise, and motion and edge effects.
Current approaches to metal artifact reduction (MAR) in X-ray CT images typically fall into three groups. The first group, Group 1, uses N-order polynomial fitting to do beam-hardening correction to reduce metal artifacts. Here we call this method ‘BHC (beam-hardening correction)’. The second group, Group 2, focuses on segmentation/subtraction of metals in the projections, followed by completion methods with analytical or iterative algorithms. The third group, or Group 3, uses a spectrum-based or physical modeling approach to statistically and iteratively reduce or suppress the artifacts.
Each of these artifact reduction approaches has drawbacks. Group 1 MAR methods can only reduce artifacts approximately and work properly only for one kind of metal. Group 2 MAR methods typically only provide partial suppression of the artifacts, and can introduce new blurring artifacts around metals in the sample. This is because information about structures in the sample shadowed by the metal are erased. In contrast, Group 3 MAR methods can theoretically reduce or eliminate most metal artifacts and typically achieve better results since they do not erase information present in the projections. The computational efficiency of Group 3 methods is low, however, because of the large number of iterative processing steps required.