MicroCT uses X-ray imaging and computed tomography to produce 3D images of very high resolution, with voxel sizes down to 1µm or smaller. MicroCT, which utilizes differences in X-ray attenuation properties of materials to reconstruct 3D structure, differs from conventional CT by combining a much smaller field-of-view with a high resolution detector.
MicroCT is used to study diverse materials including bone, teeth, medical implants, snow, textiles, concrete and precious stones. MicroCT reveals in great detail the internal structure of these materials, such as the trabecular architecture within bone or grain within wood, allowing quantitative analysis of properties such as density and strength.
For more in-depth information about microCT imaging, please refer to the publication list.
There are several different definitions of resolution including spatial resolution, also called 10% MTF resolution, and nominal resolution. Moreover, terms such as voxel size and detectability are also used.
What is resolution?
Resolution is the distance between objects (or cavities) at which they can still be identified as independent from each other in an image (Figure 1). Resolution is usually given as a characteristic size (µm) or a spatial frequency (line pairs per mm).
Figure 1 |
What is a Point Spread Function (PSF)?
A PSF describes how a single infinitesimally small point in the source object is spread out (blurred, smeared) by the imaging apparatus. Typically PSFs have a gauss-bell like shape.
If the PSF is narrow, this indicates less blurring and thus that small objects can still be discerned (better resolution) As two points are brought closer together, their PSFs begin to overlap and eventually they become difficult to discern (Figure 2 and Figure 3).
Figure 2 |
Figure 3 |
What is the Modular Transfer Function (MTF)?
This is the Fourier transform of the PSF. It shows how well different spatial frequencies (line pairs per mm) are transferred to the final image (Figure 4). Typically, MTFs drop off with increasing spatial frequency e.g. as objects get smaller and closer together.
Figure 4. Measurement Transfer Function (MTF) with the 10% MTF level shown as a dotted line. All spatial frequencies with intensities below 10% will be below the resolution of the system. |
How is spatial resolution determined?
Generally, two different methods exist:
What about the Signal to Noise Ratio (SNR)?
There are two general sets of factors determining image resolution.
What does voxel size mean?
Voxel size is the size of a 3D pixel in the rendered image. This can be equated to the nominal resolution of the image.
What is nominal resolution?
Nominal resolution is the smallest possible voxel size of a scanner’s reconstructed image and not to be confused with image resolution or resolution at 10% MTF (see below).
What is the connection between image size and image resolution?
Obviously, the final image resolution can never be better than the voxel size. The voxel size is often chosen in correspondence with the X-ray detector’s pixel size. However, because of other factors, such as the finite spot size of the X-ray source or image reconstruction, resolution is usually further reduced with some information smeared out over neighboring voxels.
How does Scanco determine a scanner's resolution?
A solid aluminum cylinder is scanned and from the reconstructed image, the PSF is assembled from the gradient of the cylinder’s edge. From this PSF the MTF is calculated and the 10% level of maximum MTF criterion is applied. This provides a reproducible measurement of the entire imaging process which is independent of the operator’s level of experience. This method is analogous to ISO recommendations for medical CT.
What is detectability?
Detectability is the smallest size of an object that can still be seen in an image and is not a measure of resolution. When determining detectablility, the imaged object usually has a very high contrast compared to the empty background. The object may appear even if it is smaller than the resolution of the scanner or the voxel size. However, location in the image grid will affect the visibility and estimate of X-ray absorption (Figure 5). The higher the X-ray absorption of the object, the smaller the object can be and still be detected at the same SNR (Figure 6). For instance, a gold particle (high absorption) might be detectable while a glass particle (medium absorption) of the same size might no longer be detectable.
Figure 5: Blurring of beads slightly smaller than the voxel size: The voxel volumes are only partially filled leading to an underestimation of the attenuation coefficient (partial volume effect) and inaccurate rendering of sizes (positions and real shapes left, rendered image right). |
Figure 6: Beads of different attenuation and significantly smaller than the voxel size: Even though smaller than the voxel size, the bead with the higher absorption (black) is still detectable in the image. The bead with lower absorption (gray) gives a feeble signal and might be lost in noise (positions and real shapes left, rendered image right). |
How does the SNR influence image quality?
Overall image quality is influenced by both signal-to-noise ratio (SNR) and resolution. Image quality is furthermore influenced by the original object contrast, on which the SNR depends: For example, bone and water will be easier to distinguish than two soft tissues with almost the same X-ray attenuation e.g. if the signal (tissue contrast) is smaller, SNR is smaller. Also, if there is a lot of noise in the image, object details might be obscured by the image noise e.g. if the noise is higher, SNR is smaller.
What influences SNR?
The signal is essentially the X-ray intensity detected by the image sensor. A stronger X-ray source or moving the detector closer to the source will increase signal intensity. Also, the difference in X-ray attenuation between the background and the object to be imaged will define the relevant signal level. Blood in vessels will have a stronger signal if a contrast agent is added. The noise in the image stems mainly from the statistical physical processes involved: X-ray generation, interaction with the imaged object, and detection. With longer observations (integration time) and more measurements (frame averaging) we become more certain of our detected values (mean value) and the noise is reduced (standard error). Instead of averaging over time and acquired frames one can average over image pixels. During the acquisition neighboring pixels can be binned together – 4 into 1, for example. On the down side, we would have a loss of resolution. Another option is to filter the image noise out during reconstruction. This often results in smeared or fuzzier boarders between objects representing a loss of image resolution but reducing the noise locally.
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Total scan time can be broken down into the time required for the scanner to collect data from the sample (scanning time) and the time required for the computers to reconstruct a 3D image from the raw data (reconstruction time). Several factors influence scanning and reconstruction time.
What factors determine the required scanning time?
What factors determine the required reconstruction time?
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Prefixes
VOX | based on counting voxels |
DT | based on distance transformation (filling structure with spheres) |
TRI | based on triangularization of surface (thus one more interpolation step in comparison to VOX) |
Indices
TV | total volume [mm^3] |
BV | bone volume [mm^3] |
BV/TV | relative bone volume [1] (Percent) |
Conn.D. | connectivity density, normed by TV [1/mm^3] |
SMI | structure model index: 0 for parallel plates, 3 for cylindrical rods |
DT-Tb.N | trabecular number [1/mm] |
DT-Tb.Th | trabecular thickness [mm] |
DT-Tb.Sp | trabecular separation = marrow thickness [mm] |
DT-Tb.1/N.SD | standard deviation of local inverse number [mm] |
DT-Tb.Th.SD | standard deviation of local thicknesses [mm] |
DT.Tb.Sp.SD | standard deviation of local separations [mm] |
DT indices are calculated using distance transformation without assuming anything about the shape of the bone (i.e. without plate model assumption). SDs: with the DT operation, a local thickness/separation for every voxel within bone is calculated. A histogram of local thickness/separation values can be obtained, and a mean and SD of this distribution is calculated. [Explanation for Tb.1/N.SD: First answer: forget about it, take Tb.Sp.SD. Detailed answer: For DT-Tb.N, the histogram is actually of the local separation of the skeletonized structure, thus 1/N. The mean value can be inverted to give Tb.N, but the SD only makes sense as Tb.1/N.SD]
Mean1 | Mean voxel values of everything within volume of interest (mixture of bone and background). If scan was calibrated for bone, then the mean voxel value is in units of Hydroxyapatite density [mg HA/ccm], otherwise in linear attenuation coefficient [1/cm], Hounsfield [HU] or native file number [1] units |
Mean2 | Mean of segmented region, thus lin.att. only of what was considered bone |
Mean3 | Mean of additional region (if applicable) |
TRI-BS | bone surface [mm^2] |
TRI-Tb.N | trab. number, thickness, separation, this time derived from the surface ratio, assuming that the bone is made of parallel plates (MIL method). Corresponds to traditional 2D histomorphometry, but this plate-model assumption leads to a bias in most cases. Scanco recommends to use DT-Tb.N,Th,Sp for truly 3D results. |
TRI-DA | degree of anisotropy, 1= isotropic, › 1 anisotropic by definition DA = length of longest divided by shortest H-vector |
TRI-H1 | shortest vector of the MIL tensor, H1x, H1y H1z its components. |
TRI-H2 | longest vector of the MIL tensor |
TRI-H3 | intermediate vector of the MIL tensor |
TRI-|H1| etc | length of these vectors in [mm] |
El-Size-mm | voxel size in mm, in x, y and z direction |