Synchrotron-Based X-ray Computed Tomography
region that begins at 0% compressive strain and extends to ~20% compressive strain, which corresponds to elastic buckling of the foam ligaments. From ~20% compressive strain to 45% compressive strain the stress greatly increases, indicative of densifi cation (which is evident in the volume rendering in Figure 4 ). Visibly absent from the stress-strain curve is a linear region at low compressive strain, which would correspond to bending of the foam ligaments.
Image noise and ring artifacts in the reconstructed slices
( Figure 2 ) made a direct grayscale-based segmentation of the foam material difficult. Image noise can arise from the perfor- mance of the scintillator, the performance of the magnifying optic, and the performance of the detector. Typically, in laboratory-based CT imaging, the acquisition time for a radiograph is increased to sufficiently reduce image noise and increase the number of X-ray photons penetrating the sample per radiograph. This effectively increases the signal-to-noise ratio, resulting in a favorable reconstructed tomogram. With fast dynamic synchrotron-based CT imaging, increasing the acquisition time by even 1 ms would introduce blurring of the sample (at this strain rate), which is considered a less desired image artifact. Ring artifacts in reconstructed tomograms arise from poor detector performance; specifi- cally, a pixel in the detector may have different sensitivity than surrounding pixels during data acquisition, yielding a false grayscale value in the acquired radiographs. If this false grayscale value is present in the majority of radiographs, the reconstructed tomogram will contain a “ring” of higher or lower grayscale value voxels in the XY orientation (as seen in Figure 2A ). Modern commercially available laboratory- based CT microscopes minimize this type of image artifact by raising and lowering the sample during sample rotation, whereas this would be difficult in fast synchrotron-based CT imaging. To minimize these image artifacts in the reconstruct tomograms, mathematical smoothing filters are often employed (for example, median smoothing filters). For this data set, an edge-preserving smoothing filter was used to smooth the data, which reserves hard edges and smooths or merges soft edges with surrounding features. Figure 5 (top) presents an XY (top-down) slice of the foam
Figure 5 : Top: The same slice as presented in Figure 2A (top left), after applying an edge-preserving smoothing fi lter. After the smoothing step, the tomogram was segmented (middle) for the foam material. The scale bar is in micrometers. Bottom: The grayscale histograms of the tomograms before and after applying the edge-preserving smoothing fi lter.
2015 May •
www.microscopy-today.com
Figure 6 : Volume rendering of the two inner voids of the 3D printed foam shown in Figure 2 at 0% compression (white, translucent) and 29% compression (blue, opaque). The scale bar is in micrometers.
15
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76