Image Processing and Analysis:
All images were reconstructed from 2D-slice datasets into 3D volumes, processed and analyzed using a workflow coded in an in-house Mathematica notebook. Figure 2 depicts the workflow developed. This workflow includes:
  1. MRI-slice selection: In this step, slices outside the region of interest are visually selected out. This allows maximizing useful information to render 3D volumes.
  2. 3D rendering: The remaining slides from step 1 are used to render a 3D image. An automated algorithm in Mathematica is used to this end.
  3. Cropping: Cartesian and cylindrical cropping methods are employed to remove unwanted regions containing elevated levels of noise, particularly from volumes with essentially no signal.
  4. Filtering: Sharpening, smoothing and Gaussian filters are applied to prepare the 3D images for binarization and thereby clustering analysis.
  5. Binarization and segmentation: A binary image is produced through the selection of an adequate threshold value. This step can be conducted automatically through available library functions in Mathematica. However, in this work, we explored the threshold selection process by comparison with alternate sources of data, e.g. gravimetric porosity determination. More on this is discussed in the Results and Discussion section.
  6. Cluster analysis. In this step, morphological components (different pore systems that are isolated from each other) are clustered and the overall porosity is calculated. The binary image is dependent on choosing a threshold value that effectively represents the pore structure without diluting the signal with low pixel values.