3D µCT images of specimens of carbon fiber reinforced polyamide 6 plaque, fiber orientation tensor data of these images, and three Python code files for two different algebraic and one machine learning based tensor interpolation algorithms

In the folder "code" there are three Python scripts. The "component_averaging_method.py" and the "decomposition_method.py" work the same: The script needs an input .txt-file with coordinates and the corresponding fiber orientation tensors (the example used in the publication is given (file "Input_file_FOT.txt")). After running the code you are asked in the console for the name of the output file and for lower and upper x and y limit, which are 1 and 13, respectively, in the given case. The scripts then calculate the fiber orientation tensors at all missing positions with the respective method, which are then written into a MATLAB file (which is named the way you input in the console). This MATLAB file is structured in a way that the fiber orientation tensors can be plotted directly with the tensor glyph visualization function of Barmpoutis ("plotDTI") given in the abstract.
The Jupyter Notebook "ANN_method.ipynb" works a bit differently as it is an artificial neural network. There, .csv-files are needed as input data. The components of the tensors are given to the network in separate files and the coordinates of the positions in another separate .csv-file. This is all documented in the paper as well. The output again is a .csv-file that has to be transferred into MATLAB if users want to use the same visualization function.

The folder "scans_and_FOT" includes all nine scans and respective fiber orientation tensors used for the publication. The scans are given as .mhd- and .raw-files, the orientation tensors are given in the .dat-files. To generate the fiber orientation tensors from the images, the code by Pinter et al., which is given in the abstract, was used. This C++ code writes out a vector valued image with the orientations per voxel. From this, again with another MATLAB file, which composes the orientation tensor from the vector-valued image, these .dat files can be generated. As this is not the main focus of the publication, and the functionality of the python scripts can be verified with the given orientation tensors, this MATLAB script is not part of this dataset.

Please consider the paper or contact the author Juliane Blarr for further questions.