@@ -10,7 +10,7 @@ LabDCT is a novel technique for 3D grain mapping using lab X-ray source, inspire
3) 'gpu_cuda_comp' - run on NVIDIA gpu and require CUDA driver, fast and robust. Suitable for projections with serious spot overlapping;
4) 'gpu_cuda_index_compete' - run on NVIDIA gpu and require CUDA driver, based on brute force indexing and competing completeness for assign orientation for un-indexed voxels. However, this method has not be optimized to be robust enough yet and thus so far not recommended to use.
# How to use the code?
# How to use the code? Generally, 4 steps:
## 1) Prepare for recontruction
- run [get_spots.m](https://gricad-gitlab.univ-grenoble-alpes.fr/TomoX_SIMaP/GrainRecon/-/blob/master/get_spots.m) for spot segmentation from LabDCT projections;
- run [get_tomo_slices_create_h5.m](https://gricad-gitlab.univ-grenoble-alpes.fr/TomoX_SIMaP/GrainRecon/-/blob/master/get_tomo_slices_create_h5.m) to obtain an hdf5 file for tomo volume mask;
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@@ -38,7 +38,7 @@ Examples of bash file for submitting jobs to ESRF slurm cluster:
sbatch go_SLURM_gpu_single_job.slurm
```
# Example
# Example dataset
Three examples are available in [./Examples](https://gricad-gitlab.univ-grenoble-alpes.fr/TomoX_SIMaP/GrainRecon/-/tree/master/Examples):
1) a virtual Fe sample containing 6 grains in a magnified geometry; <br>
2) a virtual Fe sample containing 144 grains in both Laue-focusing and magnified geometries; <br>
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@@ -54,7 +54,7 @@ A mirror site is accessible [here](https://github.com/haixingfang/GrainRecon) on
# Reference
A manuscript has been prepared and now is under review:<br>
H. Fang, W. Ludwig, P. Lhuissier, Reconstruction algorithms for grain mapping by laboratory X-ray diffraction contrast tomography (in review).<br>
Please cite this article if you use or get inspired by the code presented here.
Please cite this article if you use or get inspired by the code presented here.<br>
