mantidimaging.core.reconstruct.astra_recon module¶
- class mantidimaging.core.reconstruct.astra_recon.AstraRecon[source]¶
Bases:
mantidimaging.core.reconstruct.base_recon.BaseRecon
- static find_cor(images: mantidimaging.core.data.images.Images, slice_idx: int, start_cor: float, recon_params: mantidimaging.core.utility.data_containers.ReconstructionParameters) float [source]¶
Find the best CoR for this slice by maximising the squared sum of the reconstructed slice.
Larger squared sum -> bigger deviance from the mean, i.e. larger distance between noise and data
- static full(images: mantidimaging.core.data.images.Images, cors: List[mantidimaging.core.utility.data_containers.ScalarCoR], recon_params: mantidimaging.core.utility.data_containers.ReconstructionParameters, progress: Optional[mantidimaging.core.utility.progress_reporting.progress.Progress] = None) mantidimaging.core.data.images.Images [source]¶
Performs a volume reconstruction using sample data provided as sinograms.
- Parameters
images – Array of sinogram images
cors – Array of centre of rotation values
proj_angles – Array of projection angles
recon_params – Reconstruction Parameters
progress – Optional progress reporter
- Returns
3D image data for reconstructed volume
- static single_sino(sino: numpy.ndarray, cor: mantidimaging.core.utility.data_containers.ScalarCoR, proj_angles: mantidimaging.core.utility.data_containers.ProjectionAngles, recon_params: mantidimaging.core.utility.data_containers.ReconstructionParameters, progress: Optional[mantidimaging.core.utility.progress_reporting.progress.Progress] = None) numpy.ndarray [source]¶
Reconstruct a single sinogram
- Parameters
sino – The 2D sinogram as a numpy array
cor – Center of rotation for parallel geometry. It will be converted to vector geometry before reconstructing
proj_angles – Projection angles
recon_params – Reconstruction parameters to configure which algorithm/filter/etc is used
- Returns
2D image data for reconstructed slice