pytomography.algorithms.fbp
#
This module contains classes that implement filtered back projection reconstruction algorithms.
Module Contents#
Classes#
Implementation of filtered back projection reconstruction \(\hat{f} = \frac{\pi}{N_{\text{proj}}} \mathcal{R}^{-1}\mathcal{F}^{-1}\Pi\mathcal{F} g\) where \(N_{\text{proj}}\) is the number of projections, \(\mathcal{R}\) is the 3D radon transform, \(\mathcal{F}\) is the 2D Fourier transform (applied to each projection seperately), and \(\Pi\) is the filter applied in Fourier space, which is by default the ramp filter. |
- class pytomography.algorithms.fbp.FilteredBackProjection(image, angles, filter=None)[source]#
Implementation of filtered back projection reconstruction \(\hat{f} = \frac{\pi}{N_{\text{proj}}} \mathcal{R}^{-1}\mathcal{F}^{-1}\Pi\mathcal{F} g\) where \(N_{\text{proj}}\) is the number of projections, \(\mathcal{R}\) is the 3D radon transform, \(\mathcal{F}\) is the 2D Fourier transform (applied to each projection seperately), and \(\Pi\) is the filter applied in Fourier space, which is by default the ramp filter.
- Parameters:
image (torch.Tensor) – image data \(g\) to be reconstructed
angles (Sequence) – Angles corresponding to each projection in the image/
filter (Callable, optional) – Additional Fourier space filter (applied after Ramp Filter) used during reconstruction.