7.3.1. algotom.rec.reconstruction
Module of FFT-based reconstruction methods in the reconstruction stage:
Filtered back-projection (FBP) method for GPU (using numba and cuda) and CPU.
Direct Fourier inversion (DFI) method.
Wrapper for Astra-Toolbox reconstruction methods (optional)
Wrapper for Tomopy-gridrec reconstruction method (optional)
Center-of-rotation determination using slice metrics.
Functions:
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Apply the FBP (filtered back-projection) reconstruction method to a sinogram-image or a chunk of sinogram-images. |
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Apply the DFI (direct Fourier inversion) reconstruction method (Ref. |
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Apply the gridrec method to a sinogram-image or a chunk of sinogram-images. |
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Wrapper of reconstruction methods implemented in the astra toolbox package. |
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Find the center-of-rotation (COR) using metrics of reconstructed slices at different CORs. |
- algotom.rec.reconstruction.make_smoothing_window(filter_name, width)[source]
Make a 1d smoothing window.
- Parameters
filter_name ({“hann”, “bartlett”, “blackman”, “hamming”, “nuttall”, “parzen”, “triang”}) – Window function used for filtering.
width (int) – Width of the window.
- Returns
array_like – 1D array.
- algotom.rec.reconstruction.make_2d_ramp_window(height, width, filter_name=None)[source]
Make the 2d ramp window (in the Fourier space) by repeating the 1d ramp window with the option of adding a smoothing window.
- Parameters
height (int) – Height of the window.
width (int) – Width of the window.
filter_name ({None, “hann”, “bartlett”, “blackman”, “hamming”, “nuttall”, “parzen”, “triang”}) – Name of a smoothing window used.
- Returns
complex ndarray – 2D array.
- algotom.rec.reconstruction.apply_ramp_filter(sinogram, ramp_win=None, filter_name=None, pad=None, pad_mode='edge')[source]
Apply the ramp filter to a sinogram with the option of adding a smoothing filter.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
ramp_win (complex ndarray or None) – Ramp window in the Fourier space.
filter_name ({None, “hann”, “bartlett”, “blackman”, “hamming”, “nuttall”, “parzen”, “triang”}) – Name of a smoothing window used.
pad (int or None) – To apply padding before the FFT. The value is set to 10% of the image width if None is given.
pad_mode (str) – Padding method. Full list can be found at numpy_pad documentation.
- Returns
array_like – Filtered sinogram.
- algotom.rec.reconstruction.back_projection_gpu(recon, sinogram, angles, xlist, center, sino_height, sino_width)
Implement the back-projection algorithm using GPU.
- Parameters
recon (array_like) – Square array of zeros. Initialized reconstruction-image.
sinogram (array_like) – 2D array. (Filtered) sinogram image.
angles (array_like) – 1D array. Angles (radian) corresponding to the sinogram.
xlist (array_like) – 1D array. Distances of the integration lines to the image center.
center (float) – Center of rotation.
sino_height (int) – Height of the sinogram image.
sino_width (int) – Width of the sinogram image.
- Returns
recon (array_like) – Note that this is the GPU kernel function, i.e. no need of “return”.
- algotom.rec.reconstruction.back_projection_gpu_chunk(recons, sinograms, angles, xlist, center, sino_height, sino_width, num_sino)
Implement the back-projection algorithm for a chunk of sinograms using GPU. Axis of a sinogram/slice in the 3D array is 1.
- Parameters
recons (array_like) – 3D array of zeros. Initialized reconstruction-images.
sinograms (array_like) – 3D array. (Filtered) sinogram images.
angles (array_like) – 1D array. Angles (radian) corresponding to a sinogram.
xlist (array_like) – 1D array. Distances of the integration lines to the image center.
center (float) – Center of rotation.
sino_height (int) – Height of the sinogram image.
sino_width (int) – Width of the sinogram image.
num_sino (int) – Number of sinograms.
- Returns
recons (array_like) – Reconstructed images.
- algotom.rec.reconstruction.back_projection_cpu(sinogram, angles, xlist, center)
Implement the back-projection algorithm using CPU.
- Parameters
sinogram (array_like) – 2D array. (Filtered) sinogram image.
angles (array_like) – 1D array. Angles (radian) corresponding to the sinogram.
xlist (array_like) – 1D array. Distances of the integration lines to the image center.
center (float) – Center of rotation.
- Returns
recon (array_like) – Square array. Reconstructed image.
- algotom.rec.reconstruction.fbp_reconstruction(sinogram, center, angles=None, ratio=1.0, ramp_win=None, filter_name='hann', pad=None, pad_mode='edge', apply_log=True, gpu=True, block=(16, 16), ncore=None)[source]
Apply the FBP (filtered back-projection) reconstruction method to a sinogram-image or a chunk of sinogram-images. Angular axis is 0. If input is 3D array, the slicing axis of sinograms must be 1, e.g. data[:, index, :].
- Parameters
sinogram (array_like) – 2D/3D array. Sinogram image.
center (float) – Center of rotation.
angles (array_like, optional) – 1D array. List of angles (in radian) corresponding to the sinogram.
ratio (float, optional) – To apply a circle mask to the reconstructed image.
ramp_win (complex ndarray, optional) – Ramp window in the Fourier space. Generated if None.
filter_name ({None, “hann”, “bartlett”, “blackman”, “hamming”, “nuttall”, “parzen”, “triang”}) – Apply a smoothing filter.
pad (int, optional) – To apply padding before the FFT. The value is set to 10% of the image width if None is given.
pad_mode (str, optional) – Padding method. Full list can be found at numpy_pad documentation.
apply_log (bool, optional) – Apply the logarithm function to the sinogram before reconstruction.
gpu (bool, optional) – Use GPU for computing if True.
block (tuple of two integer-values, optional) – Size of a GPU block. E.g. (8, 8), (16, 16), (32, 32), …
ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.
- Returns
array_like – Square array. Reconstructed image.
- algotom.rec.reconstruction.generate_mapping_coordinate(width_sino, height_sino, width_rec, height_rec)[source]
Calculate coordinates in the sinogram space from coordinates in the reconstruction space (in the Fourier domain). They are used for the DFI (direct Fourier inversion) reconstruction method.
- Parameters
width_sino (int) – Width of a sinogram image.
height_sino (int) – Height of a sinogram image.
width_rec (int) – Width of a reconstruction image.
height_rec (int) – Height of a reconstruction image.
- Returns
r_mat (array_like) – 2D array. Broadcast of the r-coordinates.
theta_mat (array_like) – 2D array. Broadcast of the theta-coordinates.
- algotom.rec.reconstruction.dfi_reconstruction(sinogram, center, angles=None, ratio=1.0, filter_name='hann', pad_rate=0.25, pad_mode='edge', apply_log=True, ncore=None)[source]
Apply the DFI (direct Fourier inversion) reconstruction method (Ref. [1]) to a sinogram-image or a chunk of sinogram-images. Angular axis is 0. If input is 3D array, the slicing axis of sinograms must be 1, e.g. data[:, index, :].
- Parameters
sinogram (array_like) – 2D/3D array. Sinogram image.
center (float) – Center of rotation.
angles (array_like) – 1D array. List of angles (in radian) corresponding to the sinogram.
ratio (float) – To apply a circle mask to the reconstructed image.
filter_name ({None, “hann”, “bartlett”, “blackman”, “hamming”, “nuttall”, “parzen”, “triang”}) – Apply a smoothing filter.
pad_rate (float) – To apply padding before the FFT. The padding width equals to (pad_rate * image_width).
pad_mode (str) – Padding method. Full list can be found at numpy_pad documentation.
apply_log (bool) – Apply the logarithm function to the sinogram before reconstruction.
ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.
- Returns
array_like – Square array. Reconstructed image.
References
- algotom.rec.reconstruction.gridrec_reconstruction(sinogram, center, angles=None, ratio=1.0, filter_name='shepp', apply_log=True, pad=100, filter_par=0.9, ncore=1)[source]
Apply the gridrec method to a sinogram-image or a chunk of sinogram-images. Angular axis is 0. If input is 3D array, the slicing axis of sinograms must be 1, e.g. data[:, index, :]. This is the wrapper of the gridrec method implemented in the Tomopy package: https://tomopy.readthedocs.io/en/latest/api/tomopy.recon.algorithm.html. Users must install Tomopy before using this function.
- Parameters
sinogram (array_like) – 2D/3D array. Sinogram image.
center (float) – Center of rotation.
angles (array_like) – 1D array. List of angles (radian) corresponding to the sinogram.
ratio (float) – To apply a circle mask to the reconstructed image.
filter_name (str or None) – Apply a smoothing filter. Full list is at: https://github.com/tomopy/tomopy/blob/master/source/tomopy/recon/algorithm.py
apply_log (bool) – Apply the logarithm function to the sinogram before reconstruction.
pad (bool or int) – Apply edge padding to the nearest power of 2.
ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.
- Returns
array_like – Square array.
- algotom.rec.reconstruction.astra_reconstruction(sinogram, center, angles=None, ratio=1.0, method='FBP_CUDA', num_iter=1, filter_name='hann', pad=None, apply_log=True, ncore=1)[source]
Wrapper of reconstruction methods implemented in the astra toolbox package. https://www.astra-toolbox.com/docs/algs/index.html Users must install Astra Toolbox before using this function. Apply the method to a sinogram-image or a chunk of sinogram-images. Angular axis is 0. If input is 3D array, the slicing axis of sinograms must be 1, e.g. data[:, index, :]
- Parameters
sinogram (array_like) – 2D/3D array. Sinogram image.
center (float) – Center of rotation.
angles (array_like) – 1D array. List of angles (radian) corresponding to the sinogram.
ratio (float) – To apply a circle mask to the reconstructed image.
method (str) – Reconstruction algorithms. For CPU: ‘FBP’, ‘SIRT’, ‘SART’, ‘ART’, and ‘CGLS’. For GPU: ‘FBP_CUDA’, ‘SIRT_CUDA’, ‘SART_CUDA’, and ‘CGLS_CUDA’.
num_iter (int) – Number of iterations if using iteration methods.
filter_name (str or None) – Apply filter if using FBP method. Options: ‘ram-lak’, ‘hamming’, ‘hann’, ‘lanczos’, ‘kaiser’, ‘parzen’,…
pad (int) – Padding to reduce the side effect of FFT.
apply_log (bool) – Apply the logarithm function to the sinogram before reconstruction.
- Returns
array_like – Square array.
- algotom.rec.reconstruction.find_center_based_slice_metric(sinogram, start, stop, step=0.5, radius=2, zoom=0.5, method='dfi', gpu=False, angles=None, ratio=1.0, filter_name='hann', apply_log=False, ncore=None, sigma=3, metric_function=None, **kwargs)[source]
Find the center-of-rotation (COR) using metrics of reconstructed slices at different CORs. The entropy of histogram (Ref. [1]) is used by default if the metric-function is set to None. If customized metrics are used, the minimum value must be corresponding to the best center.
- Parameters
sinogram (array_like) – 2D array. Sinogram image.
start (float) – Starting point for searching CoR.
stop (float) – Ending point for searching CoR.
step (float) – Sub-pixel searching step.
radius (float) – Searching range with the sub-pixel step.
zoom (float) – To resize the sinogram for fast coarse-searching. For example, 0.5 <=> reduce the size of the image by half.
method ({“dfi”, “gridrec”, “fbp”, “astra”}) – To select a backend method for reconstruction.
gpu (bool, optional) – Use GPU for computing if True.
angles (array_like, optional) – 1D array. List of angles (in radian) corresponding to the sinogram.
ratio (float, optional) – To apply a circle mask to the reconstructed image.
filter_name ({None, “hann”, “bartlett”, “blackman”, “hamming”, “nuttall”, “parzen”, “triang”}) – Apply a smoothing filter before reconstruction.
apply_log (bool, optional) – Apply the logarithm function to the sinogram before reconstruction.
ncore (int or None) – Number of cpu-cores used for computing. Automatically selected if None.
sigma (int) – Denoising the sinogram before reconstruction. Should be set to 0 for noise-free data (simulation).
metric_function (obj) – To apply a customized function for calculating metric going with keyword arguments.
- Returns
float – Center-of-rotation.
References