tomopy.recon.rotation

Module for functions related to finding axis of rotation.

Functions:

find_center(tomo, theta[, ind, init, tol, ...])

Find rotation axis location.

find_center_vo(tomo[, ind, smin, smax, ...])

Find rotation axis location using Nghia Vo's method.

find_center_pc(proj1, proj2[, tol, rotc_guess])

Find rotation axis location by finding the offset between the first projection and a mirrored projection 180 degrees apart using phase correlation in Fourier space.

write_center(tomo, theta[, dpath, ...])

Save images reconstructed with a range of rotation centers.
tomopy.recon.rotation.find_center(tomo, theta, ind=None, init=None, tol=0.5, mask=True, ratio=1.0, sinogram_order=False)[source]

Find rotation axis location.

The function exploits systematic artifacts in reconstructed images due to shifts in the rotation center. It uses image entropy as the error metric and ‘’Nelder-Mead’’ routine (of the scipy optimization module) as the optimizer [B12].

Parameters

  • tomo (ndarray) – 3D tomographic data.

  • theta (array) – Projection angles in radian.

  • ind (int, optional) – Index of the slice to be used for reconstruction.

  • init (float) – Initial guess for the center.

  • tol (scalar) – Desired sub-pixel accuracy.

  • mask (bool, optional) – If True, apply a circular mask to the reconstructed image to limit the analysis into a circular region.

  • ratio (float, optional) – The ratio of the radius of the circular mask to the edge of the reconstructed image.

  • sinogram_order (bool, optional) – Determins whether data is a stack of sinograms (True, y-axis first axis) or a stack of radiographs (False, theta first axis).

Returns

float – Rotation axis location.

tomopy.recon.rotation.find_center_pc(proj1, proj2, tol=0.5, rotc_guess=None)[source]

Find rotation axis location by finding the offset between the first projection and a mirrored projection 180 degrees apart using phase correlation in Fourier space. The phase_cross_correlation function uses cross-correlation in Fourier space, optionally employing an upsampled matrix-multiplication DFT to achieve arbitrary subpixel precision. [B16].

Parameters

  • proj1 (ndarray) – 2D projection data.

  • proj2 (ndarray) – 2D projection data.

  • tol (scalar, optional) – Subpixel accuracy

  • rotc_guess (float, optional) – Initual guess value for the rotation center

Returns

float – Rotation axis location.

tomopy.recon.rotation.find_center_vo(tomo, ind=None, smin=-50, smax=50, srad=6, step=0.25, ratio=0.5, drop=20, ncore=None)[source]

Find rotation axis location using Nghia Vo’s method. [B25].

Parameters

  • tomo (ndarray) – 3D tomographic data or a 2D sinogram.

  • ind (int, optional) – Index of the slice to be used for reconstruction.

  • smin, smax (int, optional) – Coarse search radius. Reference to the horizontal center of the sinogram.

  • srad (float, optional) – Fine search radius.

  • step (float, optional) – Step of fine searching.

  • ratio (float, optional) – The ratio between the FOV of the camera and the size of object. It’s used to generate the mask.

  • drop (int, optional) – Drop lines around vertical center of the mask.

  • ncore (int, optional) – Number of cores that will be assigned to jobs.

Returns

float – Rotation axis location.

tomopy.recon.rotation.mask_empty_slice(tomo, threshold=0.25)[source]

Generate a mask to indicate whether current slice contains sample

At APS 1ID, some of the projection images contains large empty area above the sample, resulting in empty layers.

Parameters

  • tomo (ndarray) – 3D tomographic data.

  • threshold (float, optional) – determine whether a layer is considered to be empty

Returns

nparray – a mask indicate the emptyness of each layer

tomopy.recon.rotation.write_center(tomo, theta, dpath='tmp/center', cen_range=None, ind=None, mask=False, ratio=1.0, sinogram_order=False, algorithm='gridrec', filter_name='parzen')[source]

Save images reconstructed with a range of rotation centers.

Helps finding the rotation center manually by visual inspection of images reconstructed with a set of different centers.The output images are put into a specified folder and are named by the center position corresponding to the image.

Parameters

  • tomo (ndarray) – 3D tomographic data.

  • theta (array) – Projection angles in radian.

  • dpath (str, optional) – Folder name to save output images.

  • cen_range (list, optional) – [start, end, step] Range of center values.

  • ind (int, optional) – Index of the slice to be used for reconstruction.

  • mask (bool, optional) – If True, apply a circular mask to the reconstructed image to limit the analysis into a circular region.

  • ratio (float, optional) – The ratio of the radius of the circular mask to the edge of the reconstructed image.

  • sinogram_order (bool, optional) – Determins whether data is a stack of sinograms (True, y-axis first axis) or a stack of radiographs (False, theta first axis).

  • algorithm ({str, function}) – One of the following string values.

    ‘art’

    Algebraic reconstruction technique [B2].

    ‘bart’

    Block algebraic reconstruction technique.

    ‘fbp’

    Filtered back-projection algorithm.

    ‘gridrec’

    Fourier grid reconstruction algorithm [B5], [B23].

    ‘mlem’

    Maximum-likelihood expectation maximization algorithm [B3].

    ‘osem’

    Ordered-subset expectation maximization algorithm [B18].

    ‘ospml_hybrid’

    Ordered-subset penalized maximum likelihood algorithm with weighted linear and quadratic penalties.

    ‘ospml_quad’

    Ordered-subset penalized maximum likelihood algorithm with quadratic penalties.

    ‘pml_hybrid’

    Penalized maximum likelihood algorithm with weighted linear and quadratic penalties [B19].

    ‘pml_quad’

    Penalized maximum likelihood algorithm with quadratic penalty.

    ‘sirt’

    Simultaneous algebraic reconstruction technique.

    ‘tv’

    Total Variation reconstruction technique [B8].

    ‘grad’

    Gradient descent method with a constant step size

    ‘tikh’

    Tikhonov regularization with identity Tikhonov matrix.

filter_namestr, optional

Name of the filter for analytic reconstruction.

‘none’

No filter.

‘shepp’

Shepp-Logan filter (default).

‘cosine’

Cosine filter.

‘hann’

Cosine filter.

‘hamming’

Hamming filter.

‘ramlak’

Ram-Lak filter.

‘parzen’

Parzen filter.

‘butterworth’

Butterworth filter.

‘custom’

A numpy array of size next_power_of_2(num_detector_columns)/2 specifying a custom filter in Fourier domain. The first element of the filter should be the zero-frequency component.

‘custom2d’

A numpy array of size num_projections*next_power_of_2(num_detector_columns)/2 specifying a custom angle-dependent filter in Fourier domain. The first element of each filter should be the zero-frequency component.