TomobarRecon

Summary

TomopyRecon

Process category

Brief description

Computational demand

for typical tomography data

(low, medium, high)

Comment(s)

Reference(s)

Common alternative process(es)

reconstructor

TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software.

medium-high. Note that there is 2D and 3D version of the plugin. 3D is more computationally demanding and doesn't exploit the MPI fully.

ToMoBAR is a library of direct and model-based regularised iterative reconstruction algorithms with a plug-and-play capability. Current Savu wrapper uses the regularised FISTA algorithm

ToMoBAR

AstraReconCpu
AstraReconGpu
TomoPy

Parameters

Brief description

Savu Configurator command

>>> disp -avv

-------------------------------------------------------------------------------------
  TomobarRecon3d(savu.plugins.reconstructions.tomobar_recon_3D)                    
  A wrapper around TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software for     
  advanced iterative image reconstruction using _3D_ capabilities of regularisation. 
  The plugin will run on one cluster node, i.e. it can be slow.                      
    1)               init_vol : None
    Dataset to use as volume initialiser (doesn't currently work with preview).
    2)                preview : []
    A slice list of required frames.
    3)                    log : True
    Take the log of the data before reconstruction (True or False).
    4)     centre_of_rotation : 0.0
    Centre of rotation to use for the reconstruction.
    5)            in_datasets : []
    Create a list of the dataset(s) to process.
    6)       ring_accelerator : 50.0
    Acceleration constant for ring removal (use with care).
    7)            output_size : auto
    The dimension of the reconstructed volume (only X-Y dimension).
    8)          nonnegativity : ENABLE
    Nonnegativity constraint, choose Enable or None.
    9)         regularisation : ROF_TV
    To regularise choose methods ROF_TV, FGP_TV, SB_TV, LLT_ROF, NDF, Diff4th.
   10)             iterations : 15
    Number of outer iterations for FISTA method.
   11)             edge_param : 0.01
    Edge (noise) related parameter, relevant for NDF and Diff4th.
   12)                  ratio : 0.95
    Ratio of the m2asks diameter in pixels to the smallest edge size along given
    axis.
   13)               log_func : np.nan_to_num(-np.log(sino))
    Override the default log function.
   14)   regularisation_parameter2 : 0.005
    Regularisation (smoothing) value for LLT_ROF method.
   15)            NDF_penalty : Huber
    NDF specific penalty type Huber, Perona, Tukey.
   16)              tolerance : 1e-10
    Tolerance to stop outer iterations earlier.
   17)           ordersubsets : 6
    The number of ordered-subsets to accelerate reconstruction.
   18)           out_datasets : []
    Create a list of the dataset(s) to create.
   19)             centre_pad : False
    Pad the sinogram to centre it in order to fill the reconstructed volume ROI for
    asthetic purposes. NB: Only available for selected algorithms and will be ignored
    otherwise. WARNING: This will significantly increase the size of the data and the
    time to compute the reconstruction).
   20)   regularisation_iterations : 400
    The number of regularisation iterations.
   21)   regularisation_parameter : 0.0002
    Regularisation (smoothing) value, higher the value stronger the smoothing effect.
   22)             force_zero : [None, None]
    Set any values in the reconstructed image outside of this range to zero.
   23)              vol_shape : fixed
    Override the size of the reconstuction volume with an integer value.
   24)          converg_const : power
    Lipschitz constant, can be set to a scalar value or automatic calculation using
    power methods.
   25)   time_marching_parameter : 0.002
    Time marching parameter, relevant for (ROF_TV, LLT_ROF, NDF, Diff4th) penalties.
   26)           datafidelity : LS
    Data fidelity, Least Squares (LS) or PWLS.
   27)              outer_pad : False
    Pad the sinogram width to fill the reconstructed volume for asthetic purposes.
    Choose from True (defaults to sqrt(2)), False or float <= 2.1. NB: Only available
    for selected algorithms and will be ignored otherwise. WARNING: This will
    increase the size of the data and the time to compute the reconstruction).
   28)          ring_variable : 0.0
    Regularisation variable for ring removal.

-------------------------------------------------------------------------------------
>>>

Additional notes

For basic information on this process, please use the disp -av (or disp -avv or disp -v[v] <process index>) command in Savu Configurator (see above). The table below is intended to provide some additional notes on a number of selected topics:

Item	Parameter name	Parameter format	Example(s)		Comment(s)
Item	Parameter name	Parameter format	Parameter value	Effect	Comment(s)
1	regularisation_parameter	float	0.0002	Should be chosen for a specific dataset. Higher the value stronger the smoothing effect.	The value depends on the data. If zero is passed, no regularisation will be applied (reconstruction without filtering).
2	regularisation	string	ROF_TV, FGP_TV, SB_TV, LLT_ROF, NDF, Diff4th, TGV	ROF_TV, FGP_TV, SB_TV, NDF - deliver piecewise-constant recovery (regions with uniform intensity) LLT_ROF, Diff4th, TGV - piecewise-smooth recovery.	The plugin uses the CCPi-Regularisation toolkit which is available here: https://github.com/vais-ral/CCPi-Regularisation-Toolkit and based on this paper: https://www.sciencedirect.com/science/article/pii/S2352711018301912
3	iterations	integer	15-20	Less than 10 iterations for the iterative method (FISTA) can deliver a blurry reconstruction	The suggested value is 15 iterations, however the algorithm can stop prematurely based on the tolerance value (see bellow)
4	tolerance	float	1e-10	can stop iterations prematurely when the solution is changing "slowly"
5	regularisation_iterations	integer	400 for OS methods, 70 for non-OS	Less iterations - less effect of filtering.	One needs to iterate "long enough" in order to get to the filtered solution. The number of iterations for regularisation (filtering) method is set to 400/OS number or 400 for non-OS method. It is not harmful to over-iterate, this, however will effect the speed of the algorithm.
6	centre_of_rotation				The default value of the *centre_of_rotation* parameter is 0.0, which normally needs to be manually modified to a more appropriate value or, if VoCentering is used beforehand in the process chain, then this parameter is automatically set to a value determined by this auto-centring process.
7	ordersubsets	integer	6	effects the final solution by accelerating reconstruction process.	This directly effect the number of iterations to run. The high value (> 12), however, can result in the algorithm to diverge.
8	ratio
9	out_datasets
10	centre_pad
11	outer_pad
12	n_iterations
13	force_zero

Usage

There are 2D versions of the tomobar plugin - for 2D and 3D reconstruction. 2D version is fully MPI-ed while 3D work on a single GPU provided. Although 3D version can be significantly slower than 2D, the results are normally much better using 3D versus slice-by-slice 2D. Note, however, the passed data dimensions and the available GPU memory before running tomobar3D, the memory overflow can be easily reached.

Tomography Reconstruction

TomobarRecon

Analytics

Summary

Parameters

Brief description

Additional notes

Usage

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