Docs for CIL

docs/user_guide/reconstructions/algorithms.rst

@@ -3,7 +3,7 @@
 Reconstruction Algorithms
 =========================
 
-FBP_CUDA and SIRT_CUDA
+ASTRA Toolbox
 ----------------------
 These filters are used from the ASTRA Toolbox package.
 
@@ -21,20 +21,23 @@ centres of rotation for each slice. This means manual rotation of the images to
 The vector creation code was re-used from `ToMoBAR <https://github.com/dkazanc/ToMoBAR/blob/master/src/Python/tomobar/supp/astraOP.py#L20-L70>`_.
 
 FBP_CUDA
---------
+^^^^^^^^
 This algorithm has the option to use different filters to improve reconstruction quality.
 
 For an evaluation of the filters please go to the :ref:`Reconstruction Filters` page.
 
 SIRT_CUDA
----------
+^^^^^^^^^
 Instead of filters for improving the reconstruction quality, this filter uses number of iterations.
 
 Have a look at our refine iterations tool on the Reconstruct tab of the Reconstruction window that
 should assist you in finding the optimal number of rotations for your data.
 
+TomoPy
+------
+
 gridrec
--------
+^^^^^^^
 This filter is from the TomoPy package.
 
 The original documentation for gridrec can be found `here <https://tomopy.readthedocs.io/en/latest/api/tomopy.recon.algorithm.html#module-tomopy.recon.algorithm>`_.
@@ -45,3 +48,23 @@ From the TomoPy documentation, these are the paper references for this filter:
 
 - Dowd BA, Campbell GH, Marr RB, Nagarkar VV, Tipnis SV, Axe L, and Siddons DP. Developments in synchrotron x-ray computed microtomography at the national synchrotron light source. In Proc. SPIE, volume 3772, 224–236. 1999.
 - Rivers ML. Tomorecon: high-speed tomography reconstruction on workstations using multi-threading. In Proc. SPIE, volume 8506, 85060U–85060U–13. 2012.
+
+Core Imaging Library
+--------------------
+
+The `Core Imaging Library (CIL) <https://www.ccpi.ac.uk/cil>`_ is an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets.
+
+PDHG-TV
+^^^^^^^
+
+Primal Dual Hybrid Gradient optimiser with Total Variation regularisation. This is an advanced iterative optimised reconstruction algorithm, described in the paper below. The user can adjust regularisation parameter alpha, the figure shows a 2 values of alpha for a reconstructed slice.
+
+.. figure:: ../../_static/pdhg_tv_alpha.png
+    :alt: PDHG-TV with alpha=0.1 on the left and alpha=10 on the right
+    :width: 90%
+    :align: center
+
+    PDHG-TV with alpha=0.1 on the left and alpha=10 on the right
+
+
+- Jørgensen JS et al. 2021 `Core Imaging Library Part I: a versatile python framework for tomographic imaging <https://doi.org/10.1098/rsta.2020.0192>`_. Phil. Trans. R. Soc. A 20200192. Code.