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@@ -1,77 +1,37 @@ -# FISTA Reconstruction (Daniil Kazantsev) +# CCPi-Regularisation Toolkit (CCPi-RUT) -# General Description +**Iterative image reconstruction (IIR) methods normally require regularisation to stabilise convergence and make the reconstruction problem more well-posed. +CCPi-RUT is released under Apache 2.0 license and consists of 2D/3D regularisation methods which frequently used for IIR. +The core modules are written in C-OMP and CUDA languages and wrappers for Matlab and Python are provided.** -Software for reconstructing 2D/3D x-ray and neutron tomography datasets. The data can be undersampled, of poor contrast, noisy, and contain various artifacts. This is Matlab and C-omp implementation of iterative model-based algorithms with unconventional data fidelities and with various regularization terms (TV and higher-order LLT). The main optimization problem is solved using FISTA framework [1]. The presented algorithms are FBP, FISTA (Least-Squares), FISTA-LS-TV(LS-Total Variation), FISTA-GH(Group-Huber)-TV, and FISTA-Student-TV. More information about the algorithms can be found in papers [2,3]. Please cite [2] if the algorithms or data used in your research. - -## Requirements/Dependencies: +## Prerequisites: * MATLAB (www.mathworks.com/products/matlab/) - * ASTRA toolbox (https://github.com/astra-toolbox/astra-toolbox) - * C/C++ compiler (run compile_mex in Matlab first to compile C-functions) - -## Package Contents: - -### Demos: - * Demo1: Synthetic phantom reconstruction with noise, stripes and zingers - * DemoRD1: Real data reconstruction from sino_basalt.mat (see Data) - * DemoRD2: Real data reconstruction from sino3D_dendrites.mat (see Data) - -### Data: - * phantom_bone512.mat - a synthetic 2D phantom obtained from high-resolution x-ray scan - * sino_basalt.mat – 2D neutron (PSI) tomography sinogram (slice across a pack of basalt beads) - * sino3D_dendrites.mat – 3D (20 slices) x-ray synchrotron dataset (DLS) of growing dendrites + * Python (ver. 3.5); Cython + * C/C++ compilers + * nvcc compilers -### Main modules: +## Package Contents : - * FISTA_REC.m – Matlab function to perform FISTA-based reconstruction - * FGP_TV.c – C-omp function to solve for the weighted TV term using FGP - * SplitBregman_TV.c – C-omp function to solve for the weighted TV term using Split-Bregman - * LLT_model.c – C-omp function to solve for the weighted LLT [3] term using explicit scheme - * studentst.m – Matlab function to calculate Students t penalty with 'auto-tuning' + * 1. Rudin-Osher-Fatemi Total Variation (explicit PDE minimisation scheme) 2D/3D GPU/CPU [1] + * 2. Fast-Gradient-Projection Total Variation 2D/3D GPU/CPU [2] -### Supplementary: - - * zing_rings_add.m Matlab script to generate proj. data, add noise, zingers and stripes - * my_red_yellowMAP.mat – nice colormap for the phantom - * RMSE.m – Matlab function to calculate Root Mean Square Error - -### Practical advices: - * Full 3D reconstruction provides much better results than 2D. In the case of ring artifacts, 3D is almost necessary - * Depending on data it is better to use TV-LLT combination in order to achieve piecewise-smooth solution. The DemoRD2 shows one possible example when smoother surfaces required. - * L (Lipshitz constant) if tweaked can lead to faster convergence than automatic values - * Students’t penalty is generally quite stable in practice, however some tweaking of L might require for the real data - * You can choose between SplitBregman-TV and FISTA-TV modules. The former is slower but requires less memory (for 3D volume U it can take up to 6 x U), the latter is faster but can take more memory (for 3D volume U it can take up to 11 x U). Also the SplitBregman is quite good in improving contrast. - - ### Compiling: - - It is very important to check that OMP support is activated for the optimal use of all CPU cores. -#### Windows - In Windows enable OMP support, e.g.: -edit C:\Users\Username\AppData\Roaming\MathWorks\MATLAB\R2012b\mexopts.bat -In the file, edit 'OPTIMFLAGS' line as shown below (add /openmp entry at the end): -OPTIMFLAGS = /O2 /Oy- /DNDEBUG /openmp -define and set the number of CPU cores -PROC = feature('numCores'); -PROCS = num2str(PROC); -setenv('OMP_NUM_THREADS', PROCS); % you can check how many CPU's by: getenv -OMP_NUM_THREADS +### Demos: + * --- -#### Linux -In Linux in terminal: export OMP_NUM_THREADS = (the numer of CPU cores) -then run Matlab as normal -to compile with OMP support from Matlab in Windows run: -mex *.cpp CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp" -to compile with OMP support from Matlab in Linux ->rename *cpp to *c and compile: -mex *.c CFLAGS="\$CFLAGS -fopenmp -Wall" LDFLAGS="\$LDFLAGS -fopenmp" +### Installation: +#### Python (conda-build) +``` + export CIL_VERSION=0.9.2 +``` +#### Matlab ### References: -[1] Beck, A. and Teboulle, M., 2009. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences,2(1), pp.183-202. -[2] Kazantsev, D., Bleichrodt, F., van Leeuwen, T., Kaestner, A., Withers, P.J., Batenburg, K.J., 2017. A novel tomographic reconstruction method based on the robust Student's t function for suppressing data outliers, IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING (to appear) +[1] Rudin, L.I., Osher, S. and Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4), pp.259-268. +[2] Beck, A. and Teboulle, M., 2009. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18(11), pp.2419-2434. [3] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590. -[4] Paleo, P. and Mirone, A., 2015. Ring artifacts correction in compressed sensing tomographic reconstruction. Journal of synchrotron radiation, 22(5), pp.1268-1278. -[5] Sidky, E.Y., Jakob, H.J. and Pan, X., 2012. Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm. Physics in medicine and biology, 57(10), p.3065. -D. Kazantsev / Harwell Campus / 16.03.17 +### Acknowledgment: +CCPi-RUT is a product of the [CCPi project](https://pages.github.com/) any questions/comments please e-mail to daniil.kazantsev@manchester.ac.uk |