This project is supported by Dr. Daniil Kazantsev and aimed at development of novel iterative reconstruction solutions for synthetic and real tomographic measurements (mainly a synchrotron and laboratory-based sources). Special attention is dedicated to existing and novel model based iterative reconstruction (MBIR) algorithms. Some methods have been developed to deal with fast imaging applications (time-lapse 4D tomography), undersampled data, the missing wedge problems, and various imaging inaccuracies (artifacts). Some computionally time-consuming tasks are accelerated with the use of GPU. The linked projects on the CCPForge are: Novel Software for Spectral Tomography and Reconstruction of limited angle CT data

         The software includes (restricted access for developes):

• Projection-backprojection routines for parallel, fan beam geometry
• Various iterative reconstruction modules: Landweber, CGLS, MLEM, OSEM, FISTA, Primal-Dual  
• Various regularisation procedures: Tikhonov-L2, TV-L1, Diffusion, Higher order PDEs, Non-local means, Patch-based penalties
• Novel spatial-temporal regularisation techniques
• Novel structurual priors for hybrid reconstruction
• Real data reconstruction routines, artifacts removal     

         References (articles in open access) (for presentations use Adobe Acrobat to see animations):

1. "An anatomically driven anisotropic diffusion filtering method for 3D SPECT reconstruction", K. et al., Phys.Med.Biol., 57, 2012. CODE

2. "A novel technique to incorporate structural prior information into multimodal tomographic reconstruction", K. et al., Inverse Problems 30 (6), 2014,  065004. CODE PRESENTATION

3. "Multimodal image reconstruction using supplementary structural information in TV regularization", K. et al., Sensing and Imaging (Springer) 15(1), 2014. CODE

4. "4D-CT reconstruction with unified spatial-temporal patch-based regularization", K. et al., Inverse Problems and Imaging, 9(2), 2015, pp.447-467. CODE

5. "Employing temporal self-similarity across the entire time domain in computed tomography reconstruction", K. et al., Phil. Trans. R. Soc. A, 2015, 373: 20140389. CODE PRESENTATION

6. "Direct high-order edge-preserving regularization for tomographic image reconstruction", K. et al., arXiv, 2015, CODE

7. "Temporal sparsity exploiting nonlocal regularization for 4D computed tomography reconstruction", K. et al., Journal of X-Ray Science and Technology, vol. 24, 2016. CODE & PHANTOM

8. "Sparsity Seeking Total Generalized Variation for Undersampled Tomographic Reconstruction", K. et al., ISBI Proceedings, 2016. CODE POSTER

9. "A novel tomographic reconstruction method based on the robust Student's t function for suppressing data outliers", K. et al., IEEE TCI, 2017. CODE

10. "New iterative reconstruction methods for fan-beam tomography", K., V. Pickalov., IPSE, 2017. CODE

         For any questions please e-mail to: daniil.kazantsev@manchester.ac.uk