CCPi aims to provide the UK tomography community with a toolbox of algorithms that increases the quality and level of information that can be extracted by computed tomography. Chaired by Prof Philip Withers (University of Manchester); co-ordinated by staff in the Scientific Computing Department STFC; led by a working group of experimental and theoretical academics with links to Diamond Light Source, ISIS and Industry.
The remit of CCPi is to bring together the imaging community, maximise return on investment in software development and ensure longevity, sustainability and re-use of code.
In particular it will:
- establish a framework (CIL) within which different reconstruction algorithms, artefact reduction codes, and image analysis procedures are made available to all
- provide practical versions of emerging reconstruction tools (e.g. iterative algorithms; discrete tomography, local tomography) leading to significant improvements in the fidelity of reconstructions
- develop fast new parallel implementations of existing software to run on central and local (accelerator based and heterogeneous) hardware
- ensure professional standards of code writing and documentation
- train and support the community in the application of reconstruction and analysis tools
- interface with/outreach to a wide user base
- draw in mathematicians working on imaging and make their advances available to a wider community
- interface with instrument developers to ensure that reconstruction algorithms can be applied to data acquired on their x-ray systems
- help to define a metadata standard for X-ray projection data to aid data sharing & analysistranslate algorithms in from, and out to, cognate imaging modalities (TEM, PET, etc).
- Seek to establish relevant links and funding from complementary sources (e.g. BBSRC and MRC)
The CCPi Flagship “A Reconstruction Toolkit for Multichannel CT” was funded by the EPSRC grant EP/P02226X/1.
CCPi acknowledges the EPSRC funding of the project “Rich Nonlinear Tomography for advanced materials (LEAD)” by the grant EP/V007742/1 as well as computational support by CoSeC, the Computational Science Centre for Research Communities.