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CCP in Tomographic Imaging (CCPi) – http://www.ccpi.ac.uk

Mid-Term Report – 12 August 2013

1 Introduction The aim of this CCP is to support the emerging UK tomography community with a toolbox of algorithms to increase

the quality and level of information that can be extracted by computer tomography (CT). The size of this community

has grown sharply over the first 18 months of the project with many academic groups around the UK taking up

tomographic imaging and purchasing new lab based x-ray CT scanners (this includes the Universities of Bath,

Swansea and Nottingham as well as the RCaH and the NPL). In addition David Willetts announced funding for a new

£5m materials science imaging beamline (DIAD, Dual Imaging and Diffraction) at the Diamond Light Source.

Our aim is to bring together the imaging community, maximise return on investment in software development and

ensure longevity, sustainability and re-use of code. This was envisaged through the following actions:

• establishing a framework (toolbox) within which different reconstruction algorithms, artefact reduction codes,

and image analysis procedures are made available to all. We have packaged reconstruction codes in Matlab

and stand-alone code (see Section 2.2.1) and have packaged image quantification algorithms for VolView and

Avizo (see Section 2.2.2).

• providing practical versions of emerging reconstruction tools (e.g. iterative algorithms; discrete tomography,

local tomography) leading to significant improvements in the fidelity of reconstruction. See Section 2.3

• developing fast new parallel implementations of existing software to run on central and local (accelerator

based and heterogeneous) hardware. We have started working with Diamond Light Source staff to port

algorithms to the e-Infrastructure South’s Emerald GPGPU cluster hosted by STFC.

• ensuring professional standards of code writing and documentation. Our codes have been developed by

experienced software engineers and managed with version control via CCPForge. See Case Study 1 for an

example.

• interfacing with/outreach to a wide user base, training and support the community in the application of

reconstruction and analysis tools. We have run 6 courses with 45 individual attendees on reconstruction and

analysis and 4 workshops with 117 attendees (20 from industry) via MIRAN (see Section 2.5).

• drawing in mathematicians working on imaging and make their advances available to a wider community. We

have brought in extra funding (EPSRC grant MIRAN) to bring together mathematicians to look at image

reconstruction see Section 2.5.

• interfacing with instrument developers to ensure that reconstruction algorithms can be applied to data

acquired on their x-ray systems. We are working with Xradia (now ZEISS), Nikon Metrology and Rapiscan

systems, see section 2.4.

• helping to define a metadata standard for X-ray projection data to aid data sharing & analysis. Initial new

standard file formats (based on NeXus using hdf5) have been developed by DLS, with links to the STFC ICAT

(open source metadata management system), and are being promoted through the CCP.

• translating algorithms in from, and out to, cognate imaging modalities (TEM, PET, etc). We have funded a

fellowship in compressed sensing applied to computational tomography, see Section 2.5 and FEI (Electron

Microscope manufacturer) have funded a further fellow to work with Manchester also in this area.

• seeking to establish relevant links and funding from complementary sources (e.g. BBSRC and MRC). We have

links with the new EU COST project on Enhanced X-ray Tomographic Reconstruction and strong interaction

with the Harwell Imaging Partnership (HIP), see sections 2.5 and 4, also Industry funding see Case study 2.

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2 Progress Reports

2.1 Network Development

The CCPi network has grown rapidly in the first half of the project and communications between the members are

handled with JISCMail mailing lists – one for general membership and 4 sub-lists for instrumentation, modelling,

quantification and reconstruction. In total there are 255 members on the general list including 8 company members.

The CCP has developed strong links with the MIRAN network and the Harwell Imaging Partnership in the UK, which

have funded meetings on reconstruction theory and other imaging technologies among others.

The network is also becoming international in scope with international visitors – notably from MIRAN including Per

Christian Hansen from Technical University of Denmark, Christophe Cloquet from Vrije Universiteit Brussel and

Hyoung Suk Park from Yousev University of South Korea (see section 2.5). This has been aided by our helping to

establish a COST funded European network: MP1207: Enhanced X-ray Tomographic Reconstruction: Experiment,

Modeling, and Algorithms. Led by Joost Batenburg, additional funding is brought forward for algorithm workshops

and puts the CCPi within a European Network.

2.2 Core Support

2.2.1 Reconstruction

The image reconstruction work has been based around the iterative algorithms developed by Prof Lionheart's group

at Manchester with strong links to the group at Bath. Initially MATLAB based Conjugate Gradient Least Squares

(CGLS) code was ported along with a new user interface for users and trialled at the Manchester X-Ray facility

(HMXIF). Subsequently, the Total Variation Regularisation (TV) algorithm arising from the widening participation

grant (see case study 2) has been integrated into the package to provide a second algorithm for users to choose

from. These codes have been set up as a project on CCPForge and all development has occurred within its source

code archive. Additional work involved investigating the performance of the OpenMP parallelism within the forward

and backward projection algorithms and optimisation of these routines.

With the release of a public dataset from DLS the CGLS code was extended to work with the geometry of the

instrument on beamline I12 at the national Diamond Light Source. This required modifications to operate on parallel

beam geometries and a reader for the NeXus data file format. It also requires a version of the algorithms

independent of MATLAB in order to be able to operate in the batched environment of the DLS and the Emerald

GPGPU systems. With professional coding standards applied, versions of the code now share the same source code

as the projection algorithms.

2.2.2 Quantification

Work on 3D quantification has focused on increasing the availability of existing quantification algorithms to the CCPi

community via re-implementation and packaging for existing visualisation applications. The first task was to identify

the applications that would to run the algorithms and from the outset Avizo – a commercial package – was identified

as a prime candidate. Avizo accepts user modules implemented in C++ and makes them available to run on data

within the application. In terms of accessibility it made sense therefore to look at an open-source applications that

worked in the same way and VolView, developed by KitWare was chosen. Details of this decision process can be

found in Case Study 1 along with references and exemplar tools that have been developed.

The algorithms were initially developed for VolView in C++ but in such a way that they could easily transferable to

widely used commercial packages such as Avizo. This meant that the interface between the visualisation application

and quantification code is separate from the algorithm implementation and the implementation interface only uses

C++ standard containers (lists, vectors etc). Supported libraries are interchangeable and the following two have

been integrated and tested:

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The Insight Segmentation and Registration toolkit (ITK) an open-source, cross-platform system that

provides developers with an extensive suite of software tools for image analysis. This library is also used by

VolView and Avizo.

The Visualisation toolkit (VTK) another open-source library that provides primarily scientific visualisation

functionality and has proved useful for some quantification tasks.

Algorithms implemented include:

3D quantification for labelled images based on a Matlab code. Applying the algorithm to data is now easier

thanks to the re-implementation and can run in parallel on multi-core machines.

Accessible volume calculation. Calculates the volume accessible to a series of spheres with user defined size

and produces a segmented image according to the largest sphere that can reach each voxel.

Ellipsoid fitting. Calculates parameters of ellipsoids fitting particles in an image. This algorithm was

produced at the request of an attendee at a quantification workshop who was using 3D images as a basis for

further computational modelling work.

Calculating connected components. This gives a simple way to create a labelled image from a binary image

following thresholding. Particularly useful for porous media where we want to label the pores before

quantification.

Particle tracking. Tracking labelled particles in a time series of 3D images using a flexible algorithm for

comparing the particles from one frame to the next. This has been used to analyse leaching of particles in

fractured rock samples.

2.2.3 Other Support

In addition to the technical work the core support team has been administering the CCP. This activity has included:

Registering the CCPi website (http://www.ccpi.ac.uk), designing the site and keeping it up to date. A

secondary website for outreach and interactive examples is being built with support from the University of

Bath. A YouTube channel has been set up for CCPi to make our videos widely available.

Arranging and hosting the steering group meetings for the CCP and taking minutes.

Creating and maintaining the general and domain specific mailing lists on JISCMail.

Collating outreach material and making these available on-line.

Developing training videos and public outreach videos on the CCPi YouTube channel

http://www.youtube.com/channel/UCGB578xcyXNQyiBsufEFIeQ

2.3 Flagship Software Development

The initial flagship project for CCPi was a three year Fellowship in Iterative Reconstruction. Daniil Kazantsev was

appointed to this post and is located at the Research Complex at Harwell. The development of iterative

reconstruction algorithms was applied to the specific problem of four dimensional computed tomography. When

tomographic experiments are performed with a moving sample it is desirable to minimize the number of projections

for reconstruction to reduce blurring artefacts. Analytical techniques, such as filtered back-projection (FBP), are not

able to achieve an acceptable quality of reconstruction in this situation where we have under sampled data. The

iterative methods are better suited to deal with such problems; however additional constraints (regularisation

terms) are required on the solution to penalize strong deviations due to noise.

In the flagship project several iterative reconstruction algorithms for comparison were implemented, including

Landweber, SIRT, MLEM, OSEM and CGLS. The algorithms are currently implemented in Matlab, however they can

be transferred to other programming languages (C, Fortran, Python , CUDA). A significant amount of effort has been

spent on implementating different types of iterative penalties to ensure algorithmic stability including: total

variation (TV), anisotropic diffusion (AD), higher order PDE and also temporal smoothing. The regularising penalties

are computationally expensive in general and were implemented in CUDA as mex functions which can be called

within the Matlab environment. GPGPU acceleration allows feasible reconstruction times to operate on real data.

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Experiments were performed on neutron tomography data to reconstruct dynamic aqueous flow through pore

structures in rocks. The use of spatial and temporal penalties with the CGLS algorithm resulted in better resolution

and reduced noise in the reconstructed images (see Case Study 3).

2.4 Widening Participation

Following a call from EPSRC the CCP was successful in funding two small projects. After a national competition

Thomas Blumensath was awarded a four month fellowship in compressed sensing applied to computational

tomography, based at the University of Southampton. Compressed sensing (CS) techniques are promising a step

change in many sensing and imaging applications. Whilst progress in medical imaging has been swift, in particular

within Magnetic Resonance Imaging, application to tomography reconstruction has not progressed at a similar pace.

This has partly been due to the fact that some ideas proposed in CS had already been proposed and applied in CT

reconstruction and partly, due to the fact that CS algorithms remain computationally significantly more complex

than standard approaches.

Nevertheless, advances in CS reconstruction can have significant bearing on CT reconstruction problems, especially

in challenging imaging settings, where increased computational reconstruction effort can be afforded. Several

imaging settings could benefit from CS type reconstruction:

1. Low number of projections

2. Reduced angular access imaging

3. Low contrast imaging

4. Short exposure time

5. High absorbing materials

6. Beam hardening correction

Initial simulations have explored the applicability of advanced CS algorithms for the reconstruction of CT images

from a restricted set of observations. We have investigated the reconstruction of a simple phantom image from only

9 projections (75 rays each). Whilst Total Variation regularisation (TV) provides some benefits over filtered back

projection (FBP), a CS reconstruction inspired iteratively re-weighted TV optimisation method provides significant

additional improvements. Several open issues and potential solutions are:

Computation cost - use of greedy algorithms

Poisson noise - the optimised cost-function can be adjusted appropriately

non-linearities in x-ray transmission - non-linear transmission models can be included

This has led to an EPSRC five year Fellowship application in preparation to address these points and several links with

industrial partners have already been made with support secured, including:

Nikon metrology - EngD project agreed, candidate to be recruited

AWE - KTS in preparation

NPL – collaboration in XCT calibration interest group

Nicola Wadeson was also awarded a fellowship and has been working with specific Nikon Xtek system geometrical

information to take novel CT algorithms from academia and implement them in a commercial CT scanner setting.

Two algorithms have been ported – CGLS and TV regularisation and tested against the usual built-in FDK

reconstruction. Although these algorithms have not been implemented within the CT machine they are available for

reconstruction in the University of Manchester Labs. The work has shown the benefit for instrument suppliers

working with algorithm developers to bring state-of-the-art reconstruction to commercial scanners. After this

development work a second UK company – Rapiscan Systems – was bought in who could provide details of data

collection and patterns of use. More details can be found in Case Study 2.

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2.5 Complementary funding

Prof Bill Lionheart, Co-I on CCPi, was PI for MIRAN (Manchester Image Reconstruction and Analysis), funded by an

EPSRC "Building Global Engagements" grant that provided resources to engage with the international community on

image reconstruction algorithms. It funded a series of visits (20 short term visitors) and led to three workshops with

themes contributing to the aims and knowledge base of the CCP:

Inverse Transport, Mar 2013 - Total of 29 attendees, 28 academic and 1 from industry.

Bayesian Methods, Mar 2013 - Total of 36 attendees, 35 academic and 1 from industry

Imaging Landmines Workshop, Jun 2012 - Total of 35 attendees, 24 academic and 11 from industry

MIRAN also organised an introductory algorithms session based on the mathematical principles:

Algebraic Iterative Reconstruction Methods, Nov 2012 - Total of 17 attendees, 10 academic and 7 from

industry [training course: Professor Per Christian Hansen, DTU Informatics, Technical University of Denmark]

The MXIF group based at the Research Complex at Harwell, led by CCP Co-I Prof Peter Lee have organised 6

workshops on image reconstruction and quantification. Reconstruction has focussed on using software on the DLS

cluster and quantification has used Avizo and VolView including plug-ins developed under the core support for the

CCP. The following workshops have been held:

Reconstruction and quantification, Jul 2012 – Total of 13 attendees

Reconstruction and segmentation, Nov 2012 – Total of 9 attendees

Reconstruction and quantification, Feb 2013 – Total of 8 attendees

Reconstruction, Jul 2013 – Total of 18 attendees

Drs Martin Turner, Mark Basham and Robert Atwood have been creating links and collaborations from the CCPi with

the Harwell Imaging Partnership and the Diamond Light Source. These are there to add extra funds and support to

the network:

HIP catalyst project with the University of Bath, aims to apply iterative algorithms directly on DLS data

compared with lab based data sets, involving industrial observers from Nikon Metrology and Rolls Royce.

Coordinated and cross-funded Avizo visualisation software procurement to be available to all DLS and STFC

users for three years from Apr 2013. New extreme-end workstations were also purchased for this purpose

and an initial training course was given in Jun 2013 - Total of 9 attendees.

Commissioned the first video documentary to create a show-and-tell application describing how the DLS

imaging tomography is performed by users from the University of Birmingham. Available on the CCPi

YouTube channel (see link in section 2.2.3).

Work within DLS and STFC has supported the porting of original CCPi code from the DLS small GPGPU cluster

to run on the STFC Emerald GPGPU cluster. This included the creation of a unified meta-data and single unit

file format, with consultation from STFC (SCD) staff.

The CCPi helped set-up, and participates, in the EU Cooperation in the field of Scientific and Technical Research

(COST network, May 2013 to May 2017) for Enhanced X-ray Tomographic Reconstruction: Experiment, Modeling,

and Algorithms. Through joint activities with the CCPi this network aims to act as a catalyst for further advances

resulting in further multidisciplinary collaborative projects (e.g. Horizon 2020); enable breakthroughs in research

fields that use advanced X-ray tomography and enforce European industrial R&D with the ability to develop new

techniques and algorithms into commercially viable scanning solutions.

3 Critical Success Factors The mid-term targets and our current position against those targets are shown in the table below; and itemised

here:

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Mid-term Target Achieved

Increase user base 50 members of network >250 members of network with four sub-specialist lists.

International links 3 International Fellows visits MIRAN funded at least 3 international visitors and speakers for workshops

Links with industry 5 Company members

5 Industrialists attend training courses/workshops

8 company members

20 industrialists at MIRAN workshops

Feedback from

workshops/training

40 different people have attended workshops/training

Over 150 individuals have attended sessions including 20 from industry

Outreach 4 outreach videos for schools available on you tube

Over 4 videos are now hosted on the CCPi http://www.youtube.com/channel/ UCGB578xcyXNQyiBsufEFIeQ

Software development

New scientific

functionality added

5 3D analysis routines added

2 reconstruction tools added

6 3D analysis routines added to VolView

2 reconstruction algorithms implemented

Flagship project

New scientific

functionality added to

code

Prototype iterative algorithms available for network testing:

Centre of rotation/blurring corrections

Missing wedge algorithm

Several algorithms have been implemented for iterative reconstruction tested in Matlab with CUDA implementation of costly penalty functions.

Focussing on 4D reconstruction a novel method for temporal smoothing

Missing wedge and variants are being reconsidered for post- mid-term review

Papers 3 Key papers are (*) in case studies 1, 2 and 3.

Complementary

follow-on funding

1 parallel funded project (industry

or Research Council)

Several parallel funded projects including those described

in Section 2.5 have been successful.

4 Future Plans It is an exciting time for the network as multiple partners across multiple institutions are coordinating outcomes and

actively exploring new interactions. Targets have been often exceeded, but following the mid-term review there is a

busy schedule of goals; both those identified in the original proposal and new ones proposed from the community as

the network has evolved. There is also a series of networking activities planned in conjunction with the new EU COST

consortium (coordinated by Prof Bill Lionheart), including a “hackathon” on tomographic operations (Apr, 2014), as

well as further developments with the HIP and directly with DLS, including commissioning follow-on video

documentaries (Southampton and Manchester). In September, the CCPi is sponsoring industrial-academic links at

The Tomography for Scientific Advancement (ToScA) Symposium (Natural History Museum, London Sep 2013); and

has been invited by NPL/BSI to assist in an x-ray tomography standards committee.

Core and fellowship development of new algorithms will continue. We are providing the source code, and in some

cases compiled versions, of the reconstruction and quantification algorithms via CCPForge along with compilation

and installation instructions. We will maintain these instructions and encourage users to seek help via the forums in

the CCPForge projects. The use of the new quantification algorithms have been incorporated into training courses at

RCaH and these will be extended from user requirements in the future. We have a target of training 50 new users

each year; there is good evidence these will be over-subscribed. There is already a plan to use VolView within

subsequent image-based modelling workshops. Members of the CCP from the University of Bath are compiling

training materials on the basics of CT scanning and reconstruction algorithms that will be available via a dedicated

web site linked from the CCP’s own site. Part of the future core support will be to collate existing material on image

analysis to develop it into a form suitable for a web environment. The overall goal to encourage the main CT user

base to actively consider new re-reconstruction and quantification methods – is moving in the right direction.

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ANNEX: Case Study 1

1. Collaborative Computational Project: CCP in Tomographic Imaging (CCPi)

2. Title: Implementing 3D Quantification Algorithms for Community Use

3. Summary of Case Study:

Many members of the CCPi are actively researching innovative image analysis and quantitative

algorithms for quantifying 3D structures, together with the evolution of these structures with time.

This includes algorithms which have been honed over the past two decades via on-going

development and application to problems ranging from aero-engine turbine blade design to bone

ingrowth into bioactive scaffolds. However, many of the algorithms were developed for a particular

application: so making these cross disciplinary quantitative methodologies available to the wider

academic community, to benefit on-going programmes and initiate and facilitate new research is

the focus of part of the CCPi project, and this case study.

The work of the CCPi has been to collect and package a selection of these algorithms in a manner

that best makes them available to the research community, as well as making them generally

applicable, via a six stage process:

1. Prioritisation of the algorithms to be included

2. Identification of appropriate software packaging mechanism

3. Development and modification of algorithms to implement in packaging software

4. Testing and release, via integration into both open-source and commercial packages

5. Training workshops

6. Exemplar demonstrator projects

The initial algorithms for implementation were provided by members of the Manchester X-ray

Imaging Facility based at the Research Complex at Harwell. They include:

Applying fast PCA analysis of 3D structures, quantifying over 20 different features with up to

a 70% increase in accuracy [Atwood et al , Yue et al]

Quantitative analysis of the accessible volume within 3D structures allowing the first

quantitative comparison to porosimetry techniques (BET to MIP [Tariq et al]).

4D particle tracking, flow analysis, etc…

The open source VolView package was determined as the most appropriate packaging software,

with the CCPi software available as plug-ins [Worth]. The computation code for the quantification

and the code that implements the VolView API for user plug-ins will be kept separate as the

VolView implantation language is C++, the same as the most widely used commercial software

package, AvizoTM (where the API’s will be available in the near future). Having the algorithms in the

two most used open-source and commercial packages enables almost all researchers to perform

analysis of tomographic or other 3D and 4D data sets.

The quantification implementations make extensive use of the Insight Segmentation and

Registration Toolkit (ITK http://www.itk.org) and the Visualisation Toolkit (VTK

http://www.vtk.org) both from KitWare. These libraries are widely used, well supported and under

constant development by the community because of their flexibility, range of algorithms and quality

of design, implementation and testing. VolView (also a KitWare product) already uses these

libraries, and ITK is used within Avizo, making re-use of the computation code straight-forward.

The use of the existing ITK and VTK libraries with their own container classes, vectors, lists, etc

required careful implementation of the interfaces to the computation classes. To make the CCPi

implementation as flexible as possible standard C++ containers were used for this, despite the fact

that this means additional steps are required to copy data into and out of ITK/VTK containers.

Details of the design and implementation of one of the plug-ins is given in [Worth], showing our

software development process, and providing a full description of how to compile VolView from

source (previously unavailable) that we have developed. Compilation is a necessary step when

developing plug-ins and so this document provides a valuable resource for other researchers

developing codes beyond the CCPi framework.

The impact of these software developments has already been large with applications and

publications in fields ranging from biomedical structures to petroleum reservoir characterisation,

catalysis development, etc. Examples are shown in Fig. 1.

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Bioactive implants accurately quantified for 1st time. Impact: understand how to pre-condition. (ICL, Ulster, UoM)

Quantification of Li batteries from the macro to nano scale. Helping understand performance and failure mechanisms. (UCL, APS, DLS, UoM)

Quantification pore, interconnect, surface roughness helped development of 1st additive manufactured patella implant! (UoM, Stryker, Liverpool, ICL)

Figure 1. Example applications of the CCPi algorithms to key areas in EPSRC remit.

References:

(*) E. Maire, P.J. Withers, 2013. Quantitative x-ray tomography. International Materials Reviews; accepted 2013 [1] Atwood et al, Scripta Mat, 51 (11),1029-33, 2004. doi:10.1016/j.scriptamat.2004.08.014 [2] Tariq et al, Acta Mat, 59(5):2109-20, 2011. doi:10.1016/j.actamat.2010.12.012 (*) D. Worth, 2013. Developing and Image Analysis Plugin for VolView, RAL Technical Reports, RAL-TR-2013-007, http://epubs.cclrc.ac.uk/bitstream/9412/RAL-TR-2013-007.pdf [3] Yue et al, Acta Biomater, 7 (2011) 2637–43, 2011, doi:10.1016/j.actbio.2011.02.009

4. Key outputs:

Assessment and prioritisation of 3D image quantification algorithms has been performed and these

algorithms have been packaged in open source and commercial software that can be run on both

Linux and Windows operating systems.

The software has been tested and the source code releases are available to download. The

compiled plug-ins and VolView (for Windows) along with source code for the plug-ins are available

from the CCP software repository CCPForge in the Image Quantification Algorithms project

http://ccpforge.cse.rl.ac.uk/gf/project/iqa/. We maintain a revision control system to track changes

during development, and a file release system to monitor downloads. There is an invitation to the

user community who wish to develop algorithms to join the project.

The software has already been used in a CCPi training workshop on quantification that was run at

the Research Complex at Harwell, and will be used in image-based modelling workshops at the

University of Manchester later in 2013. The algorithms and the plug-ins were made available for

people to take home and use on their own data.

5. Information on how this case study relates to strategic goals in the EPSRC Software as

an Infrastructure Strategy: The case study relates to several strategic goals:

Shaping capability – Over a half dozen original algorithms have now been implemented in

Volview, the main open-source visualisation product worldwide.

Delivering impact – These have already been tested and used by over 10 different university

groups, with 2 training sessions having been held.

Ensuring trust – Robust, high quality tested software is now available as open source

Planning for the future – The same DLL's are being incorporated in the main commercial

visualisation platform, Avizo, and are in alpha testing with dissemination planned for late 2013.

6. Names of key academics and any collaborators:

Peter D. Lee, Sheng Yue, Loic Courtous (University of Manchester, Materials); Julian Jones, Steven

Neethling (Imperial College London, Mats, ESE); Paul Shearing (UCL, Chem Eng.) Eric Jones

(Stryker Orthopeadics); Robert Atwood, Nghia Vo (Diamond)

7. Sources of significant sponsorship (if applicable):

Stryker Orthopeadics, 2 PhD studentships and 1 year PDRA (~£200k); HIP/Tata Steel/Diamond:

case study funds and Studentship (~£50k)

8. Who should we contact for more information?

Dr David Worth, Scientific Computing, STFC, david.worth@stfc.ac.uk 01235 445894

Prof. Peter D. Lee, Univ. of Manchester, peter.lee@man.ac.uk, 07967 110647

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ANNEX: Case Study 2

1. Collaborative Computational Project: CCP in Tomographic Imaging (CCPi)

2. Title: Taking algebraic iterative reconstructions for x-ray CT from the blackboard to

industrial exploitation

3. Summary of Case Study:

Despite the fact that many iterative algorithms have been proposed by mathematicians, the vast

majority of CT datasets are reconstructed using primitive filtered back-projection algorithms. This is

because iterative methods require an accurate forward model of the instrument, which cannot be

achieved without working closely with the instrument supplier. Consequently algorithms are

developed on synthetic data, while users rely on FBP algorithms provided by instrument suppliers to

reconstruct their data.

The aim of this project was to widen access to novel CT algorithms by working with Nikon Xtek to

examine the steps required to take an algorithm from the blackboard into practical use on a

commercial CT scanner. The successful deployment of such an algorithm would allow more image

sets to be collected in a shorter space of time, using less data and opening up faster frame rate time

lapse imaging. If the advantages of such a work-flow could be demonstrated it was hoped other

companies would also open up their systems to iterative algorithms.

Two algorithms were considered, CGLS [Shewchuk] and TV Regularisation [Jensen et al]. The CGLS

code was developed by Will Thompson and subsequently used to reconstruct 3D projection data with

a low number of views (100 and 200) as against the 2-3000 images used commercially. A TV

Regularisation algorithm was adapted to incorporate forward and back projections using Jacob's ray

tracing [Jacobs et al], to remove the need to store the very large matrix A. The same data was

then reconstructed using TV Regularisation with different parameters, and results were compared to

the in-built FDK reconstruction.

All code was written within a robust framework and made as user friendly as possible.

Figure 1. 2D Image reconstruction from 100 projections using FDK, CGLS and TV Regularisation

References:

T.L. Jensen, J.H. Jorgensen, P.C. Hansen, and S.H. Jensen, 2011. Implementation of an optimal first-order method for strongly

convex total variation regularization. BIT Numerical Mathematics, 1-28

J.R. Shewchuk, 1994. An introduction to the conjugate gradient method without the agonizing pain.

F. Jacobs, E. Sundermann, B. De Sutter, M. Christiaens, and I. Lemahieu, 1998. A fast algorithm to calculate the exact

radiological path through a pixel or voxel space. Journal of computing and information technology, 6 (1998) 89-94

(*) W. Qiu, T. Pengpan, N.D. Smith and M. Soleimani, 2013. Evaluating iterative algebraic algorithms in terms of convergence

and image quality for cone beam CT. Computer Methods and Programs in Biomedicine, 109 (3), pp. 313-322.

(*) W. Qiu, D. Titley-Peloquin and M. Soleimani, 2012. Blockwise conjugate gradient methods for image reconstruction in

volumetric X-Ray CT. Computer Methods and Programs in Biomedicine, 108 (2), pp. 669-678.

(*) T. Pengpan, W. Qiu, N.D. Smith and M. Soleimani, 2012. Cone-beam CT using motion-compensated algebraic

reconstruction methods with limited data. Computer Methods and Programs in Biomedicine, 105(3), pp. 246-256.

(*) M.K. Greco, J. Tong, M. Soleimani, D. Bell and M.O. Schäfer, 2012. Imaging live bee brains using minimally-invasive

diagnostic radioentomology. Journal of Insect Science, 12, 89.

(*) T. Pengpan, N.D. Smith, W. Qiu, A. Yao, C.N. Mitchell and M. Soleimani, 2011. A motion-compensated cone-beam CT

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using electrical impedance tomography imaging. Physiological Measurement, 32 (1), pp. 19-34.

(*) W. Qiu, J. Tong, C. Mitchell, T. Marchant, P. Spencer, C.J. Moore and M. Soleimani, 2010. New iterative cone beam CT

reconstruction software: parameter optimisation and convergence study. Computer Methods and Programs in Biomedicine,

100 (2), pp. 166-174.

4. Key outputs:

Results: A detailed report of the study was produced, showing how by working with a company

CGLS and TV Regularisation can be used to reconstruct commercially obtained images thereby

significantly reducing background artefacts compared to FDK (a type of FBP) algorithm. This aids

the segmentation process that is important to material scientists.

Knowledge transfer: The project was a successful collaboration between algorithm developers,

commercial instrument providers (who made their raw data available) and end users in Material

Science. The maths team were able to implement algorithms developed in part through work with

another UK company (Rapiscan), provided with details of the data collection process as well as the

difficulties faced in reality provided by the expert users and help with the raw data from the

industrial collaborator.

Long term: This case study has proven the value of instrument suppliers working with algorithm

developers to make information available for the deployment of iterative code to allow users to

deploy state of the art algorithms. Two further industry funded projects have come out of this

project, one with Xradia (Will Thompson 9 month project) which exploits iterative algorithms and

one with Rapiscan/Home office (Nicola Wadeson 11 month project) which focuses on improving

reconstructed image quality.

5. Information on how this case study relates to strategic goals in the EPSRC Software as an

Infrastructure Strategy:

This project has been:

Shaping capability – Led to the transfer of new algorithms into industry (Xradia and Rapiscan)

opening up new imaging capabilities research through the development of sustainable and robust

software.

Developing Leaders and Skilled People – Nicola Wadeson and Will Thompson have now been

employed as post-docs by two imaging companies to help implement new iterative algorithms and

improve image quality, and training them in the development of robust software development

working alongside instrument developers and end user materials scientists to ensure useable

software that works effectively.

Delivering impact – Until recently iterative algorithms have stayed on the blackboard – this

project has helped to prove the value of companies working with algorithm developers to exploit the

latest imaging algorithms. Further given the medical, life-science and materials science benefits of

better images these benefits are not just economic.

Planning for the future – By working with companies and large scale facilities this project has

helped to take iterative algorithms into everyday practice. Such software will be sustained over the

long term by instrument scientists and international facilities and by the instrument provides on

commercial lab. systems. Further we will have a framework whereby new algorithms can find

industrial take up because the forward modelling problem has been circumvented.

6. Names of key academics and any collaborators:

Nicola Wadeson (post-doc), Will Thompson (post-doc), W. Lionheart (University of Manchester,

Department of Mathematics)

Phil Withers, Tristan Lowe, Sam McDonald (University of Manchester, Department of Material

Science).

Sam Hawker (Nikon Xtek), Michael Feser (Xradia), Ed Morton (Rapiscan systems)

7. Sources of significant sponsorship (if applicable):

Xradia, 9 month post doc - £65,250.

Rapsican/ Home Office, 11 month post doc - £100,000.

8. Who should we contact for more information?

Dr Nicola Wadeson, Univ. of Manchester, nicola.wadeson@manchester.ac.uk 0161 2755832

Prof. Bill Lionheart, Univ. of Manchester, bill.lionheart@manchester.ac.uk 0161 3068978

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ANNEX: Case Study 3

1. Collaborative Computational Project: CCP in Tomographic Imaging (CCPi)

2. Title: Flagship Software Development

3. Summary of Case Study:

Researchers want to visualise increasingly complex systems and to quantify 3D mechanisms and

processes operating on every decreasing time scales. Recent major advances have been achieved

using the development of ultra-fast tomography which reduces both the exposure times and number

of projections collected per data set. However, the short exposure times and the limited number of

projections required for fast imaging in order to capture the rapid process under investigation, and

avoid blurring due to sample motion often yields data with a poor signal-to-noise ratio (SNR). This

noise can limit the quality of the subsequent analysis.

In principle iterative reconstruction techniques can improve the quality of the reconstructed 3D data

when compared to the routinely used analytical methods such as filtered back-projection (FBP) or

Fourier direct inversion. Although iterative methods are more suited to processing under-sampled

noisy data, additional constraints (regularisation terms) are required to correctly penalize strong

deviations due to noise. In this project we have developed novel iterative reconstruction techniques

for applications in four dimensional computed tomography (4D-CT).

In the framework of the project several iterative reconstruction algorithms (Landweber, SIRT,

MLEM, OSEM and CGLS) have been implemented. The algorithms were implemented and tested in

Matlab, but they can be transferred to other programming languages (C, Fortran, Python, CUDA).

Significant effort was spent on implementation of different iterative penalties to ensure algorithmic

stability: Total variation, anisotropic diffusion, higher order PDE denoising model. To speed-up the

regularisation process they were implemented on CUDA as mex functions which can be called within

the Matlab environment. This enables GPU acceleration and feasible reconstruction times on real

data volumes.

We then developed a novel method of temporal smoothing [1, 2]. The new methodology has been

applied to data from neutron tomography experiments quantifying dynamic aqueous flow through

the pore structures in packed rock beds. Neutron tomography acquisition is significantly slower than

the equivalent x-ray techniques, but neutrons are much better at imaging certain processes. Using

our methodology we can identify and quantify the temporal evolution of fluid volume within the

sample [Fig 1]. The quality of the images reconstructed from ~50 projections is sufficient to allow

mapping of the location of the liquid and quantitative analysis of the liquid morphology, although we

are working with 100 projections per time step in Fig. 1.

Figure 1. The reconstructed and rendered volumes of unsaturated aqueous flow (blue) through a

packed bed of sandstone chips. Dry state (k = 1) is a reconstruction using 150 projections and

subsequent 3 reconstructions of a wet stages are made by adding subsequent 100 projections.

The use of spatial and temporal penalties with the CGLS algorithm results in better resolution and

reduced noise of reconstructed images and, crucially, a ~10x reduction in the number of projections

data required for each time step. The ability to acquire up to ten complete individual 3D volume

images in the time it would previously have taken to acquire a single volume dramatically increases

the temporal capability of the neutron tomography technique. We are now working to implement

our algorithm to enable laboratory and synchrotron x-ray tomography systems.

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4. Key outputs:

Development of new reconstruction algorithm allows faster temporal resolution in time series

experiments. The methodology could find wide ranging applications in high speed experimental

studies across the entire STFC/EPSRC research spectrum, and is already being applied to aqueous

and hydrocarbon flow.

Dissemination of results to the academic community has been achieved via peer reviewed

publications and conference presentations:

(*) D. Kazantsev, W.M. Thompson, K.J. Dobson, A.P. Kaestner, W.R.B. Lionheart, P.J. Withers and P.D. Lee (submitted) Simultaneous 4D-CT reconstruction using higher order spatial regularization and global Gibbs-type temporal prior. Submitted to Inverse Problems and Imaging Journal

(*) [1] D. Kazantsev, W.R.B. Lionheart, P.J. Withers and P.D. Lee, 2013. GPU accelerated 4D-CT reconstruction using higher order PDE regularization in spatial and temporal domains. CMMSE conference, Spain

(*) [2] D. Kazantsev, S. Ourselin, B.F. Hutton, W.R.B. Lionheart and S.R. Arridge, 2013. A novel method of embedding additional information into tensor diffusion filtering as an application for the multi-modal tomographic reconstruction. Fully 3D conference, CA, USA

Knowledge transfer: The project has enabled collaboration between CCPi members and researchers

from other academic institutes (Paul Scherrer Institut). These collaborations will increase awareness

and usage of the new algorithms. The methodologies have also been included in workshops held at

the Research Complex at Harwell and Diamond Light Source.

Training: Two workshops were performed on basics of Tomographic Imaging.

5. Information on how this case study relates to strategic goals in the EPSRC Software as an

Infrastructure Strategy:

Shaping capability – The algorithms allow dynamic reconstructions of neutron tomography data,

providing new capability through increased temporal resolution. The expansion to x-ray tomographic

applications will increase further use in the fast tomographic imaging experiments.

Delivering impact – The software was developed to satisfy the needs of researchers across a

range of research themes. We have enhanced the access of a range of users (geology,

engineering, bio applications) to the latest of iterative reconstruction algorithms for a routine

reconstruction.

Planning for the future – The software will be made available for users, with sufficient

documentation support and converted from Matlab to high-level programming languages. Ensure

trust in the software and become involved with the CCPi version control project (CCPForge)

6. Names of key academics and any collaborators:

Prof. W. Lionheart, Dr. W. Thompson (School of Mathematics), and Prof. Peter Lee, Prof. Phil

Withers, Dr. K.J. Dobson, Dr. S. McDonald (School of Material Science), all University of Manchester.

Dr. A.P. Kaestner (Spallation Neutron Source Division, Paul Scherrer Institut).

7. Sources of significant sponsorship (if applicable):

This work has been supported by the Engineering and Physical Sciences Research Council under the

main CCPi grant EP/J010456/1 and RCaH grant EP/I02249X/1; but extra travel was supported by

the European Commission under the 7th Framework Programme through the “Research

Infrastructures” action of the “Capacities” Programme, NMI3-II Grant No. 283883, Project

No.20120553

8. Who should we contact for more information?

Dr Daniil Kazantsev, Research Complex at Harwell, daniil.kazantsev@rc-harwell.ac.uk 01235

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