2024-03-29T01:16:21Z
https://zenodo.org/oai2d
oai:zenodo.org:61797
2020-01-20T17:03:21Z
user-ipuom
user-mxif
user-ccpi
openaire
Lionheart,William
Desai,Naeem
Schmidt, Søren
Withers, Philip
2016-09-08
<p>We outline rich tomography methods concentrating on tensor tomography of strain using gamma rays and monochromatic ex-rays, and neutron spin tomography for magnetic fields</p>
https://doi.org/10.5281/zenodo.61797
oai:zenodo.org:61797
Zenodo
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/ccpi
https://zenodo.org/communities/mxif
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ToScA, Tomography for Sceintific Advancement, Bath,UK, 6-7 September 2016
strain tomography
rich tomography
non-abelian tomography
neutron spin tomography
x-ray diffraction tomography
Rich and Nonabelian Tomography: strain and magnetic fields
info:eu-repo/semantics/lecture
oai:zenodo.org:33881
2020-01-25T07:25:54Z
user-ipuom
user-mxif
user-ccpi
software
Sophia Bethany Coban
2015-11-17
<p>This is the second release of the codes, which are written for the SophiaBeads Datasets Project.</p>
<p>These codes are essential to work with the SophiaBeads Datasets. As before, the release contains scripts for loading the datasets, pre-reconstruction steps, and a reconstruction algorithm (cgls_XTek) as a template.</p>
<p>Improvements: This release is more adapted to different operating systems, and also includes a new function called <strong>scrollView</strong>. The function allows the users to view the reconstructed volumes along a chosen dimension within the same window. Type <em>help scrollView</em> in the command window to find out more.</p>
<p>More information can be found on the GitHub project page.</p>
https://doi.org/10.5281/zenodo.33881
oai:zenodo.org:33881
Zenodo
https://github.com/Sophilyplum/sophiabeads-datasets/tree/v2.0
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/ccpi
https://zenodo.org/communities/mxif
https://doi.org/10.5281/zenodo.593371
info:eu-repo/semantics/openAccess
Other (Open)
SophiaBeads Datasets Project Codes
info:eu-repo/semantics/other
oai:zenodo.org:290117
2020-01-24T19:26:07Z
user-ipuom
user-mxif
user-ccpi
openaire_data
Jørgensen, J. S.
Coban, S. B.
Lionheart, W. R. B.
McDonald, S. A.
Withers, P. J.
2017-01-30
<p>The presented data set, inspired by the SophiaBeads Dataset Project for X-ray Computed Tomography, is collected for studies involving sparsity-regularised reconstruction. The aim is to provide tomographic data for various samples where the sparsity in the image varies.</p>
<p> </p>
<p>This dataset is made available as part of the publication</p>
<blockquote>
<p>"<strong><em>SparseBeads Data: Benchmarking Sparsity-Regularized Computed Tomography</em></strong>", Jakob S Jørgensen <em>et al,</em> 2017. <em>Meas. Sci. Technol.</em> <strong>28</strong> 124005.</p>
</blockquote>
<p>Direct link: https://doi.org/10.1088/1361-6501/aa8c29.</p>
<p>This manuscript is published as part of Special Feature on Advanced X-ray Tomography (open access). We refer the users to this publication for an extensive detail in the experimental planning and data acquisition.</p>
<p> </p>
<p>Each zipped data folder includes</p>
<ul>
<li>
<p>The meta data for data acquisition and geometry parameters of the scan (<strong>.xtekct</strong> and <strong>.ctprofile.xml)</strong>.</p>
</li>
<li>
<p>A sinogram of the central slice (CentreSlice > Sinograms > <strong>.tif</strong>) along with meta data for the 2D slice (<strong>.xtek2dct</strong> and <strong>.ct2dprofile.xml</strong>),</p>
</li>
<li>
<p>List of projection angles (<strong>.ang</strong>)</p>
</li>
<li>
<p>and a 2D FDK reconstruction using the CTPro reconstruction suite (RECON2D > .<strong>vol</strong>) with volume visualisation parameters (<strong>.vgi</strong>), added as a reference.</p>
</li>
</ul>
<p> </p>
<p>We also include an extra script for those that wish to use the SophiaBeads Dataset Project Codes, which essentially replaces the main script provided, <strong>sophiaBeads.m</strong> (visit https://zenodo.org/record/16539). Please note that <strong>sparseBeads.m</strong> script will have to be placed in the same folder as the project codes. The latest version of this script can be found here: https://github.com/jakobsj/SparseBeads_code</p>
<p> </p>
<p>For more information, please contact</p>
<ul>
<li>jakj [at] dtu.dk</li>
<li>jakob.jorgensen [at] manchester.ac.uk</li>
</ul>
JSJ was supported by the project ``High-Definition Tomography'' funded by Advanced Grant No. 291405 from the European Research Council. JSJ is grateful to the Schools of Mathematics and Materials, University of Manchester for hosting him during the work. SBC was supported by the School of Mathematics, University of Manchester, EPSRC CCPi (EP/J010456/1), and BP through the BP International Centre for Advanced Materials (BP-ICAM). WRBL acknowledges support from a Royal Society Wolfson Research Merit Award. SAM is grateful for funding through ZEISS. Authors acknowledge use of the Henry Moseley X-ray Imaging Facility at the University of Manchester, funded from the EPSRC under EP/F007906/1, EP/F028431/1, EP/I02249X/1 and EP/M022498/1 as well as Advanced Grant No. 695638 ``Correlative Tomography'' from the European Research Council.
https://doi.org/10.5281/zenodo.290117
oai:zenodo.org:290117
Zenodo
https://github.com/jakobsj/SparseBeads_code
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/ccpi
https://zenodo.org/communities/mxif
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
computed tomography
sparse image reconstruction
sparsity
SparseBeads Dataset
info:eu-repo/semantics/other
oai:zenodo.org:1194713
2020-01-20T17:19:21Z
user-ipuom
openaire
William Robert Breckon Lionheart
2018-03-08
<p>Seminar given in the Inverse Problems seminar series in te School Of Mathematics , University of Manchester, on Histotomography. Abstract follows:</p>
<p> </p>
<p>In many tomographic imaging problems the data consists of integrals along lines or curves. Increasingly we are seeing ``rich tomography" problems where the quantity imaged is higher dimensional than a scalar per voxel, including vectors tensors and functions. The data can also be higher dimensional and in many cases consists of a one or two dimensional spectrum for each ray. In many such cases the data contains not just integrals along rays but the distribution of values along the ray. If this is discretized into bins we can think of this as a histogram. In this talk we introduce the concept of ``histotomography". For scalar problems with histogram data this holds the possibility of reconstruction with fewer rays. In vector and tensor problems it holds the promise of reconstruction of images that are in the null space of related integral transforms. We will illustrate with examples from scalar spectral attenuation tomography and tensor tomography methods for strain using neutrons, electrons and x-rays.</p>
A Youtube video of the seminar will be available when ready at https://youtu.be/fVCmB3s5H_0 The notes appear as MIMS eprint http://eprints.maths.manchester.ac.uk/2627/
https://doi.org/10.5281/zenodo.1194713
oai:zenodo.org:1194713
Zenodo
https://zenodo.org/communities/ipuom
https://doi.org/10.5281/zenodo.1194712
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
tomography
moments
spectral tomography
strain tomography
Doppler tomography
Bragg edge
diffraction
Histotomography
info:eu-repo/semantics/lecture
oai:zenodo.org:16539
2020-01-25T07:26:45Z
user-ipuom
user-mxif
user-ccpi
software
Coban, S. B.
2015-04-01
<p>The initial release of the codes, to be used for the SophiaBeads Dataset Project.</p>
<p>The codes are <em>essential</em> to work with the SophiaBeads Dataset. The release contains scripts for loading the dataset, pre-reconstruction steps, and a reconstruction algorithm (cgls_XTek) as a template.</p>
<p>More information about this release can be found on the GitHub project page.</p>
<p> </p>
<p>SophiaBeads Dataset Project: https://zenodo.org/record/16474</p>
https://doi.org/10.5281/zenodo.16539
oai:zenodo.org:16539
Zenodo
https://github.com/Sophilyplum/sophiabeads-datasets/tree/v1.0
https://doi.org/10.5281/zenodo.16474
https://doi.org/10.5281/zenodo.16474
http://eprints.ma.man.ac.uk/2290/
http://eprints.ma.man.ac.uk/2290/
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/ccpi
https://zenodo.org/communities/mxif
https://doi.org/10.5281/zenodo.593371
info:eu-repo/semantics/openAccess
Other (Attribution)
computed tomography
x-ray
cone beam
sophiabeads-dataset
software
reconstruction algorithms
github
codes
SophiaBeads Dataset Project Codes
info:eu-repo/semantics/other
oai:zenodo.org:18123
2020-01-20T17:01:10Z
user-ipuom
user-mxif
openaire
Lionheart, William
Withers, Philip
Desai, Naeem
Schmidt, Søren
2015-05-28
<p>In this conference talk we consider the transverse ray transform on symmetric rank 2 tensor fields in three dimensional Euclidean space and give an explicit inversion formula for complete data and for data for lines parallel to three orthogonal directions. We show how this can be used to determine the strain in a polycrystalline material using a<br />
synchrotron x-ray source.<br />
We go on to consider the problem of determination of a magnetic field from neutron spin tomography data and show that this problem reduces to a non-abelian ray transform in the plane and that the solution is unique for sufficiently smooth magnetic fields.</p>
https://doi.org/10.5281/zenodo.18123
oai:zenodo.org:18123
Zenodo
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/mxif
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
AIP 2015, Applied Inverse Problems 2015, Helsinki, Finland, 25-29 May 2015
tensor tomography
non-abelian tomography
transverse ray transform
x-ray strain tomography
neutron spin tomography
Applications of tensor and non-Abelian ray transforms
info:eu-repo/semantics/lecture
oai:zenodo.org:56449
2020-01-20T17:01:08Z
user-ipuom
user-16thicebi17theit-talks
openaire
Adler, Andy
Lionheart, William
2016-06-20
<p>EIT estimates the internal conductivity distribution from body surface electrodes via the solution of an<br />
inverse problem. Most approaches require solution of a forward problem based on a finite element model (FEM) of<br />
the medium of interest. For iterative solvers, the solution time is typically dominated by the time to solve the FEM<br />
(both for estimation of the measurements and the Jacobian) at each iteration. There is thus a strong incentive to<br />
develop techniques to reduce the solution time.<br />
To obtain accurate forward solutions, it is typically necessary to model a large region far away from the electrodes.<br />
This is most severe in geophysical or endoscopic EIT applications where the medium is effectively infinite. However,<br />
it is also true of traditional EIT applications where, for example, the chest extends above and below the electrode<br />
plane(s). In these extended regions, we do not actually need so solve for the internal voltages; we simply need to<br />
model the effect of the extended region on the ROI. This effect can be thought of in two ways: 1) the extended<br />
region’s effect can be represented by its Dirichlet to Neumann map, or 2) in a resistor model representation of the<br />
FEM, the extended region can be replaced by an equivalent N-port resistor mesh (with 0.5 × N × (N + 1) resistors)<br />
Objective: To implement the model reduction scheme and verify its accuracy and improved calculation time.</p>
https://doi.org/10.5281/zenodo.56449
oai:zenodo.org:56449
Zenodo
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/16thicebi17theit-talks
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ECEBI/EIT 2016, 16 th International Conference on Electrical Bio-Impedance and 17 th International Conference on Electrical Impedance Tomography, Stockholm, 19-23 June 2016
EIT
Schur compliment
resistor network
region of interest
FEM
model reduction
nusance parameters
Model Reduction for forward solutions
info:eu-repo/semantics/lecture
oai:zenodo.org:18059
2020-01-20T14:02:27Z
user-ipuom
Steel, PH
Cooper, JE
Boden, C
Lionheart, WRB
2007-09-03
<p>Abstract:</p>
<p>The juvenile wood type is produced by southern pine trees during their first 10 years of growth producing a significant volume of wood at tree center having inferior properties that decrease lumber value. Increased volumes of lumber are influenced by juvenile wood as intensive plantation silviculture increases early tree growth. Research has shown that knowledge of knot location in logs prior to sawing has the potential to significantly increase lumber value yield. Detection of juvenile and knot wood prior to sawing logs will allow application of sawing patterns that place these wood types into locations or products that have reduced influence on the final lumber value. This study tested an electrical impedance tomography (EIT) scanner for detection of juvenile and knot wood in southern pine. EIT and CT images were compared to determine the accuracy of juvenile and knot wood detection by EIT technology.</p>
https://doi.org/10.5281/zenodo.18059
oai:zenodo.org:18059
Zenodo
isbn:978 0 853 16321 3
http://www.isipt.org/world-congress/5.html
https://zenodo.org/communities/ipuom
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
5 WCIPT, 5th World Congress on Industrial Process Tomography,, Bergen, Norway, September 3rd to 6th 2007
EIT
Electrical Impedance Tomography
Knot wood
imaging
EIT Detection of Juvenile and Knot Wood in Southern Pine Logs
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:16956
2020-01-24T19:25:44Z
user-ipuom
openaire_data
Lionheart, William R.B.
2015-04-20
<p>This zip file contains a DICOM data set of magnetic resonance images a normal male mathematics professor aged 52. The experimental subject is the author. The MRI scans are T2 weighted turbo-spin-echo (T2W TSE) and T1 weighted Fast Field Echo (T1W FFE).</p>
<p>The subject suffers from a small vertical strabismus (hypertropia), a misalignment of the eyes, which is visible in this data set.</p>
<p>The author would like to thank the Radiology Department at the Macclesfield General Hospital for performing the scan.</p>
<p> </p>
https://doi.org/10.5281/zenodo.16956
oai:zenodo.org:16956
Zenodo
https://zenodo.org/communities/ipuom
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution Share Alike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/legalcode
MRI, head, brain, DICOM, T2W TSE, T1W FFE, normal male, human
An MRI DICOM data set of the head of a normal male human aged 52
info:eu-repo/semantics/other
oai:zenodo.org:7025167
2022-08-28T02:26:16Z
user-ipuom
openaire
Lionheart, William R.B.
2022-08-26
<p>Presentation at Quasilinear Equations, Inverse Problems and Their Applications, August 23–29, 2021, Educational Center “Sirius”, Sochi, Russian Federation. special session to celebrate the 75th anniversary of Vladimir Sharaftdinov</p>
https://doi.org/10.5281/zenodo.7025167
oai:zenodo.org:7025167
eng
Zenodo
https://zenodo.org/communities/ipuom
https://doi.org/10.5281/zenodo.7025166
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
QIPA2022, Quasilinear Equations, Inverse Problems and Their Applications,, Educational Center "Sirius", Sochi, Russian Federation., August 23–29 2022
rich tomography, tensor tomography, anisotropy, inverse problem, radar, diffraction, SAX
Applications of tomography of higher rank tensors
info:eu-repo/semantics/lecture
oai:zenodo.org:18295
2020-01-20T17:00:14Z
user-ipuom
user-mxif
openaire
Coban, S.B.
Withers, P.J.
Lionheart, W.R.B.
McDonald, S.A.
2015-06-01
<p>A driving force for the development of new reconstruction algorithms is to achieve better quality images using less information (lower dose, fewer projections, in less time), but under what circumstances do iterative methods become worth the effort? In this paper we propose a framework that enables the performance of reconstruction algorithms to be mapped. Such a framework allows fair comparisons to be made, providing insights into experimental acquisition strategies and methods of quantifying the quality of reconstructions, and identifying the sweet spot for different algorithms. </p>
https://doi.org/10.5281/zenodo.18295
oai:zenodo.org:18295
Zenodo
https://doi.org/10.5281/zenodo.16474
http://eprints.ma.man.ac.uk/2290/
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/mxif
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Fully3D 2015, The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Newport, Rhode Island, USA, May 31 to June 4, 2015
x-ray
computed tomography
iterative reconstruction methods
comparison of algorithms
sophiabeads-dataset
quantification
When do the iterative reconstruction methods become worth the effort?
info:eu-repo/semantics/lecture
oai:zenodo.org:17924
2020-01-20T16:58:09Z
user-ipuom
user-icbmaeit
Lionheart, William
Bayford, Richard
2003-04-23
<p>Abstracts from the 4th Conference on Biomedical Applications of Electrical Impedance Tomography held at the Department of Mathematics, University of Manchester Institute of Science and Technology (UMIST), April 23-25, 2003.</p>
https://doi.org/10.5281/zenodo.17924
oai:zenodo.org:17924
Zenodo
https://doi.org/10.1088/0967-3334/25/1/E01
https://zenodo.org/communities/icbmaeit
https://zenodo.org/communities/ipuom
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
EIT2003, Conference on Biomedical Applications of Electrical Impedance Tomography, UMIST, Manchester, UK, April 23-25, 2003
Electrical Impedance Tomography
Manchester
Proceedings of the 4th Conference on Biomedical Applications of Electrical Impedance Tomography: Abstracts
info:eu-repo/semantics/other
oai:zenodo.org:291413
2020-01-20T13:44:51Z
user-ipuom
openaire
William Lionheart
2016-06-23
<p>This was a keynote talk at ICEBI/EIt Stockholm 2016 on Electrical Impedance Tomography.My aim was to draw comparisons with other disciplines including electric fish and geophysics to see what we can learn. To highlight some current developments and challenges in<br>
bio-impedance and impedance imaging and ti look forward to possible future directions.</p>
<p> </p>
https://doi.org/10.5281/zenodo.291413
oai:zenodo.org:291413
Zenodo
https://zenodo.org/communities/ipuom
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ICEBI 16 and EIT 17, 16th International Conference on Electrical Bioimpedacne and 17th Conference on Electrcal Impedance Tomography, Stockholm, Sweden, 19-23 June 2016
EIT
bioimpedance
geophysics
weakly electric fish
Evolution of bio-impedance (and impedance imaging): from solid foundations and in to the future
info:eu-repo/semantics/lecture
oai:zenodo.org:18729
2020-01-20T17:20:47Z
user-ipuom
openaire
Lionheart, WRB
Ledger, PD
2015-06-18
<p>Recent work has demonstrated that the response of a conductive object to a low frequency alternating magnetic field, in which the eddy current approximation is valid, can be approximated using a rank-2 tensor dependent only on the shape, size,, conductivity and permeability of the object. We present numerical and experimental examples of these polarizability tensors and the relevance of these findings to the location of low-metallic content anti-personnel land mines, and distinguishing them from other buried objects.</p>
https://doi.org/10.5281/zenodo.18729
oai:zenodo.org:18729
Zenodo
https://zenodo.org/communities/ipuom
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
EMI2015, Engineering Mechanics Institute 2015, Stanford, USA, 16-19 June 2015
metal detectors
polarization tensor
polarizability tensor
eddy current
land mine
edge finite element
Remington 222
Characterization of metal objects using polarizability tensor
info:eu-repo/semantics/lecture
oai:zenodo.org:61394
2020-01-20T17:06:54Z
user-ipuom
user-mxif
openaire
Lionheart, William
2016-09-01
<p>We discuss several cases of what we call "Rich Tomography" problems in which more data is measured than a scalar for each ray. We give examples of infra red spectral tomography and Bragg edge neutron tomography in which the data is insufficient. For diffraction tomography of strain for polycrystaline materials we give an explicit reconstruction procedure. We go on to describe a way to find six independent rotation axes using Pascal's theorem of projective geometry</p>
This talk was given at an Inverse Problems seminar at University College London in 2013. Some of the material later appeared in Lionheart and Withers 2015 Diffraction Tomography of Strain http://iopscience.iop.org/article/10.1088/0266-5611/31/4/045005/meta
https://doi.org/10.5281/zenodo.61394
oai:zenodo.org:61394
Zenodo
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/mxif
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
rich tomography
infra red chemical species tomography
spectral tomography
Bragg edge tomography
x-ray strain tomography
transverse ray transform
Spectral and Diffraction Tomography
info:eu-repo/semantics/lecture
oai:zenodo.org:17559
2020-01-25T07:26:54Z
user-eidors-releases
user-ipuom
software
Adler, Andy
Boyle, Alistair
Crabb, Michael G.
Gagnon, Hervé
Grychtol, Bartłomiej
Lesparre, Nolwenn
Lionheart, William R. B.
2015-05-12
<p>EIDORS is a MATLAB based software library that aims to provide free software algorithms for forward modelling and inverse solutions of Electrical Impedance and (to some extent) Diffusion-based Optical Tomography, in medical, industrial and geophysical settings<br />
and to share data and promote collaboration.</p>
<p>Release 3.8 of EIDORS builds upon a strong foundation in reconstruction algorithms, adding and improving a number of aspects.</p>
https://doi.org/10.5281/zenodo.17559
oai:zenodo.org:17559
Zenodo
http://prdownloads.sf.net/eidors3d/eidors-v3.8.zip
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/eidors-releases
https://doi.org/
info:eu-repo/semantics/openAccess
GNU General Public License v2.0 only
https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
Electrical Impedance Tomography
Inverse problems
reconstruction software
EIDORS v3.8
info:eu-repo/semantics/other
oai:zenodo.org:61409
2020-01-20T17:28:14Z
user-ipuom
user-mxif
user-ccpi
openaire
Moe, Yngve M.
Lloyd, Ryan
Tregidgo, Henry
Coban, Sophia B.
Gajjar,Parmesh
Behnsen, Julia
Turner, Martin
Lionheart, William R.B.
2016-09-02
<p>3-dimensional computerised tomography is usually reconstructed using the naïve Feldkamp-Davis-Kress algorithm or FDK, which is not an exact reconstruction algorithm. This caus-<br>
es image errors, or artefacts that appear out of the central slice, and are especially visible near edges between horizontal layers - this is demonstrated in Figure 1.<br>
To overcome these artefacts; one can perform multiple scans at different heights and combine the reconstructed volumes. However, exact reconstruction methods are necessary to<br>
remove them altogether. These algorithms will also reduce the scanning time which makes them very attractive. Such methods exist, and Alexander Katsevich provided an algo-<br>
rithm already in 2002[1]. His algorithm uses a helical scan-geometry, rotating the X-Ray source while moving it in parallel with the rotation axis - or, equivalently, scanning the ob-<br>
ject while rotating it and moving it parallel to its rotation axis. In this poster, we demonstrate that it is possible to perform and reconstruct such scans, with a Nikon XTEK scanner<br>
designed for circular scans.</p>
https://doi.org/10.5281/zenodo.61409
oai:zenodo.org:61409
Zenodo
https://zenodo.org/communities/ipuom
https://zenodo.org/communities/ccpi
https://zenodo.org/communities/mxif
https://doi.org/
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
ToScA, Tomography for Scientific Advancement, Bath, UK, 6-7 September 2016
helical scan
cone beam CT
Katsevick
Nikon Xtek
artefacts
Implementing an exact algorithm for Helical CT:Removing the cone-beam artefacts
info:eu-repo/semantics/conferencePoster
oai:zenodo.org:56396
2020-01-20T17:42:25Z
user-ipuom
user-16thicebi17theit-talks
openaire
Lionheart, William
2016-06-23
<p>Key note talk at the 16 th International Conference on Electrical Bio-Impedance and 17 th International Conference on Electrical Impedance Tomography. Includes animation of transient potential by Michael Crabb and the MIRAN meshing and visualization software by Andrea Borsic</p>
https://doi.org/10.5281/zenodo.56396
oai:zenodo.org:56396
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https://zenodo.org/communities/16thicebi17theit-talks
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ICEBI and EIT 2016, 16 th International Conference on Electrical Bio-Impedance and 17 th International Conference on Electrical Impedance Tomography in Stockholm 2016, Stockholm, Sweden, 19-23 June 2016
bioimpedance,
EIT
impedance imaging
geophysics
weakly electric fish
transient
bayesian inverse problems
uncertainty quantification
MIRAN
Meshing
GPU
Evolution of bio-impedance (and impedance imaging): from solid foundations and in to the future
info:eu-repo/semantics/lecture
oai:zenodo.org:2540509
2020-01-24T19:25:58Z
user-ipuom
user-mxif
user-ccpi
openaire_data
user-eu
Fisher, Sarah L
Holmes, Danny J
Jørgensen, Jakob S
Gajjar, Parmesh
Behnsen, Julia
Lionheart, William R B
Withers, Philip J
2019-01-09
<p>This data accompanies the publication</p>
<p> </p>
<p><strong>Laminography in the Lab: Imaging planar objects using a conventional x-ray CT scanner</strong></p>
<p>by Sarah L Fisher, Danny J Holmes, Jakob S Jørgensen, Parmesh Gajjar, Julia Behnsen, William R B Lionheart and Philip J Withers</p>
<p>Measurement Science and Technology, Vol. 30, No. 3, 2019</p>
<p><a href="https://doi.org/10.1088/1361-6501/aafcae">https://doi.org/10.1088/1361-6501/aafcae</a></p>
<p> </p>
<p> </p>
<p>The computed laminography (CL) and limited angle CT (LACT) data sets for the lego sample of the paper are provided, including both raw projection data and the final reconstructions, a total of 4 files for each of the CL and LACT cases. The files are (for CL)</p>
<p>CLProjectionData.zip: zip file with the raw projection images and meta data as produced by the Nikon instrument.</p>
<p>CLShadingCorrection.zip: zip file containing dark and flat field images.</p>
<p>CLreconstruction.mat: MATLAB mat-file with final CL reconstruction, size 798x798x200 voxels.</p>
<p>CLreconstruction.vol: Same CL reconstruction but in a generic binary file that for example simplify the loading of data into Avizo.</p>
<p> </p>
<p>The same set of files is available for the CT data. The CT data reconstruction has size 798x798x798.</p>
<p> </p>
<p>Accompanying MATLAB reconstruction code is available from</p>
<p><a href="https://github.com/sarahfisher1/Laminography">https://github.com/sarahfisher1/Laminography</a></p>
https://doi.org/10.5281/zenodo.2540509
oai:zenodo.org:2540509
Zenodo
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https://zenodo.org/communities/ccpi
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https://doi.org/10.5281/zenodo.2540508
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x-ray
tomography
laminography
lego
Data for Laminography in the Lab: Imaging planar objects using a conventional x-ray CT scanner
info:eu-repo/semantics/other
oai:zenodo.org:16474
2020-05-06T12:34:00Z
user-ipuom
user-mxif
user-ccpi
openaire_data
Coban, S. B.
McDonald, S. A.
2015-03-30
<p><strong>SophiaBeads Dataset</strong> are acquired specifically for testing and comparing reconstruction methods for x-ray computed tomography. The sample is a plastic tube with a diameter of 25mm, filled with uniform Soda-Lime Glass (SiO2-Na2O) beads of diameters 2.5mm (with standard deviation 0.1mm). The sample has been scanned in the same conditions for each dataset, where the number of projections is halved after each batch scan (starting from 2048, down to 64 projections). This allows us to understand the effects of fewer projections, and develop algorithms that deliver quality results when the information content is low (i.e. faster scans or lower dose). </p>
<p> </p>
<p>These dataset are also suitable for developing and/or testing</p>
<ul>
<li>segmentation methods,</li>
<li>image or data correction techniques (e.g. centre of rotation),</li>
<li>forward and back project implementations, and</li>
<li>benchmarking user’s own code/method.</li>
</ul>
<p> </p>
<p>For the reconstruction and quantification examples, please see (will update with link to paper!)</p>
<p>To use these datasets for own research, please follow the technical report available via (http://eprints.ma.man.ac.uk/2290/), and the GitHub repository (http://sophilyplum.github.io/sophiabeads-datasets/). If publishing work with these datasets, <strong>the reader must cite the dataset <em>and</em> the codes separately</strong>. </p>
<p> </p>
<p>The dataset are taken using the <em>Nikon Custom Bay</em> machine (using a cone-beam geometry), located in the Manchester Xray Imaging Facility (www.mxif.manchester.ac.uk/resources/imaging-systems/nikon-custom-bay), by Sophia B. Coban and Samuel A. McDonald. </p>
<p> </p>
<p>Contact details for authors: </p>
<p>Sophia B. Coban: </p>
<ul>
<li>email: sophia.coban@gmail.com</li>
</ul>
<p>Samuel A. McDonald: </p>
<ul>
<li>email: sam.mcdonald@manchester.ac.uk</li>
</ul>
<p> </p>
<p>SophiaBeads Dataset Project Codes (DOI): https://zenodo.org/record/16539</p>
https://doi.org/10.5281/zenodo.16474
oai:zenodo.org:16474
Zenodo
https://doi.org/10.5281/zenodo.16539
https://doi.org/10.5281/zenodo.16539
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https://doi.org/
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Creative Commons Attribution Share Alike 4.0 International
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computed tomography
x-ray
cone beam
reconstruction algorithms
sophiabeads-dataset
segmentation
data correction
large dataset
SophiaBeads Dataset Project
info:eu-repo/semantics/other