{"id":30,"date":"2018-09-28T18:09:55","date_gmt":"2018-09-28T18:09:55","guid":{"rendered":"http:\/\/easi-cil.compute.dtu.dk\/?page_id=30"},"modified":"2023-03-21T16:33:17","modified_gmt":"2023-03-21T16:33:17","slug":"music","status":"publish","type":"page","link":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/datasets\/music\/","title":{"rendered":"Multi-Spectral Imaging via Computed Tomography (MUSIC)"},"content":{"rendered":"

The Mu<\/strong>lti-S<\/strong>pectral I<\/strong>maging via C<\/strong>omputed Tomography (MUSIC) dataset in a two-part (2D- and 3D spectral) open access dataset for advanced image analysis of spectral radiographic (x-ray) scans, their tomographic reconstruction and the detection of specific materials within such scans. The scans operate at a photon energy range of around 20 keV up to 160 keV (see figure).<\/p>\n

\"MSUIC
illustration of the energy spectrum (highlight in green) that is covered by the MUSIC dataset. Image from Kehl et a. 2018 (preprint).<\/figcaption><\/figure>\n

<\/p>\n

The dataset includes — for 2D- as well as 3D spectral data — the corrected (e.g. calibrated) radiographic projections, their tomographic reconstructions (based on 37 projections of 256 detector pixels into a 100×100 pixel CT image per slice) and the corresponding set of segmentation variants demonstrated in Kehl et al. 2018 (preprint<\/a>).<\/p>\n

The dataset is actively curated by the group and new segmentation- and further also classification data will become available in the future.<\/p>\n

We hope to contribute with this open access dataset to continuing research on topics of tomographic reconstruction and correction (e.g. compressed-sensing reconstruction, metal artifact reduction, inverse problems in practice), (hyper-)spectral image- and volume segmentation, material classification in CT and — as a final goal of the CIL project — the increase in airport security with these methods.<\/p>\n

The MUSIC dataset is part of the CIL (C<\/strong>heck-I<\/strong>n L<\/strong>uggage) project, funded by the ‘Innovation Fund Denmark’ in collaboration with Innospexion and Exruptive.<\/p>\n

The dataset:<\/p>\n

MUSIC 2D spectral – HDF5 data (MatLab\/Octave\/Python)<\/a>: \"compressedArchive\"<\/a><\/p>\n

MUSIC 2D spectral – MHD data (Python\/C++\/Paraview\/MITK\/etc.)<\/a>: \"compressedArchive\"<\/a><\/p>\n

MUSIC 3D spectral – HDF5 data (MatLab\/Octave\/Python)<\/a>: \"compressedArchive\"<\/a><\/p>\n

MUSIC 3D spectral – MHD data (Python\/C++\/Paraview\/MITK\/etc.)<\/a>: \"compressedArchive\"<\/a><\/p>\n

Now, the software for graph-based segmentation given in the paper is also available for Windows 64-bit in binary form: \"compressedArchive\"<\/a><\/p>\n

For building from source – working in Linux and on Windows via MinGW32-x86_64-w64 – you can use the official github link: \"Github<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The Multi-Spectral Imaging via Computed Tomography (MUSIC) dataset in a two-part (2D- and 3D spectral) open access dataset for advanced image analysis of spectral radiographic (x-ray) scans, their tomographic reconstruction and the detection of specific materials within such scans. The scans operate at a photon energy range of around 20 keV up to 160 keV… Continue Reading →<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":7,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-30","page","type-page","status-publish","hentry","clear"],"_links":{"self":[{"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/pages\/30","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/comments?post=30"}],"version-history":[{"count":10,"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/pages\/30\/revisions"}],"predecessor-version":[{"id":93,"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/pages\/30\/revisions\/93"}],"up":[{"embeddable":true,"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/pages\/7"}],"wp:attachment":[{"href":"http:\/\/easi-cil.compute.dtu.dk\/index.php\/wp-json\/wp\/v2\/media?parent=30"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}