TY - ADVS
T1 - Hyperspectral X-ray CT Voxelized TV reconstruction of a single, iodine-stained lizard head sample
AU - Warr, Ryan
AU - Ametova, Evelina
AU - Cernik, Robert
AU - Fardell, Gemma
AU - Handschuh, Stephan
AU - Jørgensen, Jakob Sauer
AU - PAPOUTSELLIS, EVANGELOS
AU - Pasca, Edoardo
AU - Withers, Philip
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Dataset description These datasets are voxel based reconstructions of hyperspectral CT data using the Core Imaging Library (CIL). They are stored as NeXus files (derived from hdf5) which can be read in, visualised and manipulated using CIL. - PDHG_TV_1000_Sp_alpha_0.004.nxs Is the solution after 1000 iterations of PDHG with TV applied in the spatial domain. - PDHG_TV_1000_SpCh_alpha_0.003_beta_0.5.nxs Is the solution after 1000 iterations of PDHG with TV applied both in the spatial domain, and in the energy (channel) domain. Dataset intended use These datasets are used in the CIL training notebook: https://github.com/TomographicImaging/CIL-Demos/blob/main/examples/3_Multichannel/03_Hyperspectral_reconstruction.ipynb They can be imported using CIL, with the following code snippet: from cil.io import NEXUSDataReader reader = NEXUSDataReader(file_name='path/to/data/PDHG_TV_1000_Sp_alpha_0.004.nxs') data = reader.read()
AB - Dataset description These datasets are voxel based reconstructions of hyperspectral CT data using the Core Imaging Library (CIL). They are stored as NeXus files (derived from hdf5) which can be read in, visualised and manipulated using CIL. - PDHG_TV_1000_Sp_alpha_0.004.nxs Is the solution after 1000 iterations of PDHG with TV applied in the spatial domain. - PDHG_TV_1000_SpCh_alpha_0.003_beta_0.5.nxs Is the solution after 1000 iterations of PDHG with TV applied both in the spatial domain, and in the energy (channel) domain. Dataset intended use These datasets are used in the CIL training notebook: https://github.com/TomographicImaging/CIL-Demos/blob/main/examples/3_Multichannel/03_Hyperspectral_reconstruction.ipynb They can be imported using CIL, with the following code snippet: from cil.io import NEXUSDataReader reader = NEXUSDataReader(file_name='path/to/data/PDHG_TV_1000_Sp_alpha_0.004.nxs') data = reader.read()
U2 - 10.5281/zenodo.7016574
DO - 10.5281/zenodo.7016574
M3 - Data set/Database
PB - Zenodo
ER -