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Micro-CT dataset of Rijksmuseum cornett top half (2/2)
<p><strong>Summary</strong></p> <p>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. BK-AM-62-B; https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B). This dataset contains tile 9-15 (out of 15), dataset 2 (out of 2) covering the top half of the cornetto.</p> <p>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</p> <p><em> </em></p> <p><strong>Sample Information</strong></p> <p>The sample is cornett, height 56.0 cm x width 10.0 cm x diameter 3.5 cm , c. 1600 - c. 1650 [Rijksmuseum inventory number BK-NM-62-B, https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B]. It was mounted in a custom made foam stand on the rotation stage. See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and results [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] for analysis of the reconstructed images. <em> <br><br></em></p> <p><strong><em>Experimental Plan</em></strong></p> <p>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</p> <p>To image the entire object, thirty tiles were scanned with SOD = 734 and SDD = 1098, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time.</p> <p>For the reconstructed CT volume, see 10.5281/zenodo.14265065.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>This dataset contains tile 9-15 (out of 15), dataset 2 (out of 2) covering the top half of the cornetto.</p> <p>Each tile data folder (T*) contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter) image before acquisition, <em>io000000.tif</em></li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p> </p> <p><strong>References</strong></p> <p>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong>F.G.Bossema</strong>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8">J. Dorscheid, <strong>F.G. Bossema</strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). The dual space: Concept and applications in cultural heritage. <em>IEEE BITS the Information Theory Magazine</em>, <em>2</em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</p> <p>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J.,</p> <p>2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11,</p> <p>100364. https://doi.org/10.1016/j.softx.2019.100364</p> <p> </p>
Micro-CT dataset of Rijksmuseum cornett top half (1/2)
<p><strong>Summary</strong></p> <p>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. BK-AM-62-B; https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B). This dataset contains tile 1-8 (out of 15), dataset 1 (out of 2) covering the top half of the cornetto.</p> <p>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</p> <p><em> </em></p> <p><strong>Sample Information</strong></p> <p>The sample is cornett, height 56.0 cm x width 10.0 cm x diameter 3.5 cm , c. 1600 - c. 1650 [Rijksmuseum inventory number BK-NM-62-B, https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B]. It was mounted in a custom made foam stand on the rotation stage. See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and results [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] for analysis of the reconstructed images. <em> <br><br></em></p> <p><strong><em>Experimental Plan</em></strong></p> <p>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</p> <p>To image the entire object, thirty tiles were scanned with SOD = 734 and SDD = 1098, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time.</p> <p>For the reconstructed CT volume, see 10.5281/zenodo.14265065.</p> <p> </p> <p><strong>List of Contents</strong></p> <p>This dataset contains tile 1-8 (out of 15), dataset 1 (out of 2) covering the top half of the cornetto.</p> <p>Each tile data folder (T*) contains:</p> <ul> <li>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</li> <li>flat-field (or open-shutter) image before acquisition, <em>io000000.tif</em></li> <li>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</li> <li><em>data settings XRE.txt</em>, a text file with scanner metadata</li> </ul> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p> </p> <p><strong>References</strong></p> <p>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong>F.G.Bossema</strong>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8">J. Dorscheid, <strong>F.G. Bossema</strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). The dual space: Concept and applications in cultural heritage. <em>IEEE BITS the Information Theory Magazine</em>, <em>2</em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</p> <p>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J.,</p> <p>2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11,</p> <p>100364. https://doi.org/10.1016/j.softx.2019.100364</p> <p> </p>
Micro-CT dataset of Rijksmuseum cornett bottom half (1/2)
<p><strong><span>Summary</span></strong></p> <p><span>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. </span><span>BK-AM-62-B; <a href="https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B">https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B</a>)</span><span>. </span><span>This dataset contains tile 1-8 (out of 15), dataset 1 (out of 2) covering the bottom half of the cornetto.</span></p> <p><span>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022]. </span></p> <p><em><span> </span></em></p> <p><strong><span>Apparatus</span></strong></p> <p><span>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</span></p> <p><em><span> </span></em></p> <p><strong><span>Sample Information</span></strong></p> <p><span>The sample is cornett</span><span>, </span><span><span>height 56.0 cm x width 10.0 cm x diameter 3.5 cm</span></span><span> , </span><span>c. 1600 - c. 1650</span><span> </span><span>[</span><span>Rijksmuseum inventory number</span><span> BK-NM-62-B, </span><span><span> </span></span><span>https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B</span><span>]. </span><span>It was mounted in a custom made foam stand on the rotation stage. <span> </span>See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and </span><span>results [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] </span><span>for analysis of the reconstructed images. <em> <br><br></em></span></p> <p><strong><em><span>Experimental Plan</span></em></strong></p> <p><span>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</span></p> <p><span>To image the entire object, thirty tiles were scanned with SOD = 734 and SDD = 1098, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time. </span></p> <p><span>For the reconstructed CT volume, see </span><span>10.5281/zenodo.14265065</span><span>. </span></p> <p><span> </span></p> <p><strong><span>List of Contents</span></strong></p> <p><span>This dataset contains tile 1-8 (out of 15), dataset 1 (out of 2) covering the bottom half of the cornetto.</span></p> <p><span>Each tile data folder (T*) contains:</span></p> <ul> <li><span>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</span></li> <li><span>flat-field (or open-shutter) image before acquisition, <em>io000000.tif</em></span></li> <li><span>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</span></li> <li><em><span>data settings XRE.txt</span></em><span>, a text file with scanner metadata</span></li> </ul> <p><strong><span>Additional Links</span></strong></p> <p><span>These datasets are produced by the </span><span><a><span>Computational Imaging group</span></a></span><span> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's </span><span><a><span>GitHub page</span></a></span><span>.</span></p> <p><em><span> </span></em></p> <p><strong><span>Contact Details</span></strong></p> <p><span>For more information or guidance in using these datasets, please get in touch with </span></p> <ul> <li><span>bossema [at] cwi.nl</span></li> </ul> <p><em><span> </span></em></p> <p><strong><span>Acknowledgments</span></strong></p> <p><span>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers </span><span>341-60-001, </span><span>639.073.506 and </span><span>628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</span></p> <p><span> </span></p> <p><strong><span>References</span></strong></p> <p><span>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</span></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong><span>F.G.Bossema</span></strong><span>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</span></a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8"><span>J. Dorscheid, <strong><span>F.G. Bossema</span></strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</span></a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). <span>The dual space: Concept and applications in cultural heritage. <em><span>IEEE BITS the Information Theory Magazine</span></em>, <em><span>2</span></em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</span></p> <p><span>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J.,</span></p> <p><span>2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11,</span></p> <p><span>100364. https://doi.org/10.1016/j.softx.2019.100364</span></p> <p></p>
Micro-CT dataset of Rijksmuseum cornett bottom half (2/2)
<p><strong><span>Summary</span></strong></p> <p><span>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. </span><span>BK-AM-62-B; <a href="https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B">https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B</a>)</span><span>. </span><span>This dataset contains tile 9-15 (out of 15), dataset 2 (out of 2) covering the bottom half of the cornetto.</span></p> <p><span>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022]. </span></p> <p><em><span> </span></em></p> <p><strong><span>Apparatus</span></strong></p> <p><span>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</span></p> <p><em><span> </span></em></p> <p><strong><span>Sample Information</span></strong></p> <p><span>The sample is cornett</span><span>, </span><span><span>height 56.0 cm x width 10.0 cm x diameter 3.5 cm</span></span><span> , </span><span>c. 1600 - c. 1650</span><span> </span><span>[</span><span>Rijksmuseum inventory number</span><span> BK-NM-62-B, </span><span><span> </span></span><span>https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B</span><span>]. </span><span>It was mounted in a custom made foam stand on the rotation stage. <span> </span>See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and </span><span>results [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] </span><span>for analysis of the reconstructed images. <em> <br><br></em></span></p> <p><strong><em><span>Experimental Plan</span></em></strong></p> <p><span>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</span></p> <p><span>To image the entire object, thirty tiles were scanned with SOD = 734 and SDD = 1098, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time. </span></p> <p><span>For the reconstructed CT volume, see </span><span>10.5281/zenodo.14265065</span><span>. </span></p> <p><span> </span></p> <p><strong><span>List of Contents</span></strong></p> <p><span>This dataset contains tile 9-15 (out of 15), dataset 2 (out of 2) covering the bottom half of the cornetto.</span></p> <p><span>Each tile data folder (T*) contains:</span></p> <ul> <li><span>dark-field (or closed-shutter) image, <em>di000000.tif</em>,</span></li> <li><span>flat-field (or open-shutter) image before acquisition, <em>io000000.tif</em></span></li> <li><span>raw (unprocessed or uncorrected) projections, <em>scan_*.tif</em>,</span></li> <li><em><span>data settings XRE.txt</span></em><span>, a text file with scanner metadata</span></li> </ul> <p><strong><span>Additional Links</span></strong></p> <p><span>These datasets are produced by the </span><span><a><span>Computational Imaging group</span></a></span><span> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's </span><span><a><span>GitHub page</span></a></span><span>.</span></p> <p><em><span> </span></em></p> <p><strong><span>Contact Details</span></strong></p> <p><span>For more information or guidance in using these datasets, please get in touch with </span></p> <ul> <li><span>bossema [at] cwi.nl</span></li> </ul> <p><em><span> </span></em></p> <p><strong><span>Acknowledgments</span></strong></p> <p><span>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers </span><span>341-60-001, </span><span>639.073.506 and </span><span>628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</span></p> <p><span> </span></p> <p><strong><span>References</span></strong></p> <p><span>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</span></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong><span>F.G.Bossema</span></strong><span>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</span></a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8"><span>J. Dorscheid, <strong><span>F.G. Bossema</span></strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</span></a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). <span>The dual space: Concept and applications in cultural heritage. <em><span>IEEE BITS the Information Theory Magazine</span></em>, <em><span>2</span></em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</span></p> <p><span>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J.,</span></p> <p><span>2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11,</span></p> <p><span>100364. https://doi.org/10.1016/j.softx.2019.100364</span></p> <p></p>
Micro-CT reconstruction of Rijksmuseum cornett
<p><strong>Summary</strong></p> <p>This submission contains a micro-CT reconstruction of a cornett from the Rijksmuseum collection (obj. nr. BK-AM-62-B; <a href="https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B">https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B</a>).</p> <p>The data relates to [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022].</p> <p><em> </em></p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the custom-built and highly flexible CT scanner, FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. This apparatus consists of a cone-beam microfocus X-ray point source that projects polychromatic X-rays onto a 1944-by-1536 pixels, 14-bit, flat detector panel. Full details can be found in [Coban, 2020].</p> <p><em> </em></p> <p><strong>Sample Information</strong></p> <p>The sample is an oak sculpture of a [Rijksmuseum inventory number BK-NM-62-B, https://www.rijksmuseum.nl/nl/collectie/BK-AM-62-B]. It was mounted in a custom made foam stand on the rotation stage.</p> <p>See Figure 10 in [Bossema, 2021] for a picture of the object and the mount and [Bossema, 2021], [Dorscheid, 2022] and [Van Liere, 2022] for analysis of the reconstructed images. <em> <br><br></em></p> <p><strong><em>Experimental Plan</em></strong></p> <p>The data in this submission was collected to illustrate the scanning process to iteratively include feedback from cultural heritage experts [Bossema, 2021] and was later used for a detailed investigation of the current state and earlier restoration treatments [Dorscheid, 2022] and for illustrating the dual space method [Van Liere, 2022].</p> <p>To image the entire object, thirty tiles were scanned at image resolution 50 micron, in two sessions of 15 tiles (5 vertical, 3 horizontal). For each scan, the sample was rotated 360° in circular and continuous motion, with a dark-field (closed-shutter), and flat-field (open-shutter) images taken before the acquisition. The datasets of each tile consist of 1200 projections, at 70kV, 42W, 300ms exposure time.</p> <p>This dataset contains the reconstruction that was afterwards made using FleXbox [Kostenko, 2019], using the SIRT algorithm for 200 iterations with binning 2 on the radiographs, resulting in a voxel size of 100 micron<em>. </em></p> <p><strong>List of Contents</strong></p> <p>The content of the submission is given below.</p> <ul> <li>BK-AM-62-B_cornetto_recon <ul> <li>cornetto_tiling_recon_24.py</li> <li>tiling1 <ul> <li>recon24 <ul> <li>merged_volume_bin2</li> </ul> </li> </ul> </li> <li>tiling2 <ul> <li>recon24 <ul> <li>merged_volume_bin2</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p>The python script was used to obtain the reconstructions. The merged_volume_bin2 folders contain the reconstructions, in .tiff format of the top half (tiling1) and bottom half (tiling2) of the cornett.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>These datasets are produced by the <a>Computational Imaging group</a> at Centrum Wiskunde & Informatica (CI-CWI). For any relevant Python/MATLAB scripts for the FleX-ray datasets, we refer the reader to our group's <a>GitHub page</a>.</p> <p><em> </em></p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please get in touch with </p> <ul> <li>bossema [at] cwi.nl</li> </ul> <p><em> </em></p> <p><strong>Acknowledgments</strong></p> <p>The authors would like to acknowledge the funding from the Netherlands Organisation for Scientific Research (NWO), project numbers 341-60-001, 639.073.506 and 628.007.033 and Netherlands Institute for Conservation, Art and Science (NICAS).</p> <p> </p> <p><strong>References</strong></p> <p>S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, “Explorative imaging and its implementation at the FleX-ray Laboratory,” <em>J. Imaging</em>, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.</p> <p><a href="https://www.sciencedirect.com/science/article/pii/S1296207421000558"><strong>F.G.Bossema</strong>, S.B. Coban, A. Kostenko, P. van Duin, J. Dorscheid, I. Garachon, E. Hermens, R. van Liere, K. J. Batenburg, “Integrating expert feedback on the spot in a time-efficient explorative CT scanning workflow for cultural heritage objects”, Journal of Cultural Heritage, Vol. 49, p38-47, 2021</a></p> <p><a href="https://heritagesciencejournal.springeropen.com/articles/10.1186/s40494-022-00800-8">J. Dorscheid, <strong>F.G. Bossema</strong>, P. van Duin, S.B. Coban, R. van Liere, K.J. Batenburg, G.P. Di Stefano, “Looking under the skin – multi-scale CT scanning of a peculiarly constructed cornett in the Rijksmuseum”, Heritage Science 10, 161 (2022)</a></p> <p>R. van Liere, K.J. Batenburg, I. Garachon, C.-L. Wang, J. Dorscheid (2022). The dual space: Concept and applications in cultural heritage. <em>IEEE BITS the Information Theory Magazine</em>, <em>2</em>(1), 49–57. doi:10.1109/MBITS.2022.3202508</p> <p>Kostenko, A., Palenstijn, W.J., Coban, S.B., Hendriksen, A.A., van Liere, R., Batenburg, K.J., 2020. Prototyping X-ray tomographic reconstruction pipelines with FleXbox. SoftwareX 11, 100364. https://doi.org/10.1016/j.softx.2019.100364</p> <p> </p> <p> </p>
Original CT image stacks of five fossil petrosal bones from Siberia, 3D PDF files of reconstructed endocasts, blood vessels and innervation patterns, 3D PDF instruction file, STL files of the petrosals
<p>Five partially preserved mammaliaform petrosals recovered from Middle Jurassic sediments of the Berezovsk coal mine (Krasnoyarsk Krai, Russia) show similarities to other early mammaliaforms like the morganucodontan <i>Morganucodon</i> and the docodontan <i>Haldanodon</i> in having an inflated promontorium, and a curved and apically inflated cochlear canal. But they are distinct from dryolestoid and derived mammalian petrosals by the weak coiling of the cochlear duct and the presence of a perilymphatic foramen with an open perilymphatic sulcus. The two larger and robust specimens exhibit striking similarities to docodontan petrosals. Inside the bone an intricate circumpromontorial venous plexus was discovered as recently described for the docodontan <i>Borealestes</i>, confirming this structure is consistently present in basal mammaliaforms. The three smaller and slender petrosals likely belong to haramiyidans and are unique in showing a septum-like structure medially along the cochlear nerve entrance. The protruding perforated bony bar that is preserved in two of the three is interpreted here to be a remnant of a bony septum with multiple foramina for cochlear nerve fibers, representing an autapomorphic feature of Haramiyida. This newly described passageway for nerve fibers shows that the formation of the osteological structure surrounding the nervous pathways of the cochlea is more plastic among the most basal mammaliaforms than previously thought.</p>
Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment.
<p><strong>Considerations on baseline generation for Imaging AI studies illustrated on the CT-based prediction of empyema and outcome assessment.</strong></p> <p><strong>Introduction</strong>: For AI-based classification tasks in computed tomography, a reference standard for evaluating the clinical diagnostic accuracy of individual classes is essential. To enable the implementation of an AI tool in clinical practice, this should be drawn from clinical routine data, using State-of-the-art scanners, evaluated in a blinded manner, and verified with a reference test.</p> <p><strong>Methods: </strong>2659 consecutive CTs performed between 01/2016 and 01/2021 with reported pleural effusion were retrospectively included. Pathology reports from thoracocentesis or biopsy within 7 days of CT were used as reference standard (n = 335). Two radiologists (4 and 10 PGY) blindly assessed chest CTs (n=335, 81 empyemas) for pleural CT features and ICC was determined. In addition, both pleural CT features and radiological diagnosis were extracted from written radiological reports. If needed, consensus was achieved using an experienced radiologist's opinion (29 PGY). We assessed the correlation of these findings with the following patient outcomes: mortality and median hospital stay.</p> <p><strong>Results: </strong>Specificity and sensitivity for clinical detection of empyema (N=81) were 90.94 (95%-CI 86.55-94.05) and 72.84 (95%-CI: 61.63-81.85%) in all effusions, with moderate to almost perfect interrater agreement for all pleural findings associated with empyema (Cohen's kappa = 0.41-0.82). Features describing pleural enhancement or thickening achieved the highest accuracy with 87.02% and 81.49%, respectively. Empyema was associated with a longer hospital stay (median= 20 versus 14 days), and findings consistent with pleural carcinosis impacted mortality. </p>
Characterisation Data - CNT CF CT
<p>cf_3d.ctan</p>
Characterisation Data - CNT CF CT
<p>pristine_3D</p>
Characterisation Data - CNT CF CT
<p>cf__ir_rec.ctan</p>
CT-Based Radiomics Analysis to predict histo-pathological outcomes following liver resection in Colorectal Liver Metastases
<p>I uploaded the images of the manuscript "CT-Based Radiomics Analysis to predict histo-pathological outcomes following liver resection in Colorectal Liver Metastases".</p> <p>The objective of the study was to assess the radiomics features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with liver colon rectal metastases in order to predict histo-pathological outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.</p>
Seismogenic process of fluid injection revealed by in situ dynamic CT scanning
<p>In the process of unconventional energy exploitation, large volumes of fluid are injected into low permeability reservoirs for hydraulic transformation to generate a fracture network conducive to fluid migration. Unfortunately, extensive fluid injection can induce earthquakes, endangering human lives and causing serious economic losses. Understanding the seismogenic process of fluid injection within a reservoir helps to minimize the induced earthquakes. To better monitor the deformation changes in reservoirs, we conducted in situ dynamic X-ray computed tomography (CT) during a series of experiments lasting 3 minutes for real-time identification of the location and geometry of hydraulic fractures and found that the seismogenic process of fluid injection with a wide range of frequencies is triggered by water–rock interactions.</p> <p>The data we uploaded can be divided into two main parts. The first part is the confining, axial and water injection pressures recorded during the hydraulic fracturing experiment. The second part is the CT scans of the rock sample during the hydraulic fracturing experiment.</p>
micro-CT data and segmentation files for "The ant abdomen: the skeletomuscular and soft tissue anatomy of Amblyopone australis workers (Hymenoptera: Formicidae)"
<p>Two compressed (.zip) files are included. One (Amblyopone_australis_CASENT0753222_CT.zip) contains a directory of 2149 .tif files comprising the micro-CT dataset used in the study. The other (Amblyopone_australis_CASENT0753222_Segmentation.zip) contains a .ORSSession (ORS Dragonfly) file with the dataset and all segmentation labels created in the study. The segmentation labels are organized into multi-regions of interest (multiROIs) for organizational purposes.</p>
Comparability of skeletal fibulae surfaces generated by different source scanning (dual-energy CT scan vs. high resolution laser scanning) and 3D geometric morphometric validation
<p><strong>SI_Appendix 1.</strong> Matrix of Cartesian coordinates of analyzed specimens.</p> <p><strong>SI_Appendix 2</strong>. Sample list and acquisition methods. </p>
Results of the CT competition at the 11th International Workshop on Combinatorial Testing
<p>These are the models and raw results achieved by participating tools in the CT competition at the 11th International Workshop on Combinatorial Testing (IWCT 2022).</p> <p>The file ct-comp-models.tar.bz2 contains the input files in CTWedge and ACTS format.</p> <p>The file ct-comp-final-results.tar.bz2 contains one folder per tool. In each of these folders, files conform to the following syntax:[toolname]_[model_filename]_[strength]_[iteration].[suffix]<br> where "toolname" is identical to the current folder,<br> "model_filename" is the exact name of the model file (including suffix),<br> "strength" is the combinatorial strength of the resulting CA (between 2 and 5),<br> "iteration" takes values between 1 and 3 and refers to the round of execution (at lower strengths, tools were tested using 3 iterations; at higher strengths, some tools required too much execution time and were only run once),<br> "suffix" is one of the following: "time" for the output of GNU time in verbose mode; "out" for the resulting CA (in headerless CSV form); "err" for the standard output (usually empty). Note that additional intermediate files may exist for some tools (e.g. when their original output is not strictly a headerless CSV).</p> <p>All tools were executed in Docker containers based on the official OpenJDK image, which is itself based on Debian Buster (Docker identifier: openjdk:14-buster). The experiments were performed on a machine using a Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz (16 physical cores, 32 logical cores) with 256 GB RAM.</p>
Multicenter CT phantoms public dataset for radiomics reproducibility tests
<p>No description provided.</p>
Supporting CT Data for: Two exceptionally preserved juvenile specimens of Gorgosaurus libratus (Tyrannosauridae, Albertosaurinae) provide new insight into the timing of ontogenetic changes in tyrannosaurids
<p>Known from dozens of specimens discovered since the early 20<sup>th</sup> century, <em>Gorgosaurus libratus</em> has arguably contributed more than any other taxon to our understanding of the life history of tyrannosaurids. However, juvenile material for this taxon is rare. Here, we describe two small, articulated <em>Gorgosaurus</em> specimens (skull lengths of ~500 mm) that help advance our knowledge of the anatomy and ontogeny of this taxon and of tyrannosaurids in general. The new specimens exhibit hallmark juvenile tyrannosaurid features, including long, low, and narrow skulls, large circular orbits, absent or incipient cranial ornamentation, ziphodont dentition, and an overall gracile skull frame. Comparison with other <em>Gorgosaurus</em> specimens of various ontogenetic stages allows for an examination of the timing of morphological changes that occurred through ontogeny in this taxon relative to other tyrannosaurids. Of particular note, <em>Gorgosaurus</em> and the larger <em>Tyrannosaurus rex</em> are found to have experienced similar ontogenetic transformations at similar percent skull length relative to the large known individuals for each respective taxon but at different absolute body sizes and biological ages, occurring at a larger size and older age in <em>T. rex</em> than in <em>Gorgosaurus</em>. These results suggest a dissociation between the timing of cranial development and body size in tyrannosaurids. Finally, the recognition of ontogenetically invariant characters in <em>Gorgosaurus</em> makes it possible to determine the taxonomic identity of previously misidentified specimens.</p>
Supplementary material 1 from: Krzikowski M, Nguyen TQ, Pham CT, Rödder D, Rauhaus A, Le MD, Ziegler T (2022) Assessment of the threat status of the amphibians in Vietnam - Implementation of the One Plan Approach. Nature Conservation 49: 77-116. https://doi.org/10.3897/natureconservation.49.82145
Assessment of the threat status of the amphibians in Vietnam - Implementation of the One Plan Approach
abnormal CT findings among patients with abdominal pain in the radiology department of a teriary care center
<p>These are Data were collected from the hospital system regarding demographic characteristics, past medical and surgical history, symptom description and physical examination findings, diagnosis (both initial and final), laboratory tests, CT findings, and results from other requested radiology modalities. All information was obtained after obtaining ethical approval.</p>
Normalized CT images and reference segmentations of thoracic and lumbar vertebrae from the CSI 2014 workshop
<p>This is the dataset of the vertebra segmentation challenge of the <a href="http://csi-workshop.weebly.com/challenges.html">CSI 2014 workshop</a> that was held in conjunction with MICCAI 2014.</p> <ul> <li><strong>1-10</strong>: Training set, scans of 10 young adult (16-35 years old)</li> <li><strong>11-15</strong>: Test set, scans of 5 young adult (20-35 years old)</li> <li><strong>16-20</strong>: Test set, scans of 5 patients with vertebral compression fractures </li> </ul> <p>Scans were acquired at the Department of Radiological Sciences, University of California, Irvine, School of Medicine and were published under the <a href="http://opendatacommons.org/licenses/pddl/1.0/">ODC Public Domain Dedication and License</a> on <a href="http://spineweb.digitalimaginggroup.ca/">SpineWeb</a> (datasets 2 and 15). The dataset and challenge is further described in this publication: <a href="http://dx.doi.org/10.1016/j.compmedimag.2015.12.006">A multi-center milestone study of clinical vertebral CT segmentation</a></p> <p>The data that is published here has been normalized:</p> <ul> <li>Voxel values are Hounsfield values and have been clipped to [-1000, 3095]</li> <li>Image origin has been set to 0,0,0</li> <li>Image orientation has been standardized to RAI orientation</li> <li>Small islands and other obvious mistakes have been removed</li> <li>Segmentation masks have been smoothed with a 2x2x2 median filter</li> <li>Vertebrae have been anatomically labeled (8 = T1, 9 = T2, ..., 24 = L5)</li> <li>Because not always all visible vertebrae were segmented in the original data, only segmentations of the thoracic and lumbar vertebrae have been retained</li> </ul> <p><strong>License</strong></p> <p>This dataset is released under the <a href="https://opendatacommons.org/licenses/by/1-0/">Open Data Commons Attribution License</a> (which the original license allows me to do). When using this dataset for publication of any kind, please reference the following paper to meet the attribution requirement:</p> <blockquote> <p>Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, Hammernik K, Urschler M, Ibragimov B, Korez R, Vrtovec T, Castro-Mateos I, Pozo JM, Frangi AF, Summers RM, Li S. A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph. 2016; 49:16-28. doi: 10.1016/j.compmedimag.2015.12.006.</p> </blockquote> <p>There is no need to reference this upload, referencing the original authors is sufficient.</p> <p><strong>Notes</strong></p> <p>The dataset contains 2 cases with only 4 lumbar vertebrae in which L5/S1 has not been segmented (i.e., label 25 is missing).</p> <p>The resolution and segmentation quality of the diseased cases (16-20) is quite low.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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