LoDoInd: A Benchmark Low-dose Industrial CT Dataset - 1 of 3

<h2>Summary</h2> <p>This dataset accompanies the paper "LoDoInd: Introducing A Benchmark Low-dose Industrial CT Dataset and Enhancing Denoising with 2.5D Deep Learning Techniques". We are releasing the dataset with five different dose levels, including a reference set. All datasets are pre-registered, making them immediately suitable for deep learning applications in industrial CT.</p> <h2>Description</h2> <p>The uploaded content includes reconstructed images for noise levels 1 and 2. Each level comprises 4000 slices, with each slice being 1250x1250 pixels. Due to the 50 GB space limitation per submission on Zenodo, the rest of the dataset is available through separate links listed below:</p> <ul> <li>Noise1 and Noise2 (this one) <a href="../records/10356955" target="_blank" rel="noopener">https://zenodo.org/records/10356955</a></li> <li>Noise3 and Noise4 <a href="../records/10391277" target="_blank" rel="noopener">https://zenodo.org/records/10391277</a></li> <li>Noise5 and Reference <a href="../records/10391412" target="_blank" rel="noopener">https://zenodo.org/records/10391412</a></li> </ul> <p>The scanning parameters for all noise levels are summarized in the table below:</p> <table> <tbody> <tr> <td>&nbsp;</td> <td>Averaged Projs</td> <td>Exposure Time/ms</td> <td>Scan Time/min</td> <td>Voltage/kV</td> <td>Current/uA</td> </tr> <tr> <td>Reference</td> <td>6</td> <td>333</td> <td>59.3</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 1</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>180</td> </tr> <tr> <td>Noise Level 2</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>90</td> </tr> <tr> <td>Noise Level 3</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>45</td> </tr> <tr> <td>Noise Level 4</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>23</td> </tr> <tr> <td>Noise Level 5</td> <td>1</td> <td>333</td> <td>9.9</td> <td>140</td> <td>12</td> </tr> </tbody> </table> <h2>Additional link</h2> <p>The code for supervised learning-based denoising is available <a href="https://github.com/jiayangshi/LoDoInd">code</a> .</p> <h2>Acknowledgment</h2> <p>This research was co-financed by the European Union H2020-MSCA-ITN-2020 under grant agreement no. 956172 (xCTing).&nbsp;</p>