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Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration

<p>This dataset contains the data used to arrive at the conclusions in the research article <em>Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration</em>, by Balfour et al [<em>BioMedical Engineering OnLine</em> 2015, <strong>14</strong>:85].</p> <p>This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction (&quot;STIR&quot;, see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33).</p> <p>Three types of data are provided, which should be sufficient for repeating the findings of the study:</p> <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins (&quot;gates&quot;) for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> <p>These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images.</p> <p>For more detailed information regarding the method, please refer to the article.</p> <p>The PET data is split into several sub-categories:</p> <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> <p>Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.</p> <p>&nbsp;</p>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0

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