A normative database of free-breathing pediatric thoracic 4D dynamic MRI images

In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. The shared open-source normative database is from our ongoing virtual growing child (VGC) project, which includes 4D dynamic magnetic resonance imaging (dMRI) images during one breathing cycle for each normal child and also 10 object segmentations at end expiration (EE) and end inspiration (EI) phases of the respiratory cycle in the 4D image. The lung volumes at EE and EI as well as the excursion volumes of chest wall and diaphragm from EE to EI, left and right separately, are also reported. The database has 4,000 3D segmentations from 200 healthy children, which to our knowledge is the largest d..., The normative database is from our ongoing NIH funded virtual growing child (VGC) project. All dMRI scans are acquired from healthy children during free-breathing. The dMRI protocol was as follows: 3T MRI scanner (Verio, Siemens, Erlangen, Germany), true-FISP bright-blood sequence, TR=3.82 ms, TE=1.91 ms, voxel size ~1×1×6 mm3, 320×320 matrix, bandwidth 258 Hz, and flip angle 76o. With recent advances, for each sagittal location across the thorax and abdomen, we acquire 40 2D slices over several tidal breathing cycles at ~480 ms/slice. On average, 35 sagittal locations are imaged, yielding a total of ~1400 2D MRI slices, with a resulting total scan time of 11-13 minutes for any particular subject.
The collected dMRI goes through the procedure of 4D image construction, image processing, object segmentation, and then volumetric measurements from segmentations.
(1) 4D image construction: For the acquired dMRI scans, we utilized an automated 4D image construction approach [1] to form one 4D..., , # A normative database of free-breathing pediatric thoracic 4D dynamic MRI images

[https://doi.org/10.5061/dryad.vmcvdnczf](https://doi.org/10.5061/dryad.vmcvdnczf)

## Description of the data and file structure

In total, dynamic MRI (dMRI) images from 200 healthy children were acquired and then constructed with a 4D image per subject, and 3D volumes at end expiration (EE) and end inspiration (EI) time points were segmented with each having 10 object segmentation, leading to a total of 4,000 (200×2×10) 3D segmented object samples. Each object sample has a 3D segmentation mask covering an average of 25 slices, for a total of 100,000 2D slices with object segmentation in the database.

Besides the dMRI and 3D segmentation masks, we also provide the volumetric measurements for the lung (left, right, separately) volumes at EE and EI, and also the chest wall and diaphragm (left, right, separately) tidal volumes in one CSV (\"Dryad_dMRI_volumetric2.csv\").

All the images and segmentation mas..., All data were in dicom after de-identification by removing personally identifiable information (PII).
We used Ambra (now called InteleRad) for our deidentification as well as distribution of research studies. We created a list of DICOM tags containing PHI, and those tags are automatically removed on upload to Ambra. Here’s that list of tags as examples,

\"0010,0020\": \"REMOVE:\",
\"0010,1000\": \"REMOVE:\",
\"0010,1001\": \"REMOVE:\",
\"0010,1002\": \"REMOVE:\",
\"0010,1005\": \"REMOVE:\",
\"0010,0021\": \"REMOVE:\",
\"0010,0010\": \"REMOVE:\",
...