DOI: 10.5061/dryad.vmcvdnczf
title: A normative database of free-breathing pediatric thoracic 4D dynamic MRI images

description: 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 dMRI dataset of healthy children to date. The database is unique
 and provides dMRI images, object segmentations, and quantitative regional
 respiratory measurement parameters of volumes for healthy children. The
 database can serve as a reference standard to quantify regional
 respiratory abnormalities in young patients with various respiratory
 conditions and facilitate treatment planning and response assessment. The
 database can be useful to advance future AI-based research on image-based
 object segmentation and analysis.
description: 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
 mm<sup>3</sup>, 320×320 matrix, bandwidth 258 Hz, and flip
 angle 76<sup>o</sup>. 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 image over one breathing
 cycle (consisting of typically 5-8 respiratory phases) from each acquired
 dMRI scan to represent the whole dynamic thoraco-abdominal body region.
 The algorithm selects roughly 175-280 slices (35 sagittal locations × 5-8
 respiratory phases) from the 1400 acquired slices in an optimal manner
 using an optical flux method. (2) Image processing:
 Intensity standardization [2] is performed on every time point/3D volume
 of the 4D image. (3) Object segmentation: For each
 subject, there are 10 objects segmented at both EE and EI time points in
 this database. They include the thoracoabdominal skin outer boundary, left
 and right lungs, liver, spleen, left and right kidneys, diaphragm, and
 left and right hemi-diaphragms. All of the healthy children in this study
 have larger field of view (LFOV) images, which include the full thorax and
 abdomen in sagittal dMRI images. We used a pretrained U-Net based deep
 learning network to first segment all objects, and then all
 auto-segmentation results were visually checked and manually refined as
 needed, under supervision of a radiologist (DAT) with over 25 years of
 expertise in MRI and thoracoabdominal radiology. Manual segmentations have
 been performed for all objects in all data sets. (4)
 Volumetric measurements based on object segmentations for lung (left and
 right separately) volumes at end expiration/end inspiration, as well as
 for chest wall and diaphragm excursion volumes (left and right separately)
 are reported. [1] Hao Y, Udupa JK, Tong Y, Wu C, Li H,
 McDonough JM, Lott C, Qiu C, Galagedera N, Anari JB, Torigian DA, Cahill
 PJ. OFx: A method of 4D image construction from free-breathing non-gated
 MRI slice acquisitions of the thorax via optical flux. Med Image Anal.
 2021;72:102088. doi: 10.1016/j.media.2021.102088. PubMed PMID: 34052519;
 PMCID: PMC8316349. [2] Nyul LG, Udupa JK. On
 standardizing the MR image intensity scale. Magn Reson Med.
 1999;42(6):1072-81. doi:
 10.1002/(sici)1522-2594(199912)42:6&lt;1072::aid-mrm11&gt;3.0.co;2-m. PubMed PMID: 10571928.
description: # 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 masks are in DICOM file format, the international standard to transmit, store, retrieve, print, process, and display medical imaging information. An additional spreadsheet is used to show the volume information. The data are organized in different directories (dicom_dMRI.zip), which means that for each subject, we have one folder called EE and EI. Within EE or EI, there will be a sub-folder called as “images” including the intensity images, and another sub-folder called as “label” including 10 labels for 10 objects. 10 objects with their names in detail are as below, Lvr - liver RDphm - right hemi-diaphragm RKd - right kidney RLg - right lung Spl - spleen TA-Skn - thoracic-abdominal skin DPhm - (bilateral) diaphragm LDphm-lefthemi-diaphragm LKd - left kidney LLg - left lung VGC-Dynamic MRI Subject 1:                         EE (end expiration):                                     Images: intensity image                                     Labels : object contour (binary mask file)                         EI (end inspiration):                                     Images: intensity image                                     Labels : object contour (binary mask file) Subject 2:                         … ## Sharing/Access information Data was derived from the following source: [http://www.mipg.upenn.edu/Vnews/mipg_data_share/Thorax_dMRI.html](http://www.mipg.upenn.edu/Vnews/mipg_data_share/Thorax_dMRI.html) ## Code/Software DICOM is common format for medical images which can be easily used in some open source software packages including for example ImageJ [https://imagej.net/ij/](https://imagej.net/ij/), 3D Slicer [https://www.slicer.org/](https://www.slicer.org/), and ITK-SNAP [http://www.itksnap.org/docs/fullmanual.php](http://www.itksnap.org/docs/fullmanual.php) ## Version changes **09-May-2024:** The first version of data sharing with 141 subjects and their segmentations (2,820 3D masks). The volume file is in Excel. **17-June-2025:** the second version of data sharing including 59 new subjects and their segmentations (4,000 3D masks); and all the volumetric results for all 200 subjects including the left, right lung volume at end inspiration and end expiration, left and right chest wall tidal volumes and left and right diaphragm tidal volumes. All volumes have the unit in cc. The volume file is in CSV. ## Human subjects data 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:", "0010,0030": "REMOVE:", "0010,0040": "REMOVE:", "0010,1010": "REMOVE:", "0010,1020": "REMOVE:", "0010,1030": "REMOVE:", "0010,1040": "REMOVE:", "0010,1060": "REMOVE:", "0010,2150": "REMOVE:", "0010,2160": "REMOVE:", "0010,21F0": "REMOVE:", "0010,2154": "REMOVE:", "0008,1048": "REMOVE:", "0008,0080": "REMOVE:", "0008,0081": "REMOVE:", "0008,0090": "REMOVE:", "0008,0096": "REMOVE:", "0008,1010": "REMOVE:", "0008,1050": "REMOVE:", "0032,1031": "REMOVE:", "0032,1032": "REMOVE:", "0091,1020": "REMOVE:", "6000,0010": "REMOVE:", "6000,0011": "REMOVE:", "6000,0040": "REMOVE:", "6000,0050": "REMOVE:", "6000,0100": "REMOVE:", "6000,0102": "REMOVE:", "6000,1500": "REMOVE:", "6000,3000": "REMOVE:"