Evidence map›Paper›PMID 42563379›Full record

ArticleMedical physics2026

Estimating pediatric lung volumetric parameters via rapid, limited-slice, free-breathing thoracic dynamic MRI.

You Hao, Yubing Tong, Caiyun Wu, Joseph M McDonough, Samantha Gogel, David M Biko, Patrick J Cahill, Jason B Anari, Drew A Torigian, Jayaram K Udupa

Abstract read
In one paragraph

Article in Medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

You HaoDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Yubing TongDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Caiyun WuDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Joseph M McDonoughCenter for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Samantha GogelCenter for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
David M BikoDepartment of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Patrick J CahillCenter for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Jason B AnariCenter for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Drew A TorigianDepartment of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Jayaram K UdupaDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Virtual growing child 5-dimensional functional models for treating respiratory anomaliesR01HL150147 · NHLBI · UNIVERSITY OF PENNSYLVANIA · PI TORIGIAN, DREW, UDUPA, JAYARAM K · 2020 to 2023
$3.1M
NHLBI NIH HHS R01 HL150147NHLBI NIH HHS R01HL150147
6 · The paper itself

Abstract

backgroundDynamic, separate lung volume assessment during breathing is critical for evaluating thoracic disorders like scoliosis, thoracic insufficiency syndrome (TIS), and pulmonary diseases, as well as for monitoring treatments. While traditional volumetric assessment relies on static 3D imaging during breath-holds, this is unfeasible for many pediatric patients or those with severe respiratory distress. Free-breathing 4D dynamic MRI (dMRI) is the ideal modality due to its lack of radiation, excellent soft-tissue contrast, and flexible imaging planes. However, its clinical use is hampered by long acquisition times, during which patients struggle to maintain stable, consistent breathing patterns or remain still. Consequently, methods to accelerate dMRI acquisition while maintaining volumetric accuracy are urgently needed to make free-breathing assessments clinically practical. PURPOSE: We present an observational study involving free-breathing short-scan-time dynamic MRI (dMRI) method that can be routinely used for computing dynamic lung volumes accurately.

methods(1) Full sampled free-breathing sagittal 2D dMRI scans are gathered from 45 normal children via bSSFP sequence. Sparse dMRI (s-dMRI) scans are simulated from these datasets by optimally subsampling in the spatio-temporal domains via a limited number of selected sagittal locations and time instances. (2) A 4D image is constructed from both scans. Lungs are segmented from 4D image, and their volumes from full and sparse dMRI scans are computed. (3) A regression model is developed to predict full-scan volumes from sparse-scan data on a training set. (4) The accuracy is analyzed on both synthesized sparse dMRI scans from a separate fully-sampled-scan test set and actual s-dMRI scans prospectively acquired from 10 normal children.

resultsWith 5 slices per lung and 40 time points, the predicted volume showed a ∼2% deviation from the full-scan volume, with a total scan-time of ∼9 min (vs. 49.72 ± 5.01 min for the full scan with 15-22 slices per lung and 80 time points). When the spatial sampling was increased to full number of slices (15-22 per lung) but only 40 time points, these metrics become 0.4%, and 24.86 ± 2.5 min.

conclusionss-dMRI is a practical approach for computing dynamic lung volumes that can be used routinely with no radiation concern, especially on patients who cannot tolerate long acquisition times.

Indexed as

Imaging, Three-DimensionalLungLung Volume MeasurementsMagnetic Resonance ImagingRespirationThoraxChildFemaleHumansMaleTime Factorsdynamic magnetic resonance imaging (dMRI)lung volumepediatric lung imagingspatio‐temporal samplingthoracic insufficiency syndrome (TIS)

Identifiers

PMID42563379
PMCPMC13447894

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.