Evidence map›Paper›PMID 36039169›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering

QdMRI: A system for comprehensive analysis of thoracic dynamics via dynamic MRI.

Yubing Tong, Jayaram K Udupa, You Hao, Lipeng Xie, Joseph M McDonough, Caiyun Wu, Carina Lott, Abigail Clark, Jason B Anari, Drew A Torigian and 1 more

Open access · greenAbstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 41% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Yubing TongMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Jayaram K UdupaMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
You HaoMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Lipeng XieMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Joseph M McDonoughThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States.
Caiyun WuMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Carina LottThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States.
Abigail ClarkThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States.
Jason B AnariThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States.
Drew A TorigianMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Patrick J CahillThe Wyss/Campbell Center for Thoracic Insufficiency Syndrome, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States.
California University of Pennsylvania · USChildren's Hospital of Philadelphia · US

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 HL150147
6 · The paper itself

Abstract

Quantitative thoracic dynamic magnetic resonance imaging (QdMRI), a recently developed technique, provides a potential solution for evaluating treatment effects in thoracic insufficiency syndrome (TIS). In this paper, we integrate all related algorithms and modules during our work from the past 10 years on TIS into one system, named QdMRI, to address the following questions: (1) How to effectively acquire dynamic images? For many TIS patients, subjects are unable to cooperate with breathing instructions during image acquisition. Image acquisition can only be implemented under free-breathing conditions, and it is not feasible to use a surrogate device for tracing breathing signals. (2) How to assess the thoracic structures from the acquired image, such as lungs, left and right, separately? (3) How to depict the dynamics of thoracic structures due to respiration motion? (4) How to use the structural and functional information for the quantitative evaluation of surgical TIS treatment and for the design of the surgery plan? The QdMRI system includes 4 major modules: dynamic MRI (dMRI) acquisition, 4D image construction, image segmentation (from 4D image), and visualization of segmentation results, dynamic measurements, and comparisons of measurements from TIS patients with those from normal children. Scanning/image acquisition time for one subject is ~20 minutes, 4D image construction time is ~5 minutes, image segmentation of lungs via deep learning is 70 seconds for all time points (with the average DICE 0.96 in healthy children), and measurement computation time is 2 seconds.

Indexed as

4D constructiondeep learningdynamic MRIlung parenchymathoracic insufficiency syndrome (TIS)

Identifiers

PMID36039169
PMCPMC9420222
OpenAlexW4220906707

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.