Evidence map›Paper›PMID 34719059›Full record

ArticleMagnetic resonance in medicine2022

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

Junghun Cho, Jinwei Zhang, Pascal Spincemaille, Hang Zhang, Simon Hubertus, Yan Wen, Ramin Jafari, Shun Zhang, Thanh D Nguyen, Alexey V Dimov and 2 more

Open access · greenAbstract read
In one paragraph

Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

16 citing papers in PubMed, 29 citations in OpenAlex.

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  8. Sensitivity assessment of QSM+qBOLD (or QQ) in detecting elevated oxygen extraction fraction (OEF) in physiological change.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2025
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  16. Brain oxygen extraction and neural tissue susceptibility are associated with cognitive impairment in older individuals.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022
    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

12 authors at 1 institution in 1 country.

Junghun ChoDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.ORCID 0000-0002-0826-5463
Jinwei ZhangDepartment of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Pascal SpincemailleDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Hang ZhangDepartment of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Simon HubertusDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Yan WenDepartment of Biomedical Engineering, Cornell University, Ithaca, New York, USA.ORCID 0000-0003-3355-9655
Ramin JafariDepartment of Biomedical Engineering, Cornell University, Ithaca, New York, USA.ORCID 0000-0002-1853-6677
Shun ZhangDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Thanh D NguyenDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.ORCID 0000-0002-1411-7694
Alexey V DimovDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.ORCID 0000-0003-0460-6635
Ajay GuptaDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.
Yi WangDepartment of Radiology, Weill Cornell Medical College, New York, New York, USA.ORCID 0000-0003-1404-8526
Cornell University · US

Funding

Quantitative Mapping of Substantia Nigra Iron in Parkinson's DiseaseR01NS095562 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI SHTILBANS, ALEXANDER, WANG, YI · 2016 to 2022
$3.1M
No-Gd MRI for Monitoring Disease Status in Multiple SclerosisR01NS105144 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI GAUTHIER, SUSAN A, GUPTA, AJAY · 2018 to 2022
$2.8M
Multiple Sclerosis Lesion Magnetic Susceptibility ActivityR01NS090464 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI WANG, YI · 2015 to 2020
$2.3M
3.0T Magnetic Resonance Imaging System for Preclinical, Translational and Clinical ResearchS10OD021782 · OD · WEILL MEDICAL COLL OF CORNELL UNIV · PI WANG, YI · 2016 to 2016
$2.0M
Quantitative Susceptibility Mapping (QSM) as a Non-Invasive Imaging Biomarker for Predicting Neurodegeneration in Alzheimer's DiseaseR21AG067466 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI KOVANLIKAYA, ILHAMI, WANG, YI · 2020 to 2020
$466k
Development and validation of MRI mapping of brain oxygen metabolism for clinical useK99NS123229 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHO, JUNGHUN · 2021 to 2022
$183k
NIA NIH HHS R21 AG067466NIH HHS S10 OD021782NINDS NIH HHS K99 NS123229NINDS NIH HHS R01 NS090464NINDS NIH HHS R01 NS095562NINDS NIH HHS R01 NS105144
6 · The paper itself

Abstract

purposeTo improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) -based oxygen extraction fraction (OEF) mapping using a deep neural network (QQ-NET).

methodsThe 3D multi-echo gradient echo images were acquired in 34 ischemic stroke patients and 4 healthy subjects. Arterial spin labeling and diffusion weighted imaging (DWI) were also performed in the patients. NET was developed to solve the QQ model inversion problem based on Unet. QQ-based OEF maps were reconstructed with previously introduced temporal clustering, tissue composition, and total variation (CCTV) and NET. The results were compared in simulation, ischemic stroke patients, and healthy subjects using a two-sample Kolmogorov-Smirnov test.

resultsIn the simulation, QQ-NET provided more accurate and precise OEF maps than QQ-CCTV with 150 times faster reconstruction speed. In the subacute stroke patients, OEF from QQ-NET had greater contrast-to-noise ratio (CNR) between DWI-defined lesions and their unaffected contralateral normal tissue than with QQ-CCTV: 1.9 ± 1.3 vs 6.6 ± 10.7 (p = 0.03). In healthy subjects, both QQ-CCTV and QQ-NET provided uniform OEF maps.

conclusionQQ-NET improves the accuracy of QQ-based OEF with faster reconstruction.

Indexed as

Deep LearningOxygenBrainBrain MappingCerebrovascular CirculationGray MatterHumansMagnetic Resonance ImagingOxygen ConsumptionOxygen SaturationOxygencerebral metabolic rate of oxygendeep learningDLoxygen extraction fractionqBOLDQQQQ-NETQSMQSM+qBOLDquantitative blood oxygenation level-dependent imagingquantitative susceptibility mappingUnet

Identifiers

PMID34719059
PMCPMC9133659
OpenAlexW3210009877

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.