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
Author:
Affiliation:
1. Department of Radiology Weill Cornell Medical College New York New York USA
2. Department of Biomedical Engineering Cornell University Ithaca New York USA
Funder
National Institutes of Health
Publisher
Wiley
Subject
Radiology, Nuclear Medicine and imaging
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.29057
Reference81 articles.
1. Variability of cerebral blood volume and oxygen extraction: stages of cerebral haemodynamic impairment revisited
2. Cerebrovascular Reserve and Stroke Risk in Patients With Carotid Stenosis or Occlusion
3. Oxygen Extraction Fraction and Stroke Risk in Patients with Carotid Stenosis or Occlusion: A Systematic Review and Meta-Analysis
4. QUantitative Imaging of eXtraction of oxygen and TIssue consumption (QUIXOTIC) using venular-targeted velocity-selective spin labeling
5. Venous oxygenation mapping using velocity-selective excitation and arterial nulling
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