Hyperpolarized gas magnetic resonance imaging texture analysis and machine learning to explain accelerated lung function decline in ex-smokers with and without COPD

2021 ◽  
Author(s):  
Maksym Sharma ◽  
Andrew R. Westcott ◽  
David G. McCormack ◽  
Grace Parraga
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Lung Function ◽  
Texture Analysis ◽  
Resonance Imaging ◽  
Lung Function Decline ◽  
Hyperpolarized Gas

Relationship between structural changes and hyperpolarized gas magnetic resonance imaging in chronic obstructive pulmonary disease using computational simulations with realistic alveolar geometry

2009 ◽  
Vol 367 (1896) ◽  
pp. 2347-2369 ◽  
Author(s):  
Michal Plotkowiak ◽  
Kelly Burrowes ◽  
Jan Wolber ◽  
Christopher Buckley ◽  
Robert Davies ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Lung Function ◽  
Structural Changes ◽  
Image Acquisition ◽  
Chronic Obstructive ◽  
Resonance Imaging ◽  
Apparent Diffusion Coefficients ◽  
Hyperpolarized Gas ◽  
Hyperpolarized Gas Mri

Both the development of accurate models of lung function and their quantitative validation can be significantly enhanced by the use of functional imaging techniques. The advent of hyperpolarized noble gas magnetic resonance imaging (MRI) technology has increased the amount of local, functional information we can obtain from the lung. In particular, application of 3 He to measure apparent diffusion coefficients has enabled some measure of lung microstructure and airspace size within the lung. Models mimicking image acquisition in hyperpolarized gas MRI can improve understanding of the relationship between image findings and lung structure, and can be used to improve the definition of imaging protocols. In this paper, we review the state of the art in hyperpolarized gas MRI modelling. We also present our own results, obtained using a Monte Carlo approach and a realistic alveolar sac geometry, which has previously been applied in functional lung studies. In this way, we demonstrate the potential for models combining lung function and image acquisition, which could provide valuable tools in both basic studies and clinical practice.

scholarly journals Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning

2017 ◽  
Vol 56 (6) ◽  
pp. 806-812 ◽  
Author(s):  
Turid Torheim ◽  
Eirik Malinen ◽  
Knut Håkon Hole ◽  
Kjersti Vassmo Lund ◽  
Ulf G. Indahl ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Resonance Imaging ◽  
Cervical Cancers ◽  
Multiparametric Magnetic Resonance Imaging
Sign in / Sign up

Export Citation Format

Share Document