Dataset Information

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T.

ABSTRACT:

Purpose

Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI.

Methods

Dynamic OE-MRI was performed on healthy participants using a dual-echo multi-slice spoiled gradient echo sequence at 3 T and cyclical gas delivery. ICA was applied to each echo within a thoracic mask. The ICA component relating to the oxygen-enhancement signal was automatically identified using correlation analysis. The oxygen-enhancement component was reconstructed, and the percentage signal enhancement (PSE) was calculated. The lung PSE of current smokers was compared with nonsmokers; scan-rescan repeatability, ICA pipeline repeatability, and reproducibility between two vendors were assessed.

Results

ICA successfully extracted a consistent oxygen-enhancement component for all participants. Lung tissue and oxygenated blood displayed the opposite oxygen-induced signal enhancements. A significant difference in PSE was observed between the lungs of current smokers and nonsmokers. The scan-rescan repeatability and the ICA pipeline repeatability were good.

Conclusion

The developed pipeline demonstrated sensitivity to the signal enhancements of the lung tissue and oxygenated blood at 3 T. The difference in lung PSE between current smokers and nonsmokers indicates a likely sensitivity to lung function alterations that may be seen in mild pathology, supporting future use of our methods in patient studies.

SUBMITTER: Needleman SH

PROVIDER: S-EPMC10952250 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T.

Needleman Sarah H SH Kim Mina M McClelland Jamie R JR Naish Josephine H JH Tibiletti Marta M O'Connor James P B JPB Parker Geoff J M GJM

Magnetic resonance in medicine 20231120 3

<h4>Purpose</h4>Dynamic lung oxygen-enhanced MRI (OE-MRI) is challenging due to the presence of confounding signals and poor signal-to-noise ratio, particularly at 3 T. We have created a robust pipeline utilizing independent component analysis (ICA) to automatically extract the oxygen-induced signal change from confounding factors to improve the accuracy and sensitivity of lung OE-MRI.<h4>Methods</h4>Dynamic OE-MRI was performed on healthy participants using a dual-echo multi-slice spoiled gradi ...[more]

PMID: 37984456

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Dataset Information

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T.

Purpose

Methods

Results

Conclusion

Publications

Independent component analysis (ICA) applied to dynamic oxygen-enhanced MRI (OE-MRI) for robust functional lung imaging at 3 T.

Similar Datasets

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