Removal of off‐resonance xenon gas artifacts in pulmonary gas‐transfer MRI.

Purpose: Hyperpolarized xenon (129Xe) gas‐transfer imaging allows different components of pulmonary gas transfer—alveolar air space, lung interstitium/blood plasma (barrier), and red blood cells (RBCs)—to be assessed separately in a single breath. However, quantitative analysis is challenging because dissolved‐phase 129Xe images are often contaminated by off‐resonant gas‐phase signal generated via imperfectly selective excitation. Although previous methods required additional data for gas‐phase removal, the method reported here requires no/minimal sequence modifications/data acquisitions, allowing many previously acquired images to be corrected retroactively. Methods: 129Xe imaging was implemented at 3.0T via an interleaved three‐dimensional radial acquisition of the gaseous and dissolved phases (using one‐point Dixon reconstruction for the dissolved phase) in 46 human subjects and a phantom. Gas‐phase contamination (9.5% ± 4.8%) was removed from gas‐transfer data using a modified gas‐phase image. The signal‐to‐noise ratio (SNR) and signal distributions were compared before and after contamination removal. Additionally, theoretical gaseous contaminations were simulated at different magnetic field strengths for comparison. Results: Gas‐phase contamination at 3.0T was more diffuse and located predominantly outside the lungs, relative to simulated 1.5T contamination caused by the larger frequency offset. Phantom experiments illustrated a 91% removal efficiency. In human subjects, contamination removal produced significant changes in dissolved signal SNR (+7.8%), mean (–1.4%), and standard deviation (–2.3%) despite low contamination. Repeat measurements showed reduced variance (dissolved mean, –1.0%; standard deviation, –8.4%). Conclusion: Off‐resonance gas‐phase contamination can be removed robustly with no/minimal sequence modifications. Contamination removal permits more accurate quantification, reduces radiofrequency stringency requirements, and increases data consistency, providing improved sensitivity needed for multicenter trials. [ABSTRACT FROM AUTHOR]