An initial investigation of hyperpolarized gas tagging magnetic resonance imaging in evaluating deformable image registration-based lung ventilation.

Published

Journal Article

BACKGROUND: Deformable image registration (DIR)-based lung ventilation mapping is attractive due to its simplicity, and also challenging due to its susceptibility to errors and uncertainties. In this study, we explored the use of 3D Hyperpolarized (HP) gas tagging MRI to evaluate DIR-based lung ventilation. METHOD AND MATERIAL: Three healthy volunteers included in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton MRI (p-MRI) using balanced steady-state free precession pulse sequence at end of inhalation and end of exhalation. We first obtained the reference displacement vector fields (DVFs) from the t-MRIs by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR-based DVFs from the p-MRIs by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVFs and the DIR-based DVFs using the Jacobian method and then compared using cross correlation and mutual information. RESULTS: The DIR-based lung ventilations calculated using p-MRI varied considerably from the reference lung ventilations based on t-MRI among all three subjects. The lung ventilations generated using Velocity AI were preferable for the better spatial homogeneity and accuracy compared to the ones using MIM, with higher average cross correlation (0.328 vs 0.262) and larger average mutual information (0.528 vs 0.323). CONCLUSION: We demonstrated that different DIR algorithms resulted in different lung ventilation maps due to underlining differences in the DVFs. HP gas tagging MRI provides a unique platform for evaluating DIR-based lung ventilation.

Full Text

Duke Authors

Cited Authors

  • Cui, T; Miller, GW; Mugler, JP; Cates, GD; Mata, JF; de Lange, EE; Huang, Q; Altes, TA; Yin, F-F; Cai, J

Published Date

  • December 2018

Published In

Volume / Issue

  • 45 / 12

Start / End Page

  • 5535 - 5542

PubMed ID

  • 30276819

Pubmed Central ID

  • 30276819

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1002/mp.13223

Language

  • eng

Conference Location

  • United States