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An initial investigation of hyperpolarized gas tagging magnetic resonance imaging in evaluating deformable image registration‐based lung ventilation
Author(s) -
Cui Taoran,
Miller G. Wilson,
Mugler John P.,
Cates Gordon D.,
Mata Jaime F.,
Lange Eduard E.,
Huang Qijie,
Altes Talissa A.,
Yin FangFang,
Cai Jing
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13223
Subject(s) - exhalation , magnetic resonance imaging , nuclear medicine , lung ventilation , image registration , ventilation (architecture) , computer science , lung , biomedical engineering , physics , medicine , artificial intelligence , radiology , image (mathematics) , thermodynamics
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 ( DVF s) from the t‐ MRI s by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR ‐based DVF s from the p‐ MRI s by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVF s and the DIR ‐based DVF s 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 DVF s. HP gas tagging MRI provides a unique platform for evaluating DIR ‐based lung ventilation.