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A framework for Fourier‐decomposition free‐breathing pulmonary 1 H MRI ventilation measurements
Author(s) -
Guo Fumin,
Capaldi Dante P.I.,
McCormack David G.,
Fenster Aaron,
Parraga Grace
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27527
Subject(s) - reproducibility , segmentation , image registration , correlation coefficient , coefficient of variation , nuclear medicine , similarity (geometry) , fiducial marker , artificial intelligence , ventilation (architecture) , pearson product moment correlation coefficient , concordance correlation coefficient , mathematics , computer science , biomedical engineering , pattern recognition (psychology) , medicine , physics , statistics , image (mathematics) , thermodynamics
Purpose To develop a rapid Fourier decomposition (FD) free‐breathing pulmonary 1 H MRI (FDMRI) image processing and biomarker pipeline for research use. Methods We acquired MRI in 20 asthmatic subjects using a balanced steady‐state free precession (bSSFP) sequence optimized for ventilation imaging. 2D 1 H MRI series were segmented by enforcing the spatial similarity between adjacent images and the right‐to‐left lung volume–ratio. The segmented lung series were co‐registered using a coarse‐to‐fine deformable registration framework that used dual optimization techniques. All pairwise registrations were implemented in parallel and FD was performed to generate 2D ventilation‐weighted maps and ventilation‐defect‐percent (VDP). Lung segmentation and registration accuracy were evaluated by comparing algorithm and manual lung‐masks, deformed manual lung‐masks, and fiducials in the moving and fixed images using Dice‐similarity‐coefficient (DSC), mean‐absolute‐distance (MAD), and target‐registration‐error (TRE). The relationship of FD‐VDP and 3 He‐VDP was evaluated using the Pearson‐correlation‐coefficient ( r ) and Bland Altman analysis. Algorithm reproducibility was evaluated using the coefficient‐of‐variation (CoV) and intra‐class‐correlation‐coefficient (ICC) for segmentation, registration, and FD‐VDP components. Results For lung segmentation, there was a DSC of 95 ± 1.5% and MAD of 2.3 ± 0.5 mm, and for registration there was a DSC of 97 ± 0.8%, MAD of 1.6 ± 0.4 mm and TRE of 3.6 ± 1.2 mm. Reproducibility for segmentation DSC (CoV/ICC = 0.5%/0.92), registration TRE (CoV/ICC = 0.4%/0.98), and FD‐VDP (Cov/ICC = 3.9%/0.97) was high. The pipeline required 10 min/subject. FD‐VDP was correlated with 3 He‐VDP ( r = 0.69, P < 0.001) although there was a bias toward lower FD‐VDP (bias = −4.9%). Conclusions We developed and evaluated a pipeline that provides a rapid and precise method for FDMRI ventilation maps.