A framework for Fourier‐decomposition free‐breathing pulmonary 1H MRI ventilation measurements. Issue 3 (26th October 2018)

• Record Type: Journal Article
• Authors: Guo, Fumin; Capaldi, Dante P.I.; McCormack, David G.; Fenster, Aaron; Parraga, Grace
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 3(2019)
• Page Start: 2135
• Page End: 2146
• Publication Date: 2018-10-26
• Subjects: asthma -- Fourier decomposition -- free‐breathing 1H MRI -- quantification -- segmentation and registration; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27527
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : 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.