Technical challenges of quantitative chest MRI data analysis in a large cohort pediatric study

Abstract

Objectives: This study was conducted in order to evaluate the effect of geometric distortion (GD) on MRI lung volume quantification and evaluate available manual, semi-automated, and fully automated methods for lung segmentation.

Methods: A phantom was scanned with MRI and CT. GD was quantified as the difference in phantom's volume between MRI and CT, with CT as gold standard. Dice scores were used to measure overlap in shapes. Furthermore, 11 subjects from a prospective population-based cohort study each underwent four chest MRI acquisitions. The resulting 44 MRI scans with 2D and 3D Gradwarp were used to test five segmentation methods. Intraclass correlation coefficient, Bland-Altman plots, Wilcoxon, Mann-Whitney U, and paired t tests were used for statistics.

Results: Using phantoms, volume differences between CT and MRI varied according to MRI positions and 2D and 3D Gradwarp correction. With the phantom located at the isocenter, MRI overestimated the volume relative to CT by 5.56 ± 1.16 to 6.99 ± 0.22% with body and torso coils, respectively. Higher Dice scores and smaller intraobject differences were found for 3D Gradwarp MR images. In subjects, semi-automated and fully automated segmentation tools showed high agreement with manual segmentations (ICC = 0.971-0.993 for end-inspiratory scans; ICC = 0.992-0.995 for end-expiratory scans). Manual segmentation time per scan was approximately 3-4 h and 2-3 min for fully automated methods.

Conclusions: Volume overestimation of MRI due to GD can be quantified. Semi-automated and fully automated segmentation methods allow accurate, reproducible, and fast lung volume quantification. Chest MRI can be a valid radiation-free imaging modality for lung segmentation and volume quantification in large cohort studies.

Key points: • Geometric distortion varies according to MRI setting and patient positioning. • Automated segmentation methods allow fast and accurate lung volume quantification. • MRI is a valid radiation-free alternative to CT for quantitative data analysis.

Keywords: Imaging; Lung; Lung volume measurements; Magnetic resonance imaging; Phantoms.

Fig. 1

Flowchart of acquisition scheme per subject. Each subject (n = 11) underwent two end-inspiratory and two end-expiratory acquisitions. 2D and 3D Gradwarp correction was applied to one end-inspiratory and one end-expiratory scan. In total, 11 subjects underwent four acquisitions, resulting in 44 scans

Fig. 2

MRI and CT acquisition scheme of body phantom. a Phantom acquisitions with MRI with six different scan settings. b Phantom acquisitions with CT scan as reference images

Fig. 3

Effect of GD on volume quantification with MRI compared to CT according to various MRI scan settings. Relative volume difference (%) for a electronic displacement of FOV, b manual displacement of phantom, c table repositioning, d parallel imaging, and e use of torso coil. Reference = reference MRI isocenter position. Positions distanced 5 cm from isocenter: L = left, R = right, I = inferior, S = superior, LI = left inferior, RI = right inferior, LS = left superior, RS = right superior

Fig. 3

Effect of GD on volume quantification with MRI compared to CT according to various MRI scan settings. Relative volume difference (%) for a electronic displacement of FOV, b manual displacement of phantom, c table repositioning, d parallel imaging, and e use of torso coil. Reference = reference MRI isocenter position. Positions distanced 5 cm from isocenter: L = left, R = right, I = inferior, S = superior, LI = left inferior, RI = right inferior, LS = left superior, RS = right superior

Fig. 4

Relative volume difference (%) for a electronic displacement of FOV, b manual displacement of phantom, c table repositioning, d parallel imaging, and e use of torso coil. The horizontal line through each box indicates the median, rectangular boxes represent the interquartile ranges, and whiskers represent minimum and maximum values. Blue = 2D Gradwarp, orange = 3D Gradwarp

Fig. 4

Relative volume difference (%) for a electronic displacement of FOV, b manual displacement of phantom, c table repositioning, d parallel imaging, and e use of torso coil. The horizontal line through each box indicates the median, rectangular boxes represent the interquartile ranges, and whiskers represent minimum and maximum values. Blue = 2D Gradwarp, orange = 3D Gradwarp

Fig. 5

Images illustrate the effect of 2D and 3D Gradwarp. a CT reference image, b MR image with 2D Gradwarp, c MR image with 3D Gradwarp. MR images were obtained with phantom distanced 5 cm to the right of the scanner isocenter. Bending of bottles on the right side of the phantom (blue and green bottles) were seen when the bottles moved further from the scanner isocenter. With 3D Gradwarp, all bottles appear straight

Fig. 6

Lung volume segmentations with tested segmentation methods. a Exemplary slice with corresponding segmentation results obtained with b MS, c 3D Slicer, d GeoS, e Pennati software, and f Ivanovska software