Anatomical pulmonary magnetic resonance imaging segmentation for regional structure‐function measurements of asthma

Guo, Fumin; Svenningsen, Sarah; Eddy, Rachel L.; Capaldi, Dante P. I.; Sheikh, Khadija; Fenster, Aaron; Párraga, Grace

doi:10.1118/1.4948999

Cited by 18 publications

(19 citation statements)

References 68 publications

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“…A B 1 map was first applied to all UTE MR images to correct for flip angle inhomogeneities, which were then subsequently segmented using a multiregional segmentation approach, previously described . Briefly, a single observer (F.G.) placed seeds on the left lung, right lung, and the background.…”

Section: Methodsmentioning

confidence: 99%

Ultrashort echo time MRI biomarkers of asthma

Sheikh

Guo

Capaldi

et al. 2016

Magnetic Resonance Imaging

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Section: Methodsmentioning

confidence: 99%

Ultrashort echo time MRI biomarkers of asthma

Sheikh

Guo

Capaldi

et al. 2016

Magnetic Resonance Imaging

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“…With use of the coregistered tidal inspiration and expiration volumes, specific ventilation maps were generated as shown in Figure 1. The reference phase used to register the interpolated dataset was segmented by using a multiregional segmentation approach, as previously described (28). A primal dual optimization technique was implemented to solve the convex segmentation optimization problem (28), resulting in the segmented lung.…”

Section: Image Analysismentioning

confidence: 99%

Free-breathing Pulmonary MR Imaging to Quantify Regional Ventilation

Capaldi¹,

Eddy²,

Svenningsen³

et al. 2018

Radiology

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Purpose To measure regional specific ventilation with free-breathing hydrogen 1 (H) magnetic resonance (MR) imaging without exogenous contrast material and to investigate correlations with hyperpolarized helium 3 (He) MR imaging and pulmonary function test measurements in healthy volunteers and patients with asthma. Materials and Methods Subjects underwent free-breathing H and static breath-hold hyperpolarizedHe MR imaging as well as spirometry and plethysmography; participants were consecutively recruited between January and June 2017. Free-breathing H MR imaging was performed with an optimized balanced steady-state free-precession sequence; images were retrospectively grouped into tidal inspiration or tidal expiration volumes with exponentially weighted phase interpolation. MR imaging volumes were coregistered by using optical flow deformable registration to generateH MR imaging-derived specific ventilation maps. Hyperpolarized He MR imaging- andH MR imaging-derived specific ventilation maps were coregistered to quantify regional specific ventilation within hyperpolarized He MR imaging ventilation masks. Differences between groups were determined with the Mann-Whitney test and relationships were determined with Spearman (ρ) correlation coefficients. Statistical analyses were performed with software. Results Thirty subjects (median age: 50 years; interquartile range [IQR]: 30 years), including 23 with asthma and seven healthy volunteers, were evaluated. BothH MR imaging-derived specific ventilation and hyperpolarized He MR imaging-derived ventilation percentage were significantly greater in healthy volunteers than in patients with asthma (specific ventilation: 0.14 [IQR: 0.05] vs 0.08 [IQR: 0.06], respectively, P< .0001; ventilation percentage: 99% [IQR: 1%] vs 94% [IQR: 5%], P < .0001). For all subjects, H MR imaging-derived specific ventilation correlated with plethysmography-derived specific ventilation (ρ = 0.54, P = .002) and hyperpolarizedHe MR imaging-derived ventilation percentage (ρ = 0.67, P < .0001) as well as with forced expiratory volume in 1 second (FEV) (ρ = 0.65, P = .0001), ratio of FEV to forced vital capacity (ρ = 0.75, P < .0001), ratio of residual volume to total lung capacity (ρ = -0.68, P< .0001), and airway resistance (ρ = -0.51, P = .004). H MR imaging-derived specific ventilation was significantly greater in the gravitational-dependent versus nondependent lung in healthy subjects (P = .02) but not in patients with asthma (P = .1). In patients with asthma, coregisteredH MR imaging specific ventilation and hyperpolarized He MR imaging maps showed that specific ventilation was diminished in correspondingHe MR imaging ventilation defects (0.05 ± 0.04) compared with well-ventilated regions (0.09 ± 0.05) (P < .0001). Conclusion H MR imaging-derived specific ventilation correlated with plethysmography-derived specific ventilation and ventilation defects seen by using hyperpolarizedHe MR imaging. RSNA, 2018 Online supplemental material is available for this article.

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“…Two observers (F.G. and D.C.) initialized segmentation by seeding the left lung, right lung, and background on 2–3 1 H MRI slices 5 times on 5 days separated by at least 24 h each. These user seeds were used to generate the region‐specific appearance models that provide the segmentation data terms and guide the segmentation by fixing the labeling of challenging regions. The segmented lung series were co‐registered to an end‐inhalation slice, and all the pairwise registrations were implemented in parallel.…”

Section: Methodsmentioning

confidence: 99%

“…Given a series of 2D free‐breathing pulmonary 1 H MR images

I false(x false) = \{I_{i} (x) false| i = 1 \dots N\}

x \in Ω

, we aimed to segment each image

I_{i} false(x false)

into mutually disjoint left lung

R_{italicll}

, right lung

R_{italicrl}

, and background

R_{b}

, i.e.,

Ω = \cup_{l} R_{l}

R_{l} \cap R_{k} = \emptyset

, for

\forall l, k \in L = {l l, r l, b}

l \neq k

. The Potts method provides a way for multi‐region image segmentation by achieving a minimum labeling cost and 2D perimeter as follows:

\underset{ξ_{l} (x) \in false{0, 1 false}}{falsemin} \underset{l \in L}{false\sum} 〈ξ_{l}, ρ_{l}〉 + \underset{normalΩ}{false\int} g (x) |\nabla ξ_{l}| d x,

…”

Section: Theorymentioning

confidence: 99%

“…2D coronal slices were acquired using a fast balanced steady-state pulse sequence (FIESTA; GEHC, Milwaukee, WI): TR/TE/flip angle = 1.9 ms/0.6 ms/15°, FOV = 40 × 40 cm 2 , matrix = 256 × 256, bandwidth = 250 KHz, NEX = 1, number of phases = 500, slice thickness = 15 mm, and respiratory bellows. These user seeds were used to generate the region-specific appearance models 25 that provide the segmentation data terms and guide the segmentation by fixing the labeling of challenging regions. The segmented lung series were co-registered to an end-inhalation slice, and all the pairwise registrations were…”

Section: Image Acquisitionmentioning

confidence: 99%

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A framework for Fourier‐decomposition free‐breathing pulmonary ¹H MRI ventilation measurements

Guo

Capaldi

McCormack

et al. 2018

Magnetic Resonance in Med

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Purpose To develop a rapid Fourier decomposition (FD) free‐breathing pulmonary 1H 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 1H 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 3He‐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 3He‐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.

show abstract

Anatomical pulmonary magnetic resonance imaging segmentation for regional structure‐function measurements of asthma

Abstract: This lung segmentation approach provided the necessary and sufficient precision and accuracy required for research and clinical studies.

Cited by 18 publications

References 68 publications

Ultrashort echo time MRI biomarkers of asthma

Ultrashort echo time MRI biomarkers of asthma

Free-breathing Pulmonary MR Imaging to Quantify Regional Ventilation

A framework for Fourier‐decomposition free‐breathing pulmonary ¹H MRI ventilation measurements

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Anatomical pulmonary magnetic resonance imaging segmentation for regional structure‐function measurements of asthma

Abstract: This lung segmentation approach provided the necessary and sufficient precision and accuracy required for research and clinical studies.

Cited by 18 publications

References 68 publications

Ultrashort echo time MRI biomarkers of asthma

Ultrashort echo time MRI biomarkers of asthma

Free-breathing Pulmonary MR Imaging to Quantify Regional Ventilation

A framework for Fourier‐decomposition free‐breathing pulmonary 1H MRI ventilation measurements

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Product

Resources

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A framework for Fourier‐decomposition free‐breathing pulmonary ¹H MRI ventilation measurements