Semiautomated Ventilation Defect Quantification in Exercise-induced Bronchoconstriction Using Hyperpolarized Helium-3 Magnetic Resonance Imaging

Rationale and Objectives

This study aimed to compare the performance of a semiautomated ventilation defect segmentation approach, adaptive K -means, with manual segmentation of hyperpolarized helium-3 magnetic resonance imaging in subjects with exercise-induced bronchoconstriction (EIB).

Materials and Methods

Six subjects with EIB underwent hyperpolarized helium-3 magnetic resonance imaging and spirometry tests at baseline, post exercise, and recovery over two separate visits. Ventilation defects were analyzed by two methods. First, two independent readers manually segmented ventilation defects. Second, defects were quantified by an adaptive K -means method that corrected for coil sensitivity, applied a vesselness filter to estimate pulmonary vasculature, and segmented defects adaptively based on the overall low-intensity signals in the lungs. These two methods were then compared in four aspects: (1) ventilation defect percent (VDP) measurements, (2) correlation between spirometric measures and measured VDP, (3) regional VDP variations pre- and post exercise challenge, and (4) Dice coefficient for spatial agreement.

Results

The adaptive K -means method was ~5 times faster, and the measured VDP bias was under 2%. The correlation between predicted forced expiratory volume in 1 second over forced vital capacity and VDP measured by adaptive K -means ( ρ = −0.64, P < 0.0001) and by the manual method ( ρ = −0.63, P < 0.0001) yielded almost identical 95% confidence intervals. Neither method of measuring VDP indicated apical/basal or anterior dependence in this small study cohort.

Conclusions

Compared to the manual method, the adaptive K -means method provided faster, reproducible, comparable measures of VDP in EIB and may be applied to a variety of lung diseases.

Introduction

Over the past decade, hyperpolarized helium-3 magnetic resonance imaging (HP 3 He MRI) has been used extensively in research for evaluating ventilation and defect in asthma , cystic fibrosis (CF) , and chronic obstructive pulmonary disease (COPD) . Quantitative assessment of ventilation and its regional distribution is critical to the application and advance of HP gas MRI in clinical research. The ventilation defect quantification from HP gas MRI has been advanced from subjective defect scores to semiquantitative measures and quantitative defect volume measures . The commonly used quantitative metric is ventilation defect percent (VDP) . Thedefect quantification from gas images contains two main steps: (1) lung cavity segmentation and (2) defect measurements within the lung cavity. Although the manual defect segmentation demonstrated good interreader agreement , it is tedious, time-consuming, and subjective. For automated lung cavity segmentation, Ray et al. proposed merging active contours using the properties of fluid flow to segment the lung in each slice to obtain the total lung cavity volume. Tustison et al. proposed a shape model–based lung segmentation, which requires offline preprocessing to obtain the unbiased shape template. Guo et al. proposed to co-segment the lung cavity jointly from three-dimensional (3D) proton and 3 He MRI pairs. For quantitative measures of defect from 3 He MRI within the lung cavity, Tustison et al. used Atropos to partition the lungs into ventilated and unventilated regions with optimal parameter settings. The hierarchical K -means method showed good spatial agreement relative to manual segmentation on subjects with COPD and CF and low spatial agreement on asthmatic subjects. Moreover, the histogram-based linear binning method proposed for defect quantification on 129 Xe images has not been assessed for 3 He images. Therefore, there is still a demand for a reliable, automated defect quantification method that is generally applicable for various lung conditions.

Three major confounding factors have made the defect quantification a difficult problem, especially in less-defected lungs. First, because of the inherent low-intensity presence on 3 He image, pulmonary vasculature regions are likely to be misclassified as ventilation defects in computer-aided detection. Second, the intensity inhomogeneity due to the nonuniform coil sensitivities can jeopardize the accuracy of intensity-based segmentation algorithms. Third, highly defected lungs tend to have smaller signal-intensity variations, whereas less-defected lungs tend to have more subtle signal heterogeneity requiring refined subclassification. In particular, asthmatic lungs are known to have spatially heterogeneous patterns of ventilation . This makes quantitative defect measurement, especially in mild asthma, a more challenging problem than in CF or COPD, where the ventilation defect is often persistent and focal. Therefore, a robust and reproducible defect quantification method for evaluating longitudinal progression and treatment response is of particular importance in asthma.

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Materials and Methods

Human Subjects

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Spirometry

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3 He Polarization and Image Acquisition

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Overview of the Defect Segmentation Pipeline

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Landmark-based Image Registration

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Vasculature Estimation

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T(x)=⎧⎩⎨⎪⎪⌈μ¯¯s+(x−μi)s1−μ¯sp1−μi⌉,⌈μ¯¯s+(x−μi)s2−μ¯sp2−μi⌉,ifp1≤x≤μiifμi≤x≤p2 T

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where ⌈⌉ ⌈

⌉ denotes the ceiling operation that rounds up to the nearest integer, p 1 and p 2 denote the minimum and maximum intensity values in the lungs, and s 1 and s 2 were chosen as 1 and 512 respectively. This transformation highlights those relatively low-intensity pixels below the µ i value. Consequently, the vascular structures on the contrast-enhanced proton images ( Fig 2b and f ) were more distinct relative to the raw proton images ( Fig 2a and e ).

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V(RA,RB)=exp(−RA/(2α2))⋅(1−exp(−RB/(2β2))), V

(

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exp

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2 . Parameters α and β were selected as 0.5 and 15 respectively. A threshold <0.15 was applied on V ( R A , R B ) to obtain the binary vasculature mask. The estimated vasculature masks ( Fig 2c and g ) were aligned with low-intensity vascular pixels in the corresponding 3 He ( Fig 2d and h ).

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Coil Sensitivity Correction

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Adaptive K -Means Clustering

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PL=Voxel count in the first binSum of all lung voxels×100%. P

Voxel count in the first bin

Sum of all lung voxels

100

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Defect cluster=⎧⎩⎨⎪⎪A.First two subclusters withK=5,B.First three subclusters withK=4,C.First cluster withK=4,PL<4%4%≤PL<10%PL≥10% Defect cluster

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First two subclusters with

First three subclusters with

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First cluster with

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Morphometric Correction of Partial Volume Effect

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Manual Segmentation

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Statistical Analysis

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D=2⋅|S∩M||S|+|M|. D

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∩

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Results

Manual Versus Semiautomated Measurements

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TABLE 1

Percent Change in Ventilation Defect Percent (ΔVDP) From Baseline to Post Challenge at Two Separate Visits Measured by Manual and Adaptive K -means Methods

Subject Manual Adaptive K -means ΔVDP Visit1 (%) ΔVDP Visit2 (%) ΔVDP Visit1 (%) ΔVDP Visit2 (%) 1 14.27 19.98 19.52 19.60 2 7.66 4.15 4.47 3.33 3 0.23 0.80 0.40 −0.61 4 8.57 15.34 3.58 13.20 5 0 1.02 −2.32 −2.49 6 14.69 14.45 16.85 22.06

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TABLE 2

Average Ventilation Defect Percent for Exercise Challenge Protocol in Subjects with EIB at Each of Two Visits

Visit Manual Adaptive K- means Baseline Post Challenge Recovery Baseline Post Challenge Recovery Visit1 1.11 ± 1.12% 8.68 ± 7.41% 1.94 ± 1.47% 2.78 ± 1.63% 9.86 ± 8.21% 2.94 ± 2.67% Visit2 0.70 ± 0.81% 9.99 ± 8.97% 1.82 ± 1.36% 1.84 ± 1.50% 11.02 ± 9.77% 2.48 ± 2.90%

EIB, exercise-induced bronchoconstriction.

Values are mean ± standard deviation. Ventilation defect percent (%) was calculated as 100% × defect volumes/total lung volumes.

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Correlation to Spirometric Measures

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TABLE 3

Spearman Correlation Between Whole Lung VDP and Spirometric Measures

FEV1/FVC %P FEV1%P_ρ__P_ value 95% CI_ρ__P_ value 95% CI Manual −0.63 <0.0001 [−0.80, −0.38] −0.54 0.00071 [−0.75, −0.25] Adaptive K -means −0.64 <0.0001 [−0.83, −0.38] −0.40 0.016 [−0.66, −0.049]

CI, confidence interval; FEV1 %P, percent predicted forced expiratory volume in 1 second; FEV1/FVC %P, percent predicted FEV1 over forced vital capacity; VDP, ventilation defect percent.

A P < 0.05 was considered significantly correlated.

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Regional Variations in VDP

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Dice Coefficient

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TABLE 4

Dice Coefficients (Mean ± Standard Deviation) for Manual and Semiautomated Segmentation

Methods Ventilation Volume Defect Volume Reader 2 – Reader 1 0.99 ± 0.015 0.56 ± 0.31 Adaptive K -means – Reader 1 0.98 ± 0.027 0.28 ± 0.24 Adaptive K -means – Reader 2 0.97 ± 0.033 0.25 ± 0.21 Hierarchical K -means – Reader 1 0.95 ± 0.013 0.16 ± 0.18 Adaptive K -means 2 – Adaptive K -means 1 1.00 ± 0.00033 0.99 ± 0.022

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Discussion

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Acknowledgments

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References

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Semiautomated Ventilation Defect Quantification in Exercise-induced Bronchoconstriction Using Hyperpolarized Helium-3 Magnetic Resonance Imaging

Rationale and Objectives

Materials and Methods

Results

Conclusions

Introduction

Materials and Methods

Human Subjects

Spirometry

3 He Polarization and Image Acquisition

Overview of the Defect Segmentation Pipeline

Landmark-based Image Registration

Vasculature Estimation

Coil Sensitivity Correction

Adaptive K -Means Clustering

Morphometric Correction of Partial Volume Effect

Manual Segmentation

Statistical Analysis

Results

Manual Versus Semiautomated Measurements

Correlation to Spirometric Measures

Regional Variations in VDP

Dice Coefficient

Discussion

Acknowledgments

References

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