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Using Hyperpolarized129 Xe MRI to Quantify the Pulmonary Ventilation Distribution

Posted Nov 30, 2016 Updated Apr 10, 2023
By Mu He MS , Bastiaan Driehuys PhD , Loretta G. Que MD , Yuh-Chin T. Huang MD MHS
13 min read

Rationale and Objectives

Ventilation heterogeneity is impossible to detect with spirometry. Alternatively, pulmonary ventilation can be imaged three-dimensionally using inhaled 129 Xe magnetic resonance imaging (MRI). To date, such images have been quantified primarily based on ventilation defects. Here, we introduce a robust means to transform 129 Xe MRI scans such that the underlying ventilation distribution and its heterogeneity can be quantified.

Materials and Methods

Quantitative 129 Xe ventilation MRI was conducted in 12 younger (24.7 ± 5.2 years) and 10 older (62.2 ± 7.2 years) healthy individuals, as well as in 9 younger (25.9 ± 6.4 yrs) and 10 older (63.2 ± 6.1 years) asthmatics. The younger healthy population was used to establish a reference ventilation distribution and thresholds for six intensity bins. These bins were used to display and quantify the ventilation defect region (VDR), the low ventilation region (LVR), and the high ventilation region (HVR).

Results

The ventilation distribution in young subjects was roughly Gaussian with a mean and standard deviation of 0.52 ± 0.18, resulting in VDR = 2.1 ± 1.3%, LVR = 15.6 ± 5.4%, and HVR = 17.4 ± 3.1%. Older healthy volunteers exhibited a significantly right-skewed distribution (0.46 ± 0.20, P = 0.034), resulting in significantly increased VDR (7.0 ± 4.8%, P = 0.008) and LVR (24.5 ± 11.5%, P = 0.025). In the asthmatics, VDR and LVR increased in the older population, and HVR was significantly reduced (13.5 ± 4.6% vs 18.9 ± 4.5%, P = 0.009). Quantitative 129 Xe MRI also revealed altered ventilation heterogeneity in response to albuterol in two asthmatics with normal spirometry.

Conclusions

Quantitative 129 Xe MRI provides a robust and objective means to display and quantify the pulmonary ventilation distribution, even in subjects who have airway function impairment not appreciated by spirometry.

Introduction

The distribution of ventilation is known to be nonuniform in healthy lungs , and this heterogeneity increases with age and disease. Ventilation heterogeneity is impossible to quantify using spirometry because it measures the lung as a single unit and is insensitive to pathology in the small airways—the so-called silent zone. Alternative approaches include using the multiple-breath nitrogen washout (MBNW) test to determine the distribution of specific ventilation (SV) , the lung clearance index (LCI) , or the multiple inert gas elimination technique (MIGET) to quantify the ventilation–perfusion relationship ; however, none of these tests provides spatial information. Alternatively, imaging methods such as computed tomography (CT) delineate spatial changes in lung structures that may allow ventilation abnormalities to be inferred. However, CT does not directly measure ventilation and its radiation dose limits some longitudinal studies.

Recently, magnetic resonance imaging (MRI) techniques have emerged that enable direct detection of inhaled gases, such as oxygen , perfluorinated gases , and hyperpolarized (HP) 3 He . These techniques enable visualization of ventilation defects that have been shown to correlate with airway tone and airway abnormalities . HP 3 He MRI readily depicts regional ventilation heterogeneity in patients with pulmonary obstructive diseases . More recently, 129 Xe gas has emerged as the most promising alternative to address dwindling supplies of 3 He . 129 Xe MRI appears to more readily detect ventilation defects than 3 He MRI and has been used to visualize elimination of ventilation defects after bronchodilator administration .

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Methods

Subjects

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Image Acquisition

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Image Analysis

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Figure 1, (a) 129 Xe MRI as acquired. (b) After correction for B1 inhomogeneity. (c) 1 H thoracic cavity image registered to 129 Xe MRI. (d) Detection of vascular structures within the 1 H image. (e) Segmentation and 1-pixel erosion to create a thoracic cavity mask with vascular structures removed, (f) histogram of 129 Xe intensities within the mask, and (g) 129 Xe histogram after rescaling by the top percentile of all intensities. (h) Binning map generated after applying thresholds. MRI, magnetic resonance imaging.

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Statistical Methods

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Results

Study Population

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Distribution of Ventilation in Young Healthy Subjects

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Figure 2, (a) Rescaled ventilation distribution for the 10 young normal individuals. (b) Average distribution for the 10 subjects has a mean of 0.52 and a standard deviation of 0.18. This distribution was used to define the thresholds and widths of six bins used to quantify and map the ventilation distribution.

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Effects of Age

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Figure 3, Examples of 129 Xe-derived ventilation distributions in a healthy younger and older subject. (a) In a 27-year-old healthy subject with an FEV1 of 86%, the 129 Xe ventilation image shows very few ventilation defects. The associated histogram is similar to that of normal young controls, with a mean of 0.54 and an SD of 0.18. (b) In a 58-year-old healthy subject with an FEV1 of 102%, the ventilation image shows areas of ventilation defects (red) and low ventilation (orange). The associated ventilation histogram shifted toward lower values compared to that of normal young controls, with a mean of 0.40 and an SD of 0.20. SD, standard deviation. (Color version of figure is available online.)

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Figure 4, Comparison of ventilation distribution parameters between the YC and OC groups. (a) Ventilation distribution histogram from the OC group (red) is overlaid with that from the YC group (blue). The ventilation distribution in the older healthy subjects shifted toward lower values compared to the younger group. (b) Comparison of ventilation distribution parameters between the young and old groups shows significant differences for all parameters except HVR (* indicates P ≤ 0.05). CV, coefficient of variation; HVR, high ventilation region; LVR, low ventilation region; OC, older control; SD, standard deviation; VDR, ventilation defect region; YC, younger control. (Color version of figure is available online.)

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Ventilation Distribution in Older and Younger Asthmatics

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Figure 5, Representative ventilation distributions in a younger asthmatic and an older asthmatic. (a) The younger asthmatic with an FEV1 of 76% exhibits a ventilation image with very few ventilation defects. The associated histogram is similar to that of normal young controls. (b) The older asthmatic with an FEV1 of 53% has a ventilation image with significant areas of ventilation defects (red) and low ventilation (orange). The associated ventilation histogram of the older asthmatic was significantly right skewed compared to that of the normal young controls. (Color version of figure is available online.)

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Figure 6, Comparison of ventilation distributions between the YA and OA groups. (a) Ventilation distribution histogram from the OA group (red) is overlaid with that from the YA group (blue). In the OA group, the distribution is shifted toward lower values compared to the younger asthmatic group. (b) Comparison of ventilation distribution parameters between the YA and the OA groups shows significant differences in all parameters (* indicates P ≤ 0.05). CV, coefficient of variation; HVR, high ventilation region; LVR, low ventilation region; OA, older asthmatic; SD, standard deviation; VDR, ventilation defect region; YA, younger asthmatic. (Color version of figure is available online.)

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Differences between FEV1 and 129 Xe MRI Ventilation Distribution

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Figure 7, Comparisons between FEV1 and 129 Xe MRI-derived ventilation distribution in patients with asthma. (a) A patient with normal FEV1 but abnormal 129 Xe MRI. ( b ) A patient with abnormal FEV1 but normal 129 Xe MRI. (c and d) Two patients with moderate asthma with similarly low FEV1 but markedly different ventilation patterns.

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Ventilation Distribution Before and After Albuterol Treatment

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Figure 8, Ventilation distribution in a patient with mild intermittent asthma who had positive bronchodilator response to albuterol based on FEV1 criteria. (a) The binning map ( top ) and histogram ( bottom ) before albuterol. (b) The binning map ( top ) and histogram ( bottom ) after albuterol showed a decreased ventilation defect region and low ventilation region, and a distribution matching that of young normal controls after bronchodilator use.

Figure 9, Ventilation distribution in a patient with mild intermittent asthma who did not respond to albuterol based on FEV1 criteria. (a) The binning map ( top ) and histogram ( bottom ) before albuterol treatment. (b) The binning map ( top ) and histogram ( bottom ) after albuterol treatment. The ventilation distribution histogram after albuterol treatment showed decreased VDR but increased LVR at the expense of normally ventilated regions. This may be the result of more normal airways dilating and redistributing ventilation.

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Discussion

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Academic Radiology, Volume 23, Issue 12
Journals General Radiology
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