Spatial fuzzy c‐means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and <sup>1</sup>H MRI

Hughes, Paul; Horn, Felix; Collier, Guilhem J.; Biancardi, Alberto; Marshall, Helen; Wild, Jim M.

doi:10.1002/jmri.25804

Cited by 35 publications

(48 citation statements)

References 19 publications

(73 reference statements)

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“…While improving the reproducibility and the calculation of %VV through the benefits of obtaining 1 H and 129 Xe images inherently coregistered in the same breath hold, CS also has a negligible influence on the derived %VV values. The mean absolute difference of 1.3% in global %VV found between prospective FS and CS datasets is below the previously calculated mean interobserver error in %VV calculation (2.3%) when analyzing FS images with the same software . It is also within the same‐day reproducibility confidence interval of %VV measurement (±1.52%) previously reported in Ebner et al Our results are in line with Qing et al, who previously reported that CS was a good candidate to accelerate the acquisition of 3 He ventilation and 1 H images in the same breath without compromising image fidelity.…”

Section: Discussionsupporting

confidence: 90%

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Single breath‐held acquisition of coregistered 3D ¹²⁹Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Collier

Hughes

Horn

et al. 2019

Magnetic Resonance in Med

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Purpose:To develop and assess a method for acquiring coregistered proton anatomical and hyperpolarized 129 Xe ventilation MR images of the lungs with compressed sensing (CS) in a single breath hold. Methods: Retrospective CS simulations were performed on fully sampled ventilation images acquired from one healthy smoker to optimize reconstruction parameters. Prospective same-breath anatomical and ventilation images were also acquired in five ex-smokers with an acceleration factor of 3 for hyperpolarized 129 Xe images, and were compared to fully sampled images acquired during the same session. The following metrics were used to assess data fidelity: mean absolute error (MAE), root mean square error, and linear regression of the signal intensity between fully sampled and undersampled images. The effect of CS reconstruction on two quantitative imaging metrics routinely reported [percentage ventilated volume (%VV) and heterogeneity score] was also investigated. Results: Retrospective simulations showed good agreement between fully sampled and CS-reconstructed (acceleration factor of 3) images with MAE (root mean square error) of 3.9% (4.5%). The prospective same-breath images showed a good match in ventilation distribution with an average R 2 of 0.76 from signal intensity linear regression and a negligible systematic bias of +0.1% in %VV calculation. A bias of −1.8% in the heterogeneity score was obtained. Conclusion: With CS, high-quality 3D images of hyperpolarized 129 Xe ventilation (resolution 4.2 × 4.2 × 7.5 mm 3 ) can be acquired with coregistered 1 H anatomical MRI in a 15-s breath hold. The accelerated acquisition time dispenses with the need for registration between separate breath-hold 129 Xe and 1 H MRI, enabling more accurate %VV calculation. K E Y W O R D S compressed sensing, hyperpolarized 129 Xe gas, lung MRI | 343 COLLIER Et aL. How to cite this article: Collier GJ, Hughes PJC, Horn FC, et al. Single breath-held acquisition of coregistered 3D 129 Xe lung ventilation and anatomical proton images of the human lung with compressed sensing.

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Section: Discussionsupporting

confidence: 90%

“…H score is then defined as the mean value of the distribution of H i,j,k . Finally, %VV was also calculated for each AF and for the fully sampled DICOM images using a semiautomated segmentation software …”

Section: Methodsmentioning

confidence: 99%

Single breath‐held acquisition of coregistered 3D ¹²⁹Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Collier

Hughes

Horn

et al. 2019

Magnetic Resonance in Med

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“…Medical image segmentation software (Mimics; Materialise, Leuven, Belgium) was used to segment the lungs of the inspiratory and expiratory CT scans. 3 He and 1 H MR lung parenchyma were segmented in Matlab (MathWorks, Natick, MA) using a modified version of the Spatial Fuzzy Cmeans (FCM) algorithm presented by Chuang et al (2006) as recently described (Hughes et al, 2017). Prior to segmentation, images were pre-processed using a bilateral filter (Tomasi and Manduchi, 1998).…”

Section: Image Segmentationmentioning

confidence: 99%

Comparison of CT ventilation imaging and hyperpolarised gas MRI: effects of breathing manoeuvre

Tahir¹,

Marshall²,

Hughes³

et al. 2019

Phys. Med. Biol.

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Image registration of lung CT images acquired at different inflation levels has been proposed as a surrogate method to map lung 'ventilation'. Prior to clinical use, it is important to understand how this technique compares with direct ventilation imaging modalities such as hyperpolarised gas MRI. However, variations in lung inflation level have been shown to affect regional ventilation distributions. Therefore, the aim of this study was to evaluate the impact of lung inflation levels when comparing CT ventilation imaging to ventilation from 3 He-MRI.

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“…Ventilation defects are clusters of voxels that represent ventilatory slow space. They can be identified through at least three different methods: manually by an experienced observer, through hierarchical k-means clustering (18), or by spatial fuzzy c-means clustering (15).The lung elements in our study that are designated as constricted certainly include voxels that would be designated as ventilation defect, but they also include others that strongly respond to methacholine, as indicated by their Ͼ50% drop in FEV1 but not to the point that they would be considered "defects." For example, a lung element with a relatively high SV (say of 0.3 compared with a lungwide mean SV of 0.2) that fell to an SV of 0.14 (compared with a lungwide mean SV of 0.2) would be considered constricted but would probably not be considered to be part of a defect.…”

Section: Sv Heterogeneitymentioning

confidence: 99%

The spatial pattern of methacholine bronchoconstriction recurs when supine independently of posture during provocation but does not recur between postures

Geier

Kubo

Theilmann

et al. 2018

Journal of Applied Physiology

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The location of lung regions with compromised ventilation (often called ventilation defects) during a bronchoconstriction event may be influenced by posture. We aimed to determine the effect of prone vs. supine posture on the spatial pattern of methacholine-induced bronchoconstriction in six healthy adults (ages 21-41, three females) using specific ventilation imaging. Three postural conditions were chosen to assign the effect of posture to the drug administration and/or imaging phase of the experiment - supine methacholine administration followed by supine imaging, prone methacholine administration followed by supine imaging, and prone methacholine administration followed by prone imaging. The two conditions in which imaging was performed supine had similar spatial patterns of bronchoconstriction despite a change in posture during methacholine administration; the odds ratio for recurrent constriction was mean (SD) = 7.4 (3.9). Conversely, dissimilar spatial patterns of bronchoconstriction emerged when posture during imaging was changed; the odds ratio for recurrent constriction between the prone methacholine/supine imaging condition and the prone methacholine/prone imaging condition was 1.2 (0.9). Logistic regression showed that height above the dependent lung border was a significant negative predictor of constriction in the two supine imaging conditions (p<0.001 for each), but not in the prone imaging condition (p=0.20). These results show that the spatial pattern of methacholine bronchoconstriction is recurrent in the supine posture, regardless of whether methacholine is given prone or supine, but that prone posture during imaging eliminates that recurrent pattern and reduces its dependence on gravitational height.

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Spatial fuzzy c‐means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and ¹H MRI

Cited by 35 publications

References 19 publications

Single breath‐held acquisition of coregistered 3D ¹²⁹Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Single breath‐held acquisition of coregistered 3D ¹²⁹Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Comparison of CT ventilation imaging and hyperpolarised gas MRI: effects of breathing manoeuvre

The spatial pattern of methacholine bronchoconstriction recurs when supine independently of posture during provocation but does not recur between postures

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Spatial fuzzy c‐means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and 1H MRI

Cited by 35 publications

References 19 publications

Single breath‐held acquisition of coregistered 3D 129Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Single breath‐held acquisition of coregistered 3D 129Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Comparison of CT ventilation imaging and hyperpolarised gas MRI: effects of breathing manoeuvre

The spatial pattern of methacholine bronchoconstriction recurs when supine independently of posture during provocation but does not recur between postures

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Spatial fuzzy c‐means thresholding for semiautomated calculation of percentage lung ventilated volume from hyperpolarized gas and ¹H MRI

Single breath‐held acquisition of coregistered 3D ¹²⁹Xe lung ventilation and anatomical proton images of the human lung with compressed sensing

Single breath‐held acquisition of coregistered 3D ¹²⁹Xe lung ventilation and anatomical proton images of the human lung with compressed sensing