Inert fluorinated gas MRI: a new pulmonary imaging modality

Couch, Marcus J.; Ball, Iain; Li, Tao; Fox, Matthew S.; Ouriadov, Alexei; Biman, Birubi; Albert, Mitchell S.

doi:10.1002/nbm.3165

Cited by 49 publications

(46 citation statements)

References 65 publications

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“…We can now use the developed structured reporting method and comparative analysis to assess performance of these methods. In addition, MRI can be advantageous for lung function assessment when incorporating hyperpolarized and/or inert fluorinated gas MRI, which will be future areas of development in this model (Fain et al , 2007, Couch et al , 2014). …”

Section: Discussionmentioning

confidence: 99%

Computed Tomography and Magnetic Resonance Imaging for Longitudinal Characterization of Lung Structure Changes in a Yucatan Miniature Pig Silicosis Model

et al. 2016

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Medical imaging is a rapidly advancing field enabling the repeated, non-invasive assessment of physiological structure and function. These beneficial characteristics can supplement studies in swine by mirroring the clinical functions of detection, diagnosis, and monitoring in humans. In addition, swine may serve as a human surrogate, facilitating the development and comparison of new imaging protocols for translation to humans. This study presents methods for pulmonary imaging developed for monitoring pulmonary disease initiation and progression in a pig exposure model with CT and MRI. In particular, a focus was placed on systematic processes, including positioning, image acquisition, and structured reporting to monitor longitudinal change. The image-based monitoring procedure was applied to six Yucatan miniature pigs. A subset of animals (n = 3) were injected with crystalline silica into the apical bronchial tree to induce silicosis. The methodology provided longitudinal monitoring and evidence of progressive lung disease while simultaneously allowing for a cross-modality comparative study highlighting the practical application of medical image data collection in swine. The integration of multi-modality imaging with structured reporting allows for cross-comparison of modalities, refinement of CT and MRI protocols, and consistently monitors potential areas of interest for guided biopsy and/or necropsy.

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Section: Discussionmentioning

confidence: 99%

Computed Tomography and Magnetic Resonance Imaging for Longitudinal Characterization of Lung Structure Changes in a Yucatan Miniature Pig Silicosis Model

et al. 2016

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“…MAGNETIC RESONANCE IMAGING (MRI) of the chest can provide regional assessment of lung ventilation by inhaling oxygen (O 2 ), hyperpolarized noble gases, or fluorinated gas . Oxygen‐enhanced (OE) MRI using a 3D radial ultrashort echo time (UTE) sequence supports quantitative differentiation of diseased vs. healthy lungs using whole lung ventilation defect percent (VDP) .…”

mentioning

confidence: 99%

Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI

Zha

Fain

Schiebler

et al. 2019

Magnetic Resonance Imaging

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Background Ultrashort echo time (UTE) proton MRI has gained popularity for assessing lung structure and function in pulmonary imaging; however, the development of rapid biomarker extraction and regional quantification has lagged behind due to labor‐intensive lung segmentation. Purpose To evaluate a deep learning (DL) approach for automated lung segmentation to extract image‐based biomarkers from functional lung imaging using 3D radial UTE oxygen‐enhanced (OE) MRI. Study Type Retrospective study aimed to evaluate a technical development. Population Forty‐five human subjects, including 16 healthy volunteers, 5 asthma, and 24 patients with cystic fibrosis. Field Strength/Sequence 1.5T MRI, 3D radial UTE (TE = 0.08 msec) sequence. Assessment Two 3D radial UTE volumes were acquired sequentially under normoxic (21% O2) and hyperoxic (100% O2) conditions. Automated segmentation of the lungs using 2D convolutional encoder‐decoder based DL method, and the subsequent functional quantification via adaptive K‐means were compared with the results obtained from the reference method, supervised region growing. Statistical Tests Relative to the reference method, the performance of DL on volumetric quantification was assessed using Dice coefficient with 95% confidence interval (CI) for accuracy, two‐sided Wilcoxon signed‐rank test for computation time, and Bland–Altman analysis on the functional measure derived from the OE images. Results The DL method produced strong agreement with supervised region growing for the right (Dice: 0.97; 95% CI = [0.96, 0.97]; P < 0.001) and left lungs (Dice: 0.96; 95% CI = [0.96, 0.97]; P < 0.001). The DL method averaged 46 seconds to generate the automatic segmentations in contrast to 1.93 hours using the reference method (P < 0.001). Bland–Altman analysis showed nonsignificant intermethod differences of volumetric (P ≥ 0.12) and functional measurements (P ≥ 0.34) in the left and right lungs. Data Conclusion DL provides rapid, automated, and robust lung segmentation for quantification of regional lung function using UTE proton MRI. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1169–1181.

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“…An alternative to inhaled hyperpolarized gas contrast is inhaled fluorinated gas for both dynamic and static breathhold MRI measurements. While the relatively low SNR of 19 F gas MRI 132 has somewhat limited widespread development and use, a number of pioneering 19 F lung MRI clinical and preclinical dynamic studies, [133][134][135] as well as diffusionweighted studies, have demonstrated its utility and potential. 133 In future, hardware and pulse sequence advances will continue to increase 19 F MRI SNR, which will accelerate 19 F MRI morphological and morphometry measurements.…”

Section: Emerging Imaging Techniquesmentioning

confidence: 99%

Advanced pulmonary MRI to quantify alveolar and acinar duct abnormalities: Current status and future clinical applications

Westcott

McCormack

Párraga

et al. 2019

Magnetic Resonance Imaging

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There are serious clinical gaps in our understanding of chronic lung disease that require novel, sensitive, and noninvasive in vivo measurements of the lung parenchyma to measure disease pathogenesis and progressive changes over time as well as response to treatment. Until recently, our knowledge and appreciation of the tissue changes that accompany lung disease has depended on ex vivo biopsy and concomitant histological and stereological measurements. These measurements have revealed the underlying pathologies that drive lung disease and have provided important observations about airway occlusion, obliteration of the terminal bronchioles and airspace enlargement, or fibrosis and their roles in disease initiation and progression. ex vivo tissue stereology and histology are the established gold standards and, more recently, micro‐computed tomography (CT) measurements of ex vivo tissue samples has also been employed to reveal new mechanistic findings about the progression of obstructive lung disease in patients. While these approaches have provided important understandings using ex vivo analysis of excised samples, recently developed hyperpolarized noble gas MRI methods provide an opportunity to noninvasively measure acinar duct and terminal airway dimensions and geometry in vivo, and, without radiation burden. Therefore, in this review we summarize emerging pulmonary MRI morphometry methods that provide noninvasive in vivo measurements of the lung in patients with bronchopulmonary dysplasia and chronic obstructive pulmonary disease, among others. We discuss new findings, future research directions, as well as clinical opportunities to address current gaps in patient care and for testing of new therapies. Level of Evidence: 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:28–40.

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Inert fluorinated gas MRI: a new pulmonary imaging modality

Cited by 49 publications

References 65 publications

Computed Tomography and Magnetic Resonance Imaging for Longitudinal Characterization of Lung Structure Changes in a Yucatan Miniature Pig Silicosis Model

Computed Tomography and Magnetic Resonance Imaging for Longitudinal Characterization of Lung Structure Changes in a Yucatan Miniature Pig Silicosis Model

Deep convolutional neural networks with multiplane consensus labeling for lung function quantification using UTE proton MRI

Advanced pulmonary MRI to quantify alveolar and acinar duct abnormalities: Current status and future clinical applications

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