Talissa A. Altes

Applications of hyperpolarized helium-3 gas magnetic resonance imaging in pediatric lung disease.

Hyperpolarized gas magnetic resonance imaging (MRI) of the lung provides high temporal and spatial resolution images of the air spaces of the lung and can be used to elucidate both lung ventilation and morphology. Because no ionizing radiation is involved, hyperpolarized gas MRI is ideal for the evaluation of pediatric lung diseases. In the article, we describe briefly the basic principles of hyperpolarized gas MRI, review the literature of hyperpolarized gas MRI in two pediatric lung diseases (asthma and cystic fibrosis), and discuss possible future clinical applications of hyperpolarized gas imaging in pediatric lung disease.

Clinical correlates of lung ventilation defects in asthmatic children

BACKGROUND Lung ventilation defects identified by using hyperpolarized 3-helium gas ((3)He) lung magnetic resonance imaging (MRI) are prevalent in asthmatic patients, but the clinical importance of ventilation defects is poorly understood. OBJECTIVES We sought to correlate the lung defect volume quantified by using (3)He MRI with clinical features in children with mild and severe asthma. METHODS Thirty-one children with asthma (median age, 10 years; age range, 3-17 years) underwent detailed characterization and (3)He lung MRI. Quantification of the (3)He signal defined ventilation defect and hypoventilated, ventilated, and well-ventilated volumes. RESULTS The ventilation defect to total lung volume fraction ranged from 0.1% to 11.6%. Children with ventilation defect percentages in the upper tercile were more likely to have severe asthma than children in the lower terciles (P = .005). The ventilation defect percentage correlated (P < .05 for all) positively with the inhaled corticosteroid dose, total number of controller medications, and total blood eosinophil counts and negatively with the Asthma Control Test score, FEV1 (percent predicted), FEV1/forced vital capacity ratio (percent predicted), and forced expiratory flow rate from 25% to 75% of expired volume (percent predicted). CONCLUSION The lung defect volume percentage measured by using (3)He MRI correlates with several clinical features of asthma, including severity, symptom score, medication requirement, airway physiology, and atopic markers.

Use of hyperpolarized helium-3 MRI to assess response to ivacaftor treatment in patients with cystic fibrosis

BACKGROUND This pilot study evaluated the effect of short- and long-term ivacaftor treatment on hyperpolarized 3He-magnetic resonance imaging (MRI)-defined ventilation defects in patients with cystic fibrosis aged ≥12years with a G551D-CFTR mutation. METHODS Part A (single-blind) comprised 4weeks of ivacaftor treatment; Part B (open-label) comprised 48weeks of treatment. The primary outcome was change from baseline in total ventilation defect (TVD; total defect volume:total lung volume ratio). RESULTS Mean change in TVD ranged from -8.2% (p=0.0547) to -12.8% (p=0.0078) in Part A (n=8) and -6.3% (p=0.1953) to -9.0% (p=0.0547) in Part B (n=8) as assessed by human reader and computer algorithm, respectively. CONCLUSIONS TVD responded to ivacaftor therapy. 3He-MRI provides an individual quantification of disease burden that may be able to detect aspects of the disease missed by population-based spirometry metrics. Assessments by human reader and computer algorithm exhibit similar trends, but the latter appears more sensitive. www.clinicaltrials.gov identifier: NCT01161537.

Direct comparison of 129Xe diffusion measurements with quantitative histology in human lungs

Chronic obstructive pulmonary disease (COPD) is an irreversible lung disease characterized by small‐airway obstruction and alveolar‐airspace destruction. Hyperpolarized 129Xe diffusion MRI of lung is a promising biomarker for assessing airspace enlargement, but has yet to be validated by direct comparison to lung histology. Here we have compared diffusion measurements of hyperpolarized (HP) 129Xe in explanted lungs to regionally matched morphological measures of airspace size.

Hyperpolarized Gas Magnetic Resonance Lung Imaging in Children and Young Adults.

The assessment of early pulmonary disease and its severity can be difficult in young children, as procedures such as spirometry cannot be performed on them. Computed tomography provides detailed structural images of the pulmonary parenchyma, but its major drawback is that the patient is exposed to ionizing radiation. In this context, magnetic resonance imaging (MRI) is a promising technique for the evaluation of pediatric lung disease, especially when serial imaging is needed. Traditionally, MRI played a small role in evaluating the pulmonary parenchyma. Because of its low proton density, the lungs display low signal intensity on conventional proton-based MRI. Hyperpolarized (HP) gases are inhaled contrast agents with an excellent safety profile and provide high signal within the lung, allowing for high temporal and spatial resolution imaging of the lung airspaces. Besides morphologic information, HP MR images also offer valuable information about pulmonary physiology. HP gas MRI has already made new contributions to the understanding of pediatric lung diseases and may become a clinically useful tool. In this article, we discuss the HP gas MRI technique, special considerations that need to be made when imaging children, and the role of MRI in 2 of the most common chronic pediatric lung diseases, asthma and cystic fibrosis. We also will discuss how HP gas MRI may be used to evaluate normal lung growth and development and the alterations occurring in chronic lung disease of prematurity and in patients with a congenital diaphragmatic hernia.

WS3.2 The effect of ivacaftor treatment on lung ventilation defects, as measured by hyperpolarized helium-3 MRI, on patients with cystic fibrosis and a G551D-CFTR mutation

WS3.1 The effect of ivacaftor on the rate of lung function decline in CF patients with a G551D-CFTR mutation G.S. Sawicki1, E. McKone2, D.J. Pasta3, J. Wagener4, C. Johnson4, M.W. Konstan5. 1Boston Children’s Hospital, Boston, United States; 2St. Vincent’s University Hospital, Dublin, Ireland; 3ICON Late Phase and Outcomes Research, San Francisco, United States; 4Vertex Pharmaceuticals Incorporated, Boston, United States; 5Case Western Reserve University School of Medicine Rainbow Babies and Children’s Hospital, Cleveland, United States

Skeletal development of the hand and wrist: digital bone age companion—a suitable alternative to the Greulich and Pyle atlas for bone age assessment?

PurposeTo assess reader performance and subjective workflow experience when reporting bone age studies with a digital bone age reference as compared to the Greulich and Pyle atlas (G&P). We hypothesized that pediatric radiologists would achieve equivalent results with each method while digital workflow would improve speed, experience, and reporting quality.Materials and methodsIRB approval was obtained for this HIPAA-compliant study. Two pediatric radiologists performed research interpretations of bone age studies randomized to either the digital (Digital Bone Age Companion, Oxford University Press) or G&P method, generating reports to mimic clinical workflow. Bone age standard selection, interpretation-reporting time, and user preferences were recorded. Reports were reviewed for typographical or speech recognition errors. Comparisons of agreement were conducted by way of Fisher’s exact tests. Interpretation-reporting times were analyzed on the natural logarithmic scale via a linear mixed model and transformed to the geometric mean. Subjective workflow experience was compared with an exact binomial test. Report errors were compared via a paired random permutation test.ResultsThere was no difference in bone age determination between atlases (p = 0.495). The interpretation-reporting time (p < 0.001) was significantly faster with the digital method. The faculty indicated preference for the digital atlas (p < 0.001). Signed reports had fewer errors with the digital atlas (p < 0.001).ConclusionsBone age study interpretations performed with the digital method were similar to those performed with the Greulich and Pyle atlas. The digital atlas saved time, improved workflow experience, and reduced reporting errors relative to the Greulich and Pyle atlas when integrated into electronic workflow.

An initial investigation of hyperpolarized gas tagging magnetic resonance imaging in evaluating deformable image registration-based lung ventilation

BACKGROUND Deformable image registration (DIR)-based lung ventilation mapping is attractive due to its simplicity, and also challenging due to its susceptibility to errors and uncertainties. In this study, we explored the use of 3D Hyperpolarized (HP) gas tagging MRI to evaluate DIR-based lung ventilation. METHOD AND MATERIAL Three healthy volunteers included in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton MRI (p-MRI) using balanced steady-state free precession pulse sequence at end of inhalation and end of exhalation. We first obtained the reference displacement vector fields (DVFs) from the t-MRIs by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR-based DVFs from the p-MRIs by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVFs and the DIR-based DVFs using the Jacobian method and then compared using cross correlation and mutual information. RESULTS The DIR-based lung ventilations calculated using p-MRI varied considerably from the reference lung ventilations based on t-MRI among all three subjects. The lung ventilations generated using Velocity AI were preferable for the better spatial homogeneity and accuracy compared to the ones using MIM, with higher average cross correlation (0.328 vs 0.262) and larger average mutual information (0.528 vs 0.323). CONCLUSION We demonstrated that different DIR algorithms resulted in different lung ventilation maps due to underlining differences in the DVFs. HP gas tagging MRI provides a unique platform for evaluating DIR-based lung ventilation.

Assessing Airflow Sensitivity to Healthy and Diseased Lung Conditions in a Computational Fluid Dynamics Model Validated In Vitro

Computational models are useful for understanding respiratory physiology. Crucial to such models are the boundary conditions specifying the flow conditions at truncated airway branches (terminal flow rates). However, most studies make assumptions about these values, which are difficult to obtain in vivo. We developed a computational fluid dynamics (CFD) model of airflows for steady expiration to investigate how terminal flows affect airflow patterns in respiratory airways. First, we measured in vitro airflow patterns in a physical airway model, using particle image velocimetry (PIV). The measured and computed airflow patterns agreed well, validating our CFD model. Next, we used the lobar flow fractions from a healthy or chronic obstructive pulmonary disease (COPD) subject as constraints to derive different terminal flow rates (i.e., three healthy and one COPD) and computed the corresponding airflow patterns in the same geometry. To assess airflow sensitivity to the boundary conditions, we used the correlation coefficient of the shape similarity (R) and the root-mean-square of the velocity magnitude difference (Drms) between two velocity contours. Airflow patterns in the central airways were similar across healthy conditions (minimum R, 0.80) despite variations in terminal flow rates but markedly different for COPD (minimum R, 0.26; maximum Drms, ten times that of healthy cases). In contrast, those in the upper airway were similar for all cases. Our findings quantify how variability in terminal and lobar flows contributes to airflow patterns in respiratory airways. They highlight the importance of using lobar flow fractions to examine physiologically relevant airflow characteristics.

Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification

RATIONALE AND OBJECTIVES We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational efficiency while demonstrating accuracy and robustness. The large data requirement for the proposed framework is made possible by a novel template-based data augmentation strategy. Supporting this work is the open-source ANTsRNet-a growing repository of well-known deep learning architectures first introduced here. MATERIALS AND METHODS Deep convolutional neural network (CNN) models were constructed and trained using a custom multilabel Dice metric loss function and a novel template-based data augmentation strategy. Training (including template generation and data augmentation) employed 205 proton MR images and 73 functional lung MRI. Evaluation was performed using data sets of size 63 and 40 images, respectively. RESULTS Accuracy for CNN-based proton lung MRI segmentation (in terms of Dice overlap) was left lung: 0.93 ± 0.03, right lung: 0.94 ± 0.02, and whole lung: 0.94 ± 0.02. Although slightly less accurate than our previously reported joint label fusion approach (left lung: 0.95 ± 0.02, right lung: 0.96 ± 0.01, and whole lung: 0.96 ± 0.01), processing time is <1 second per subject for the proposed approach versus ∼30 minutes per subject using joint label fusion. Accuracy for quantifying ventilation defects was determined based on a consensus labeling where average accuracy (Dice multilabel overlap of ventilation defect regions plus normal region) was 0.94 for the CNN method; 0.92 for our previously reported method; and 0.90, 0.92, and 0.94 for expert readers. CONCLUSION The proposed framework yields accurate automated quantification in near real time. CNNs drastically reduce processing time after offline model construction and demonstrate significant future potential for facilitating quantitative analysis of functional lung MRI.