Lung Magnetic Resonance Imaging: Technical Advancements and Clinical Applications

ABSTRACT

Since lung magnetic resonance imaging (MRI) became clinically available, limited clinical utility has been suggested for applying MRI to lung diseases. Moreover, clinical applications of MRI for patients with lung diseases or thoracic oncology may vary from country to country due to clinical indications, type of health insurance, or number of MR units available. Because of this situation, members of the Fleischner Society and of the Japanese Society for Magnetic Resonance in Medicine have published new reports to provide appropriate clinical indications for lung MRI. This review article presents a brief history of lung MRI in terms of its technical aspects and major clinical indications, such as (1) what is currently available, (2) what is promising but requires further validation or evaluation, and (3) which developments warrant research-based evaluations in preclinical or patient studies. We hope this article will provide Investigative Radiology readers with further knowledge of the current status of lung MRI and will assist them with the application of appropriate protocols in routine clinical practice.

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort

PURPOSE

To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results.

MATERIALS AND METHODS

A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions.

RESULTS

The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively.

CONCLUSIONS

The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.

Comparison of hyperpolarized 3He-MRI, CT based parametric response mapping, and mucus scores in asthmatics

Purpose: The purpose of this study was to anatomically correlate ventilation defects with regions of air trapping by whole lung, lung lobe, and airway segment in the context of airway mucus plugging in asthma. Methods: A total of 34 asthmatics [13M:21F, 13 mild/moderate, median age (range) of 49.5 (36.8-53.3) years and 21 severe, 56.1 (47.1-62.6) years] and 4 healthy subjects [1M:3F, 38.5 (26.6-52.2) years] underwent HP 3He MRI and CT imaging. HP 3He MRI was assessed for ventilation defects using a semi-automated k-means clustering algorithm. Inspiratory and expiratory CTs were analyzed using parametric response mapping (PRM) to quantify markers of emphysema and functional small airways disease (fSAD). Segmental and lobar lung masks were obtained from CT and registered to HP 3He MRI in order to localize ventilation defect percent (VDP), at the lobar and segmental level, to regions of fSAD and mucus plugging. Spearman's correlation was utilized to compare biomarkers on a global and lobar level, and a multivariate analysis was conducted to predict segmental fSAD given segmental VDP (sVDP) and mucus score as variables in order to further understand the functional relationships between regional measures of obstruction. Results: On a global level, fSAD was correlated with whole lung VDP (r = 0.65, p < 0.001), mucus score (r = 0.55, p < 0.01), and moderately correlated (-0.60 ≤ r ≤ -0.56, p < 0.001) to percent predicted (%p) FEV1, FEF25-75 and FEV1/FVC, and more weakly correlated to FVC%p (-0.38 ≤ r ≤ -0.35, p < 0.001) as expected from previous work. On a regional level, lobar VDP, mucus scores, and fSAD were also moderately correlated (r from 0.45-0.66, p < 0.01). For segmental colocalization, the model of best fit was a piecewise quadratic model, which suggests that sVDP may be increasing due to local airway obstruction that does not manifest as fSAD until more extensive disease is present. sVDP was more sensitive to the presence of a mucus plugs overall, but the prediction of fSAD using multivariate regression showed an interaction in the presence of a mucus plugs when sVDP was between 4% and 10% (p < 0.001). Conclusion: This multi-modality study in asthma confirmed that areas of ventilation defects are spatially correlated with air trapping at the level of the airway segment and suggests VDP and fSAD are sensitive to specific sources of airway obstruction in asthma, including mucus plugs.

Xenon-enhanced dual-energy tomosynthesis for functional imaging of respiratory disease-Concept and phantom study

BACKGROUND

Xenon-enhanced dual-energy (DE) computed tomography (CT) and hyperpolarized noble-gas magnetic resonance imaging (MRI) provide maps of lung ventilation that can be used to detect chronic obstructive pulmonary disease (COPD) early in its development and predict respiratory exacerbations. However, xenon-enhanced DE-CT requires high radiation doses and hyper-polarized noble-gas MRI is expensive and only available at a handful of institutions globally.

PURPOSE

To present xenon-enhanced dual-energy tomosynthesis (XeDET) for low-dose, low-cost functional imaging of respiratory disease in an experimental phantom study.

METHODS

We propose using digital tomosynthesis to produce Xe-enhanced low-energy (LE) and high-energy (HE) coronal images. DE subtraction of the LE and HE images is used to suppress soft tissues. We used an imaging phantom to investigate image quality in terms of the area under the reciever operating characteristic curve (AUC) for the Non-PreWhitening model observer with an Eye filter and internal noise (NPWEi). The phantom simulated anatomic clutter due to lung parenchyma and attenuation due to soft tissue and lung tissue. Aluminum slats were used to simulate rib structures. A stepwedge consisting of an acrylic casing with sealed cylindrical air-filled cavities was used to simulate ventilation defects with step thicknesses of 0.5, 1, and 2 cm and cylindrical radii of 0.5, 0.75, and 1 cm. The phantom was ventilated with Xe and projection data were acquired using a flat-panel detector, a tube-voltage combination of 60/140 kV with 1.2  mm of copper filtration on the HE spectrum and an angular range of ± 15 ∘ $\pm 15^{\circ}$ in 1° increments. The AUC of a NPWEi observer that has access only to a single coronal slice was calculated from measurements of the three-dimensional noise power spectrum and signal template. The AUC was calculated as a function of ventilation defect thickness and radius for total patient entrance air kermas ranging from 1.42 to 2.84 mGy with and without rib-simulating Al slats. For the AUC analysis, the observer internal noise level was obtained from an ad hoc calibration to a high-dose data set.

RESULTS

XeDET was able to suppress parenchyma-simulating clutter in coronal images enabling visualization of the simulated ventilation defects, but the limited angle acquisition resulted in residual clutter due to out-of-plane bone-mimmicking structures. The signal power of the defects increased linearly with defect radius and showed a ten-fold to fifteen-fold increase in signal power when the defect thickness increased from 0.5 to 2 cm. These trends agreed with theoretical predictions. Along the depth dimension, the power of the defects decreased exponentially with distance from the center of the defects with full-width half maxima that varied from 1.85 to 2.85 cm depending on the defect thickness and radius. The AUCs of the 1-cm-radius defect that was 2 cm in thickness ranged from good (0.8-0.9) to excellent (0.9-1.0) over the range of air kermas considered.

CONCLUSIONS

Xenon-enhanced DE tomosynthesis has the potential to enable functional imaging of respiratory disease and should be further investigated as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-based approaches.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Quantitative CT parameters correlate with lung function in chronic obstructive pulmonary disease: A systematic review and meta-analysis

Objective

This study aimed to analyze the correlation between quantitative computed tomography (CT) parameters and airflow obstruction in patients with COPD.

Methods

PubMed, Embase, Cochrane and Web of Knowledge were searched by two investigators from inception to July 2022, using a combination of pertinent items to discover articles that investigated the relationship between CT measurements and lung function parameters in patients with COPD. Five reviewers independently extracted data, and evaluated it for quality and bias. The correlation coefficient was calculated, and heterogeneity was explored. The following CT measurements were extracted: percentage of lung attenuation area <-950 Hounsfield Units (HU), mean lung density, percentage of airway wall area, air trapping index, and airway wall thickness. Two airflow obstruction parameters were extracted: forced expiratory volume in the first second as a percentage of prediction (FEV₁%pred) and FEV₁ divided by forced expiratory volume lung capacity.

Results

A total of 141 studies (25,214 participants) were identified, which 64 (6,341 participants) were suitable for our meta-analysis. Results from our analysis demonstrated that there was a significant correlation between quantitative CT parameters and lung function. The absolute pooled correlation coefficients ranged from 0.26 (95% CI, 0.18 to 0.33) to 0.70 (95% CI, 0.65 to 0.75) for inspiratory CT and 0.56 (95% CI, 0.51 to 0.60) to 0.74 (95% CI, 0.68 to 0.80) for expiratory CT.

Conclusions

Results from this analysis demonstrated that quantitative CT parameters are significantly correlated with lung function in patients with COPD. With recent advances in chest CT, we can evaluate morphological features in the lungs that cannot be obtained by other clinical indices, such as pulmonary function tests. Therefore, CT can provide a quantitative method to advance the development and testing of new interventions and therapies for patients with COPD.

Experimental feasibility of xenon-enhanced dual-energy radiography for imaging of lung function

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. We experimentally investigated the feasibility of two-dimensional xenon-enhanced dual-energy (XeDE) radiography for imaging of lung function. We optimized image quality under quantum-noise-limited conditions using a chest phantom consisting of a rectangular chamber representing the thoracic volume and PMMA slabs simulating x-ray attenuation by soft tissue. A sealed, air-filled cavity with thin PMMA walls was positioned inside the chamber to simulate a 2 cm thick ventilation defect. The chamber was ventilated with xenon and dual-energy imaging was performed using a diagnostic x-ray tube and a flat-panel detector. The contrast-to-noise ratio of ventilation defects normalized by patient x-ray exposure maximized at a kV-pair of approximately 60/140-kV and when approximately one third of the total exposure was allocated to the HE image. We used the optimized technique to image a second phantom that contained lung-parenchyma-mimicking PMMA clutter, rib-mimicking aluminum slats and an insert that simulated ventilation defects with thicknesses ranging from 0.5 cm to 2 cm and diameters ranging from 1 cm to 2 cm. From the resulting images we computed the area under the receiver operating characteristic curve (AUC) of the non-prewhitening model observer with an eye filter and internal noise. For a xenon concentration of 75%, good AUCs (i.e. 0.8-0.9) to excellent AUCs (i.e. >0.9) were obtained when the defect diameter is greater than 1.3 cm and defect thickness is 1 cm. When the xenon concentration was reduced to 50%, the AUC was ∼0.9 for defects 1.2 cm in diameter and ∼1.5 cm in thickness. Two-dimensional XeDE radiography may therefore enable detection of functional abnormalities associated with early-stage COPD, for which xenon ventilation defects can occupy up to 20% of the lung volume, and should be further developed as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-based approaches.

Mapping Alveolar Oxygen Partial Pressure in COPD Using Hyperpolarized Helium-3: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study

Chronic obstructive pulmonary disease (COPD) and emphysema are characterized by functional and structural damage which increases the spaces for gaseous diffusion and impairs oxygen exchange. Here we explore the potential for hyperpolarized (HP) 3He MRI to characterize lung structure and function in a large-scale population-based study. Participants (n = 54) from the Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study, a nested case-control study of COPD among participants with 10+ packyears underwent HP 3He MRI measuring pAO2, apparent diffusion coefficient (ADC), and ventilation. HP MRI measures were compared to full-lung CT and pulmonary function testing. High ADC values (>0.4 cm2/s) correlated with emphysema and heterogeneity in pAO2 measurements. Strong correlations were found between the heterogeneity of global pAO2 as summarized by its standard deviation (SD) (p < 0.0002) and non-physiologic pAO2 values (p < 0.0001) with percent emphysema on CT. A regional study revealed a strong association between pAO2 SD and visual emphysema severity (p < 0.003) and an association with the paraseptal emphysema subtype (p < 0.04) after adjustment for demographics and smoking status. HP noble gas pAO2 heterogeneity and the fraction of non-physiological pAO2 results increase in mild to moderate COPD. Measurements of pAO2 are sensitive to regional emphysematous damage detected by CT and may be used to probe pulmonary emphysema subtypes. HP noble gas lung MRI provides non-invasive information about COPD severity and lung function without ionizing radiation.

Lung MRI with hyperpolarised gases: current & future clinical perspectives

The use of pulmonary MRI in a clinical setting has historically been limited. Whilst CT remains the gold-standard for structural lung imaging in many clinical indications, technical developments in ultrashort and zero echo time MRI techniques are beginning to help realise non-ionising structural imaging in certain lung disorders. In this invited review, we discuss a complementary technique - hyperpolarised (HP) gas MRI with inhaled ³He and ¹²⁹Xe - a method for functional and microstructural imaging of the lung that has great potential as a clinical tool for early detection and improved understanding of pathophysiology in many lung diseases. HP gas MRI now has the potential to make an impact on clinical management by enabling safe, sensitive monitoring of disease progression and response to therapy. With reference to the significant evidence base gathered over the last two decades, we review HP gas MRI studies in patients with a range of pulmonary disorders, including COPD/emphysema, asthma, cystic fibrosis, and interstitial lung disease. We provide several examples of our experience in Sheffield of using these techniques in a diagnostic clinical setting in challenging adult and paediatric lung diseases.

Evaluation of small airway function and its application in patients with chronic obstructive pulmonary disease (Review)

Chronic obstructive pulmonary disease (COPD) is a chronic airway inflammatory disease characterized by incomplete reversible airflow limitation. The diagnosis of COPD is mainly based on pulmonary function examination. In recent years, it has been indicated that small airway dysfunction occurs in patients with all stages of COPD, even in high-risk smoking groups who have not yet met the diagnostic criteria for COPD. Early recognition of small airway dysfunction and early initiation of small airway targeted therapy have become foci of research. In the present review, the methods of evaluating small airway function were summarized and their merits and shortcomings were discussed. Furthermore, the potential of targeted treatment of small airways in patients with COPD was outlined.

Seven Pillars of Small Airways Disease in Asthma and COPD: Supporting Opportunities for Novel Therapies

Identification of pathologic changes in early and mild obstructive lung disease has shown the importance of the small airways and their contribution to symptoms. Indeed, significant small airways dysfunction has been found prior to any overt airway obstruction being detectable by conventional spirometry techniques. However, most therapies for the treatment of obstructive lung disease target the physiological changes and associated symptoms that result from chronic lung disease, rather than directly targeting the specific underlying causes of airflow disruption or the drivers of disease progression. In addition, although spirometry is the current standard for diagnosis and monitoring of response to therapy, the most widely used measure, FEV1 , does not align with the pathologic changes in early or mild disease and may not align with symptoms or exacerbation frequency in the individual patient. Newer functional and imaging techniques allow more effective assessment of small airways dysfunction; however, significant gaps in our understanding remain. Improving our knowledge of the role of small airways dysfunction in early disease in the airways, along with the identification of novel end points to measure subclinical changes in this region (ie, those not captured as symptoms or identified through standard FEV1), may lead to the development of novel therapies that directly combat early airways disease processes with a view to slowing disease progression and reversing damage. This expert opinion paper discusses small airways disease in the context of asthma and COPD and highlights gaps in current knowledge that impede earlier identification of obstructive lung disease and the development and standardization of novel small airways-specific end points for use in clinical trials.

Ultra-short echo-time magnetic resonance imaging lung segmentation with under-Annotations and domain shift

Ultra-short echo-time (UTE) magnetic resonance imaging (MRI) provides enhanced visualization of pulmonary structural and functional abnormalities and has shown promise in phenotyping lung disease. Here, we describe the development and evaluation of a lung segmentation approach to facilitate UTE MRI methods for patient-based imaging. The proposed approach employs a k-means algorithm in kernel space for pair-wise feature clustering and imposes image domain continuous regularization, coined as continuous kernel k-means (CKKM). The high-order CKKM algorithm was simplified through upper bound relaxation and solved within an iterative continuous max-flow framework. We combined the CKKM with U-net and atlas-based approaches and comprehensively evaluated the performance on 100 images from 25 patients with asthma and bronchial pulmonary dysplasia enrolled at Robarts Research Institute (Western University, London, Canada) and Centre Hospitalier Universitaire (Sainte-Justine, Montreal, Canada). For U-net, we trained the network five times on a mixture of five different images with under-annotations and applied the model to 64 images from the two centres. We also trained a U-net on five images with full and brush annotations from one centre, and tested the model on 32 images from the other centre. For an atlas-based approach, we employed three atlas images to segment 64 target images from the two centres through straightforward atlas registration and label fusion. We applied the CKKM algorithm to the baseline U-net and atlas outputs and refined the initial segmentation through multi-volume image fusion. The integration of CKKM substantially improved baseline results and yielded, with minimal computational cost, segmentation accuracy, and precision that were greater than some state-of-the-art deep learning models and similar to experienced observer manual segmentation. This suggests that deep learning and atlas-based approaches may be utilized to segment UTE MRI datasets using relatively small training datasets with under-annotations.

Pulmonary Functional Imaging: Part 1-State-of-the-Art Technical and Physiologic Underpinnings

Over the past few decades, pulmonary imaging technologies have advanced from chest radiography and nuclear medicine methods to high-spatial-resolution or low-dose chest CT and MRI. It is currently possible to identify and measure pulmonary pathologic changes before these are obvious even to patients or depicted on conventional morphologic images. Here, key technological advances are described, including multiparametric CT image processing methods, inhaled hyperpolarized and fluorinated gas MRI, and four-dimensional free-breathing CT and MRI methods to measure regional ventilation, perfusion, gas exchange, and biomechanics. The basic anatomic and physiologic underpinnings of these pulmonary functional imaging techniques are explained. In addition, advances in image analysis and computational and artificial intelligence (machine learning) methods pertinent to functional lung imaging are discussed. The clinical applications of pulmonary functional imaging, including both the opportunities and challenges for clinical translation and deployment, will be discussed in part 2 of this review. Given the technical advances in these sophisticated imaging methods and the wealth of information they can provide, it is anticipated that pulmonary functional imaging will be increasingly used in the care of patients with lung disease. © RSNA, 2021 Online supplemental material is available for this article.

Overview of MRI for pulmonary functional imaging

Morphological evaluation of the lung is important in the clinical evaluation of pulmonary diseases. However, the disease process, especially in its early phases, may primarily result in changes in pulmonary function without changing the pulmonary structure. In such cases, the traditional imaging approaches to pulmonary morphology may not provide sufficient insight into the underlying pathophysiology. Pulmonary imaging community has therefore tried to assess pulmonary diseases and functions utilizing not only nuclear medicine, but also CT and MR imaging with various technical approaches. In this review, we overview state-of-the art MR methods and the future direction of: (1) ventilation imaging, (2) perfusion imaging and (3) biomechanical evaluation for pulmonary functional imaging.

Pulmonary Ventilation Maps Generated with Free-breathing Proton MRI and a Deep Convolutional Neural Network

Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providing information about ventilation not visible to the eye or easily extracted with segmentation methods. Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilation maps as a surrogate of noble gas MRI and to validate this approach across a wide range of lung diseases. Materials and Methods In this secondary analysis of prospective trials, 114 paired noble gas MRI and two-dimensional free-breathing MRI scans were obtained in healthy volunteers with no history of chronic or acute respiratory disease and in study participants with a range of different obstructive lung diseases, including asthma, bronchiectasis, chronic obstructive pulmonary disease, and non-small-cell lung cancer between September 2013 and April 2018 (ClinicalTrials.gov identifiers: NCT03169673, NCT02351141, NCT02263794, NCT02282202, NCT02279329, and NCT02002052). A U-Net-based DCNN model was trained to map free-breathing proton MRI to hyperpolarized helium 3 (³He) MRI ventilation and validated using a sixfold validation. During training, the DCNN ventilation maps were compared with noble gas MRI scans using the Pearson correlation coefficient (r) and mean absolute error. DCNN ventilation images were segmented for ventilation and ventilation defects and were compared with noble gas MRI scans using the Dice similarity coefficient (DSC). Relationships were evaluated with the Spearman correlation coefficient (r_S). Results One hundred fourteen study participants (mean age, 56 years ± 15 [standard deviation]; 66 women) were evaluated. As compared with ³He MRI, DCNN model ventilation maps had a mean r value of 0.87 ± 0.08. The mean DSC for DL ventilation MRI and ³He MRI ventilation was 0.91 ± 0.07. The ventilation defect percentage for DL ventilation MRI was highly correlated with ³He MRI ventilation defect percentage (r_S = 0.83, P < .001, mean bias = -2.0% ± 5). Both DL ventilation MRI (r_S = -0.51, P < .001) and ³He MRI (r_S = -0.61, P < .001) ventilation defect percentage were correlated with the forced expiratory volume in 1 second. The DCNN model required approximately 2 hours for training and approximately 1 second to generate a ventilation map. Conclusion In participants with diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps from free-breathing proton MRI trained with a hyperpolarized noble-gas MRI ventilation map data set. The maps showed correlation with noble gas MRI ventilation and pulmonary function measurements. © RSNA, 2020 See also the editorial by Vogel-Claussen in this issue.

Calculating air volume fractions from computed tomography images for chronic obstructive pulmonary disease diagnosis

Quantitative evaluation using image biomarkers calculated from threshold-segmented low-attenuation areas on chest computed tomography (CT) images for diagnosing chronic obstructive pulmonary diseases (COPD) has been widely investigated. However, the segmentation results depend on the applied threshold and slice thickness of the CT images because of the partial volume effect (PVE). In this study, the air volume fraction (AV/TV) of lungs was calculated from CT images using a two-compartment model (TCM) for COPD diagnosis. A relative air volume histogram (RAVH) was constructed using the AV/TV values to describe the air content characteristics of lungs. In phantom studies, the TCM accurately calculated total cavity volumes and foam masses with percent errors of less than 8% and ±4%, respectively. In patient studies, the relative volumes of normal and damaged lung tissues and the damaged-to-normal RV ratio were defined and calculated from the RAVHs as image biomarkers, which correctly differentiated COPD patients from controls in 2.5- and 5-mm-thick images with areas under receiver operating characteristic curves of >0.94. The AV/TV calculated using the TCM can prevent the effect of slice thickness, and the image biomarkers calculated from the RAVH are reliable for diagnosing COPD

Parametric Response Mapping of Coregistered Positron Emission Tomography and Dynamic Contrast Enhanced Computed Tomography to Identify Radioresistant Subvolumes in Locally Advanced Cervical Cancer

PURPOSE

To identify subvolumes that may predict treatment response to definitive concurrent chemoradiation therapy using parametric response mapping (PRM) of coregistered positron emission tomography (PET) and dynamic contrast-enhanced (DCE) computed tomography (CT) in locally advanced cervical carcinoma.

METHODS AND MATERIALS

Pre- and midtreatment (after 23 ± 4 days of concurrent chemoradiation therapy) DCE CT and PET imaging were performed on 21 patients with cervical cancer who were enrolled in a pilot study to evaluate the prognostic value of CT perfusion for primary cervical cancer (NCT01805141). Three-dimensional coregistered maps of PET/CT standardized uptake value (SUV) and DCE CT blood flow (BF) were generated. PRM was performed using voxel-wise joint histogram analysis to classify voxels within the tumor as highly metabolic and perfused (SUV^hiBF^hi), highly metabolic and hypoxic (SUV^hiBF^lo), low metabolic activity and hypoxic (SUV^loBF^lo), or low metabolic activity and perfused (SUV^loBF^hi) tissue based on thresholds determined from population means of pretreatment PET SUV and DCE CT BF. Relationships between baseline pretreatment imaging metrics and relative changes in metabolic tumor volume (ΔMTV), calculated from before treatment and during treatment imaging, were determined using univariable and multivariable linear regression models.

RESULTS

The relative volume of three PRM subvolumes significantly changed during treatment (SUV^hiBF^hi: P = .04; SUV^hiBF^lo: P = .0008; SUV^loBF^hi: P = .02), whereas SUV^loBF^lo did not (P = .9). Pretreatment PET SUV_max (r = -.58, P = .006), PET SUV_mean (ρ = -.59, P = .005), DCE CT BF_mean (r = -.50, P = .02), tumor volume (ρ = -.65, P = .001) and PRM SUV^hiBF^hi (ρ = -.59, P = .004) were negatively correlated with ΔMTV, whereas PRM SUV^loBF^lo was positively related to ΔMTV (r = .77, P < .0001). In a multivariable model that predicted ΔMTV, PRM SUV^loBF^lo, which combines both PET/CT and DCE CT, was the only significant variable (β = 1.825, P = .03), dominating both imaging modalities independently.

CONCLUSIONS

PRM was applied in locally advanced cervical carcinoma treated definitively with chemoradiation, and radioresistant subvolumes were identified that correlated with changes in MTV and predicted treatment response. Identification of these subvolumes may assist in clinical decision making to tailor therapies, such as brachytherapy, in an effort to improve patient outcomes.

Biomarkers for chronic obstructive pulmonary disease diagnosis and progression: insights, disappointments and promise

PURPOSE OF REVIEW

This article reviews the status of biomarkers useful in the diagnosis and progression of chronic obstructive pulmonary disease (COPD). Biomarkers have been the focus of a great deal of COPD-related research in recent years, although useful markers in these specific arenas remain elusive.

RECENT FINDINGS

No biomarker other than lung function has been shown to be useful, to date, for the diagnosis of COPD. The best blood-based biomarkers for the progression of COPD may involve combinations of individual markers, such as CC16, fibrinogen and sRAGE. New imaging metrics, such as central airway collapse, pulmonary vascular changes and central airway branch variation, may be able to provide valuable prognostic and information, although these remain confined to research applications.

SUMMARY

Blood-based biomarkers for diagnosing and determining the progression of COPD remain disappointingly elusive. Although there have been some advances in nonblood-based markers, such as those from imaging, exhaled breath or physiologic assessment, these remain limited, for the most part, to research applications. Moving toward better markers that could be used in clinical application in the screening and diagnosis of COPD that could also provide prognostic information remains an important goal of research.

Pulmonary Imaging Phenotypes of Chronic Obstructive Pulmonary Disease Using Multiparametric Response Maps

Background Pulmonary imaging of chronic obstructive pulmonary disease (COPD) has focused on CT or MRI measurements, but these have not been evaluated in combination. Purpose To generate multiparametric response map (mPRM) measurements in ex-smokers with or without COPD by using volume-matched CT and hyperpolarized helium 3 (³He) MRI. Materials and Methods In this prospective study (https://clinicaltrials.gov, NCT02279329), participants underwent MRI and CT and completed pulmonary function tests, questionnaires, and the 6-minute walk test between December 2010 and January 2019. Disease status was determined by using Global initiative for chronic Obstructive Lung Disease (GOLD) criteria. The mPRM voxel values were generated by using co-registered MRI and CT labels. Kruskal-Wallis and Bonferroni tests were used to determine differences across disease severity, and correlations were determined by using Spearman coefficients. Results A total of 175 ex-smokers (mean age, 69 years ± 9 [standard deviation], 108 men) with or without COPD were evaluated. Ex-smokers without COPD had a larger fraction of normal mPRM voxels (60% vs 37%, 20%, and 7% for GOLD I, II, and III/IV disease, respectively; all P ≤ .001) and a smaller fraction of abnormal voxels, including small airways disease (normal CT, not ventilated: 5% vs 6% [not significant], 11%, and 19% [P ≤ .001 for both] for GOLD I, II, and III/IV disease, respectively) and mild emphysema (normal CT, abnormal apparent diffusion coefficient [ADC]: 33% vs 54%, 56%, and 54% for GOLD I, II, and III/IV disease respectively; all P ≤ .001). Normal mPRM measurements were positively correlated with forced expiratory volume in 1 second (FEV₁) (r = 0.65, P < .001), the FEV₁-to-forced vital capacity ratio (r = 0.81, P < .001), and diffusing capacity (r = 0.75, P < .001) and were negatively correlated with worse quality of life (r = -0.48, P < .001). Abnormal mPRM measurements of small airways disease (normal CT, not ventilated) and mild emphysema (normal CT, abnormal ADC) were negatively correlated with FEV₁ (r = -0.65 and -0.42, respectively; P < .001) and diffusing capacity (r = -0.53 and -0.60, respectively; P < .001) and were positively correlated with worse quality of life (r = 0.45 and r = 0.33, respectively; P < .001), both of which were present in ex-smokers without COPD. Conclusion Multiparametric response maps revealed two abnormal structure-function results related to emphysema and small airways disease, both of which were unexpectedly present in ex-smokers with normal spirometry and CT findings. © RSNA, 2020 Online supplemental material is available for this article.

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (³He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP ³He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP ³He MRI (r = 0.90, P < .001). Both model-predicted and HP ³He MRI VDP were correlated with forced expiratory volume in 1 second (FEV₁) (model: r = -0.65, P < .001; MRI: r = -0.70, P < .001), ratio of FEV₁ to forced vital capacity (model: r = -0.73, P < .001; MRI: r = -0.75, P < .001), diffusing capacity (model: r = -0.69, P < .001; MRI: r = -0.65, P < .001), and quality-of-life score (model: r = 0.59, P = .001; MRI: r = 0.65, P < .001). Conclusion Model-predicted ventilation maps generated by using CT textures and machine learning were correlated with MRI ventilation maps (r = 0.90, P < .001). © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fain in this issue.

CT and Functional MRI to Evaluate Airway Mucus in Severe Asthma

BACKGROUND

Intraluminal contributor(s) to airflow obstruction in severe asthma are patient-specific and must be evaluated to personalize treatment. The occurrence and functional consequence of airway mucus in the presence or absence of airway eosinophils remain undetermined.

OBJECTIVE

The objective of this study was to understand the functional consequence of airway mucus in the presence or absence of eosinophils and to identify biomarkers of mucus-related airflow obstruction.

METHODS

Mucus plugs were quantified on CT scans, and their contribution to ventilation heterogeneity (using MRI ventilation defect percent [VDP]) was evaluated in 27 patients with severe asthma. Patients were dichotomized based on sputum eosinophilia such that the relationship between mucus, eosinophilia, and ventilation heterogeneity could be investigated. Fractional exhaled nitric oxide (Feno) and related cytokines in sputum were measured.

RESULTS

Mucus plugging was present in 100% of asthma patients with sputum eosinophils and 36% of those without sputum eosinophils (P = .0006) and was correlated with MRI VDP prebronchodilator (r = 0.68; P = .0001) and postbronchodilator (r = 0.72; P < .0001). In a multivariable regression, both mucus and eosinophils contributed to the prediction of postbronchodilator MRI VDP (R² = 0.75; P < .0001). Patients with asthma in whom the mucus score was high had raised Feno (P = .03) and IL-4 (P = .02) values. Mucus plugging correlated with Feno (r = 0.63; P = .005).

CONCLUSIONS

Both airway eosinophils and mucus can contribute to ventilation heterogeneity in patients with severe asthma. Patients in whom mucus is the dominant cause of airway obstruction have evidence of an upregulated IL-4/IL-13 pathway that could be identified according to increased Feno level.

Oscillometry and pulmonary magnetic resonance imaging in asthma and COPD

Developed over six decades ago, pulmonary oscillometry has re-emerged as a noninvasive and effort-independent method for evaluating respiratory-system impedance in patients with obstructive lung disease. Here, we evaluated the relationships between hyperpolarized 3 He ventilation-defect-percent (VDP) and respiratory-system resistance, reactance and reactance area (AX ) measurements in 175 participants including 42 never-smokers without respiratory disease, 56 ex-smokers with chronic-obstructive-pulmonary-disease (COPD), 28 ex-smokers without COPD and 49 asthmatic never-smokers. COPD participants were dichotomized based on x-ray computed-tomography (CT) evidence of emphysema (relative-area CT-density-histogram ≤ 950HU (RA950 ) ≥ 6.8%). In asthma and COPD subgroups, MRI VDP was significantly related to the frequency-dependence of resistance (R5-19 ; asthma: ρ = 0.48, P = 0.0005; COPD: ρ = 0.45, P = 0.0004), reactance at 5 Hz (X5 : asthma, ρ = -0.41, P = 0.004; COPD: ρ = -0.38, P = 0.004) and AX (asthma: ρ = 0.47, P = 0.0007; COPD: ρ = 0.43, P = 0.0009). MRI VDP was also significantly related to R5-19 in COPD participants without emphysema (ρ = 0.54, P = 0.008), and to X5 in COPD participants with emphysema (ρ = -0.36, P = 0.04). AX was weakly related to VDP in asthma (ρ = 0.47, P = 0.0007) and COPD participants with (ρ = 0.39, P = 0.02) and without (ρ = 0.43, P = 0.04) emphysema. AX is sensitive to obstruction but not specific to the type of obstruction, whereas the different relationships for MRI VDP with R5-19 and X5 may reflect the different airway and parenchymal disease-specific biomechanical abnormalities that lead to ventilation defects.

COPD biomarkers and phenotypes: opportunities for better outcomes with precision imaging

A number of chronic diseases have benefited from both imaging and personalised medicine, but unfortunately, for patients with chronic obstructive pulmonary disease (COPD), there has been little clinical uptake or recognition of the key advances in thoracic imaging that might help detect disease early, or, perhaps more importantly, might help develop and phenotype patients for novel or personalised therapies that may halt disease progression. We outline our vision for how computed tomography and magnetic resonance imaging may be used to better inform COPD patient care, and, perhaps more importantly, how these may be used to help develop new therapies directed at early disease. We think that imaging and precision medicine should be considered and used together as "precision imaging" at specific stages of COPD when the major pathologies may be more responsive to therapy. While "precision medicine" is the tailoring of medical treatment to individual patients, we define "precision imaging" as the tailoring of specific therapies and interventions to individual patients with a detailed quantitative understanding of their specific imaging phenotypes and measurements. Finally, we stress the importance of "seeing" the pathology, because without this understanding, you can neither treat nor cure patients with COPD.

ACR Appropriateness Criteria® Chronic Dyspnea-Noncardiovascular Origin

Chronic dyspnea may result from a variety of disorders of cardiovascular, pulmonary, gastrointestinal, neuromuscular, systemic, and psychogenic etiology. This article discusses guidelines for the initial imaging of six variants for chronic dyspnea of noncardiovascular origin: (1) Chronic dyspnea of unclear etiology; (2) Chronic dyspnea with suspected chronic obstructive pulmonary disease; (3) Chronic dyspnea with suspected central airways disease; (4) Chronic dyspnea with suspected interstitial lung disease; (5) Chronic dyspnea with suspected disease of the pleura or chest wall; and (6) Chronic dyspnea with suspected diaphragm dysfunction. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Correlation of Regional Lung Ventilation and Gas Transfer to Red Blood Cells: Implications for Functional-Avoidance Radiation Therapy Planning

PURPOSE

To investigate the degree to which lung ventilation and gas exchange are regionally correlated, using the emerging technology of hyperpolarized (HP)-129Xe magnetic resonance imaging (MRI).

METHODS AND MATERIALS

Hyperpolarized-129Xe MRI studies were performed on 17 institutional review board-approved human subjects, including 13 healthy volunteers, 1 emphysema patient, and 3 non-small cell lung cancer patients imaged before and approximately 11 weeks after radiation therapy (RT). Subjects inhaled 1 L of HP-129Xe mixture, followed by the acquisition of interleaved ventilation and gas exchange images, from which maps were obtained of the relative HP-129Xe distribution in three states: (1) gaseous, in lung airspaces; (2) dissolved interstitially, in alveolar barrier tissue; and (3) transferred to red blood cells (RBCs), in the capillary vasculature. The relative spatial distributions of HP-129Xe in airspaces (regional ventilation) and RBCs (regional gas transfer) were compared. Further, we investigated the degree to which ventilation and RBC transfer images identified similar functional regions of interest (ROIs) suitable for functionally guided RT. For the RT patients, both ventilation and RBC functional images were used to calculate differences in the lung dose-function histogram and functional effective uniform dose.

RESULTS

The correlation of ventilation and RBC transfer was ρ = 0.39 ± 0.15 in healthy volunteers. For the RT patients, this correlation was ρ = 0.53 ± 0.02 before treatment and ρ = 0.39 ± 0.07 after treatment; for the emphysema patient it was ρ = 0.24. Comparing functional ROIs, ventilation and RBC transfer demonstrated poor spatial agreement: Dice similarity coefficient = 0.50 ± 0.07 and 0.26 ± 0.12 for the highest-33%- and highest-10%-function ROIs in healthy volunteers, and in RT patients (before treatment) these were 0.58 ± 0.04 and 0.40 ± 0.04. The average magnitude of the differences between RBC- and ventilation-derived functional effective uniform dose, fV20Gy, fV10Gy, and fV5Gy were 1.5 ± 1.4 Gy, 4.1% ± 3.8%, 5.0% ± 3.8%, and 5.3% ± 3.9%, respectively.

CONCLUSION

Ventilation may not be an effective surrogate for true regional lung function for all patients.

Pruning of the Pulmonary Vasculature in Asthma. The Severe Asthma Research Program (SARP) Cohort

RATIONALE

Loss of the peripheral pulmonary vasculature, termed vascular pruning, is associated with disease severity in patients with chronic obstructive pulmonary disease.

OBJECTIVES

To determine if pulmonary vascular pruning is associated with asthma severity and exacerbations.

METHODS

We measured the total pulmonary blood vessel volume (TBV) and the blood vessel volume of vessels less than 5 mm2 in cross-sectional area (BV5) and of vessels less than 10 mm2 (BV10) in cross-sectional area on noncontrast computed tomographic scans of participants from the Severe Asthma Research Program. Lower values of the BV5 to TBV ratio (BV5/TBV) and the BV10 to TBV ratio (BV10/TBV) represented vascular pruning (loss of the peripheral pulmonary vasculature).

MEASUREMENTS AND MAIN RESULTS

Compared with healthy control subjects, patients with severe asthma had more pulmonary vascular pruning. Among those with asthma, those with poor asthma control had more pruning than those with well-controlled disease. Pruning of the pulmonary vasculature was also associated with lower percent predicted FEV1 and FVC, greater peripheral and sputum eosinophilia, and higher BAL serum amyloid A/lipoxin A4 ratio but not with low-attenuation area or with sputum neutrophilia. Compared with individuals with less pruning, individuals with the most vascular pruning had 150% greater odds of reporting an asthma exacerbation (odds ratio, 2.50; confidence interval, 1.05-5.98; P = 0.039 for BV10/TBV) and reported 45% more asthma exacerbations during follow-up (incidence rate ratio, 1.45; confidence interval, 1.02-2.06; P = 0.036 for BV10/TBV).

CONCLUSIONS

Pruning of the peripheral pulmonary vasculature is associated with asthma severity, control, and exacerbations, and with lung function and eosinophilia.

3He diffusion MRI in human lungs

Hyperpolarized 3He gas allows the air spaces of the lungs to be imaged via MRI. Imaging of restricted diffusion is addressed here, which allows the microstructure of the lung to be characterized through the physical restrictions to gas diffusion presented by airway and alveolar walls in the lung. Measurements of the apparent diffusion coefficient (ADC) of 3He at time scales of milliseconds and seconds are compared; measurement of acinar airway sizes by determination of the microscopic anisotropy of diffusion is discussed. This is where Dr. JJH Ackerman's influence was greatest in aiding the formation of the Washington University 3He group, involving early a combination of physicists, radiologists, and surgeons, as the first applications of 3He ADC were to COPD and its destruction/modification of lung microstructure via emphysema. The sensitivity of the method to early COPD is demonstrated, as is its validation by direct comparison to histology. More recently the method has been used broadly in adult and pediatric obstructive lung diseases, from severe asthma to cystic fibrosis to bronchopulmonary dysplasia, a result of premature birth. These applications of the technique are discussed briefly.

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

We designed and generated pulmonary imaging biomarker pipelines to facilitate high-throughput research and point-of-care use in patients with chronic lung disease. Image processing modules and algorithm pipelines were embedded within a graphical user interface (based on the .NET framework) for pulmonary magnetic resonance imaging (MRI) and x-ray computed-tomography (CT) datasets. The software pipelines were generated using C++ and included: (1) inhaled He3/Xe129 MRI ventilation and apparent diffusion coefficients, (2) CT-MRI coregistration for lobar and segmental ventilation and perfusion measurements, (3) ultrashort echo-time H1 MRI proton density measurements, (4) free-breathing Fourier-decomposition H1 MRI ventilation/perfusion and free-breathing H1 MRI specific ventilation, (5) multivolume CT and MRI parametric response maps, and (6) MRI and CT texture analysis and radiomics. The image analysis framework was implemented on a desktop workstation/tablet to generate biomarkers of regional lung structure and function related to ventilation, perfusion, lung tissue texture, and integrity as well as multiparametric measures of gas trapping and airspace enlargement. All biomarkers were generated within 10 min with measurement reproducibility consistent with clinical and research requirements. The resultant pulmonary imaging biomarker pipeline provides real-time and automated lung imaging measurements for point-of-care and high-throughput research.

On the Potential Role of MRI Biomarkers of COPD to Guide Bronchoscopic Lung Volume Reduction

RATIONALE AND OBJECTIVES

In patients with severe emphysema and poor quality of life, bronchoscopic lung volume reduction (BLVR) may be considered and guided based on lobar emphysema severity. In particular, x-ray computed tomography (CT) emphysema measurements are used to identify the most diseased and the second-most diseased lobes as BLVR targets. Inhaled gas magnetic resonance imaging (MRI) also provides chronic obstructive pulmonary disease (COPD) biomarkers of lobar emphysema and ventilation abnormalities. Our objective was to retrospectively evaluate CT and MRI biomarkers of lobar emphysema and ventilation in patients with COPD eligible for BLVR. We hypothesized that MRI would provide complementary biomarkers of emphysema and ventilation that help determine the most appropriate lung lobar targets for BLVR in patients with COPD.

MATERIALS AND METHODS

We retrospectively evaluated 22 BLVR-eligible patients from the Thoracic Imaging Network of Canada cohort (diffusing capacity of the lung for carbon monoxide = 37 ± 12%_predicted, forced expiratory volume in 1 second = 34 ± 7%_predicted, total lung capacity = 131 ± 17%_predicted, and residual volume = 216 ± 36%_predicted). Lobar CT emphysema, measured using a relative area of <-950 Hounsfield units (RA₉₅₀) and MRI ventilation defect percent, was independently used to rank lung lobe disease severity.

RESULTS

In 7 of 22 patients, there were different CT and MRI predictions of the most diseased lobe. In some patients, there were large ventilation defects in lobes not targeted by CT, indicative of a poorly ventilated lung. CT and MRI classification of the most diseased and the second-most diseased lobes showed a fair-to-moderate intermethod reliability (Cohen κ = 0.40-0.59).

CONCLUSIONS

In this proof-of-concept retrospective analysis, quantitative MRI ventilation and CT emphysema measurements provided different BLVR targets in over 30% of the patients. The presence of large MRI ventilation defects in lobes next to CT-targeted lobes might also change the decision to proceed or to guide BLVR to a different lobar target.

Emphysematous and Nonemphysematous Gas Trapping in Chronic Obstructive Pulmonary Disease: Quantitative CT Findings and Pulmonary Function

Purpose To identify a prevalent computed tomography (CT) subtype in patients with chronic obstructive pulmonary disease (COPD) by separating emphysematous from nonemphysematous contributions to total gas trapping and to attempt to predict and grade the emphysematous gas trapping by using clinical and functional data. Materials and Methods Two-hundred and two consecutive eligible patients (159 men and 43 women; mean age, 70 years [age range, 41-85 years]) were prospectively studied. Pulmonary function and CT data were acquired by pulmonologists and radiologists. Noncontrast agent-enhanced thoracic CT scans were acquired at full inspiration and expiration, and were quantitatively analyzed by using two software programs. CT parameters were set as follows: 120 kVp; 200 mAs; rotation time, 0.5 second; pitch, 1.1; section thickness, 0.75 mm; and reconstruction kernels, b31f and b70f. Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds (percentage area with CT attenuation values less than -950 HU at inspiration and percentage area with CT attenuation values less than -856 HU at expiration) was compared with that obtained by coregistration analysis. A logistic regression model on the basis of anthropometric and functional data was cross-validated and trained to classify patients with COPD according to the relative contribution of emphysema to total gas trapping, as assessed at CT. Results Gas trapping obtained by difference of inspiratory and expiratory CT density thresholds was highly correlated (r = 0.99) with that obtained by coregistration analysis. Four groups of patients were distinguished according to the prevalent CT subtype: prevalent emphysematous gas trapping, prevalent functional gas trapping, mixed severe, and mixed mild. The predictive model included predicted forced expiratory volume in 1 second/vital capacity, percentage of predicted forced expiratory volume in 1 second, percentage of diffusing capacity for carbon monoxide, and body mass index as emphysema regressors at CT, with 81% overall accuracy in classifying patients according to its extent. Conclusion The relative contribution of emphysematous and nonemphysematous gas trapping obtained by coregistration of inspiratory and expiratory CT scanning can be determined accurately by difference of CT inspiratory and expiratory density thresholds. CT extent of emphysema can be predicted with accuracy suitable for clinical purposes by pulmonary function data and body mass index. ^© RSNA, 2018 Online supplemental material is available for this article.

Pulmonary 3He Magnetic Resonance Imaging Biomarkers of Regional Airspace Enlargement in Alpha-1 Antitrypsin Deficiency

RATIONALE AND OBJECTIVES

Thoracic x-ray computed tomography (CT) and hyperpolarized ³He magnetic resonance imaging (MRI) provide quantitative measurements of airspace enlargement in patients with emphysema. For patients with panlobular emphysema due to alpha-1 antitrypsin deficiency (AATD), sensitive biomarkers of disease progression and response to therapy have been difficult to develop and exploit, especially those biomarkers that correlate with outcomes like quality of life. Here, our objective was to generate and compare CT and diffusion-weighted inhaled-gas MRI measurements of emphysema including apparent diffusion coefficient (ADC) and MRI-derived mean linear intercept (L_m) in patients with AATD, chronic obstructive pulmonary disease (COPD) ex-smokers, and elderly never-smokers.

MATERIALS AND METHODS

We enrolled patients with AATD (n = 8; 57 ± 7 years), ex-smokers with COPD (n = 8; 77 ± 6 years), and a control group of never-smokers (n = 5; 64 ± 2 years) who underwent thoracic CT, MRI, spirometry, plethysmography, the St. George's Respiratory Questionnaire, and the 6-minute walk test during a single 2-hour visit. MRI-derived ADC, L_m, surface-to-volume ratio, and ventilation defect percent were generated for the apical, basal, and whole lung as was CT lung area ≤-950 Hounsfield units (RA₉₅₀), low attenuating clusters, and airway count.

RESULTS

In patients with AATD, there was a significantly different MRI-derived ADC (P = .03), L_m (P < .0001), and surface-to-volume ratio (P < .0001), but not diffusing capacity of carbon monoxide, residual volume or total lung capacity, or CT RA₉₅₀ (P > .05) compared to COPD ex-smokers with a significantly different St. George's Respiratory Questionnaire.

CONCLUSIONS

In this proof-of-concept demonstration, we evaluated CT and MRI lung emphysema measurements and observed significantly worse MRI biomarkers of emphysema in patients with AATD compared to patients with COPD, although CT RA₉₅₀ and diffusing capacity of carbon monoxide were not significantly different, underscoring the sensitivity of MRI measurements of AATD emphysema.

Aging Airways: between Normal and Disease. A Multidimensional Diagnostic Approach by Combining Clinical, Functional, and Imaging Data

The lack of data on lung function decline in the aging process as well as the lack of gold standards to define obstructive and restrictive respiratory disease in older people point out the need for a multidimensional assessment and interpretation of the aging airways. By integrating clinical data together with morphologic and morphometric findings clinicians can assess the airways with a more comprehensive perspective, helpful in the interpretation of the "grey zone" between normal aging and disease. This review focuses on the value of a multidimensional approach in the study of the aging airways, including clinical findings, respiratory function tests, and imaging as parts of a whole. Nowadays this multidimensional diagnostic approach can be used in daily clinical practice. In next future, it can be implemented by the analysis of exhaled gases, post-processing imaging techniques, and genetic analysis, that will hopefully reduce the gaps in knowledge of normal aging and airway disease in older people.

CT-Based Local Distribution Metric Improves Characterization of COPD

Parametric response mapping (PRM) of paired CT lung images has been shown to improve the phenotyping of COPD by allowing for the visualization and quantification of non-emphysematous air trapping component, referred to as functional small airways disease (fSAD). Although promising, large variability in the standard method for analyzing PRMfSAD has been observed. We postulate that representing the 3D PRMfSAD data as a single scalar quantity (relative volume of PRMfSAD) oversimplifies the original 3D data, limiting its potential to detect the subtle progression of COPD as well as varying subtypes. In this study, we propose a new approach to analyze PRM. Based on topological techniques, we generate 3D maps of local topological features from 3D PRMfSAD classification maps. We found that the surface area of fSAD (SfSAD) was the most robust and significant independent indicator of clinically meaningful measures of COPD. We also confirmed by micro-CT of human lung specimens that structural differences are associated with unique SfSAD patterns, and demonstrated longitudinal feature alterations occurred with worsening pulmonary function independent of an increase in disease extent. These findings suggest that our technique captures additional COPD characteristics, which may provide important opportunities for improved diagnosis of COPD patients.

Management of COPD: Is there a role for quantitative imaging?

While the recent development of quantitative imaging methods have led to their increased use in the diagnosis and management of many chronic diseases, medical imaging still plays a limited role in the management of chronic obstructive pulmonary disease (COPD). In this review we highlight three pulmonary imaging modalities: computed tomography (CT), magnetic resonance imaging (MRI) and optical coherence tomography (OCT) imaging and the COPD biomarkers that may be helpful for managing COPD patients. We discussed the current role imaging plays in COPD management as well as the potential role quantitative imaging will play by identifying imaging phenotypes to enable more effective COPD management and improved outcomes.

Present and future utility of computed tomography scanning in the assessment and management of COPD

Computed tomography (CT) is the modality of choice for imaging the thorax and lung structure. In chronic obstructive pulmonary disease (COPD), it used to recognise the key morphological features of emphysema, bronchial wall thickening and gas trapping. Despite this, its place in the investigation and management of COPD is yet to be determined, and it is not routinely recommended. However, lung CT already has important clinical applications where it can be used to diagnose concomitant pathology and determine which patients with severe emphysema are appropriate for lung volume reduction procedures. Furthermore, novel quantitative analysis techniques permit objective measurements of pulmonary and extrapulmonary manifestations of the disease. These techniques can give important insights into COPD, and help explore the heterogeneity and underlying mechanisms of the condition. In time, it is hoped that these techniques can be used in clinical trials to help develop disease-specific therapy and, ultimately, as a clinical tool in identifying patients who would benefit most from new and existing treatments. This review discusses the current clinical applications for CT imaging in COPD and quantification techniques, and its potential future role in stratifying disease for optimal outcome.

A Pilot Study of Quantitative MRI Parametric Response Mapping of Bone Marrow Fat for Treatment Assessment in Myelofibrosis

Myelofibrosis (MF) is a hematologic neoplasm arising as a primary disease or secondary to other myeloproliferative neoplasms (MPNs). Both primary and secondary MF are uniquely associated with progressive bone marrow fibrosis, displacing normal hematopoietic cells from the marrow space and disrupting normal production of mature blood cells. Activation of the JAK2 signaling pathway in hematopoietic stem cells commonly causes MF, and ruxolitinib, a drug targeting this pathway, is the treatment of choice for many patients. However, current measures of disease status in MF do not necessarily predict response to treatment with ruxolitinib or other drugs in MF. Bone marrow biopsies are invasive and prone to sampling error, while measurements of spleen volume only indirectly reflect bone marrow status. Toward the goal of developing an imaging biomarker for treatment response in MF, we present preliminary results from a prospective clinical study evaluating parametric response mapping (PRM) of quantitative Dixon MRI bone marrow fat fraction maps in four MF patients treated with ruxolitinib. PRM allows for the voxel-wise identification of significant change in quantitative imaging readouts over time, in this case the bone marrow fat content. We identified heterogeneous response patterns of bone marrow fat among patients and within different bone marrow sites in the same patient. We also observed discordance between changes in bone marrow fat fraction and reductions in spleen volume, the standard imaging metric for treatment efficacy. This study provides initial support for PRM analysis of quantitative MRI of bone marrow fat to monitor response to therapy in MF, setting the stage for larger studies to further develop and validate this method as a complementary imaging biomarker for this disease.