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.

Pulmonary Functional Imaging: Part 2-State-of-the-Art Clinical Applications and Opportunities for Improved Patient Care.

Pulmonary functional imaging may be defined as the regional quantification of lung function by using primarily CT, MRI, and nuclear medicine techniques. The distribution of pulmonary physiologic parameters, including ventilation, perfusion, gas exchange, and biomechanics, can be noninvasively mapped and measured throughout the lungs. This information is not accessible by using conventional pulmonary function tests, which measure total lung function without viewing the regional distribution. The latter is important because of the heterogeneous distribution of virtually all lung disorders. Moreover, techniques such as hyperpolarized xenon 129 and helium 3 MRI can probe lung physiologic structure and microstructure at the level of the alveolar-air and alveolar-red blood cell interface, which is well beyond the spatial resolution of other clinical methods. The opportunities, challenges, and current stage of clinical deployment of pulmonary functional imaging are reviewed, including applications to chronic obstructive pulmonary disease, asthma, interstitial lung disease, pulmonary embolism, and pulmonary hypertension. Among the challenges to the deployment of pulmonary functional imaging in routine clinical practice are the need for further validation, establishment of normal values, standardization of imaging acquisition and analysis, and evidence of patient outcomes benefit. When these challenges are addressed, it is anticipated that pulmonary functional imaging will have an expanding role in the evaluation and management of patients with lung disease.

Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper.

Pulmonary MRI provides structural and quantitative functional images of the lungs without ionizing radiation, but it has had limited clinical use due to low signal intensity from the lung parenchyma. The lack of radiation makes pulmonary MRI an ideal modality for pediatric examinations, pregnant women, and patients requiring serial and longitudinal follow-up. Fortunately, recent MRI techniques, including ultrashort echo time and zero echo time, are expanding clinical opportunities for pulmonary MRI. With the use of multicoil parallel acquisitions and acceleration methods, these techniques make pulmonary MRI practical for evaluating lung parenchymal and pulmonary vascular diseases. The purpose of this Fleischner Society position paper is to familiarize radiologists and other interested clinicians with these advances in pulmonary MRI and to stratify the Society recommendations for the clinical use of pulmonary MRI into three categories: (a) suggested for current clinical use, (b) promising but requiring further validation or regulatory approval, and (c) appropriate for research investigations. This position paper also provides recommendations for vendors and infrastructure, identifies methods for hypothesis-driven research, and suggests opportunities for prospective, randomized multicenter trials to investigate and validate lung MRI methods.

Regional Fractional Ventilation by Using Multibreath Wash-in (3)He MR Imaging.

Purpose To assess the feasibility and optimize the accuracy of the multibreath wash-in hyperpolarized helium 3 ((3)He) approach to ventilation measurement by using magnetic resonance (MR) imaging as well as to examine the physiologic differences that this approach reveals among nonsmokers, asymptomatic smokers, and patients with chronic obstructive pulmonary disease (COPD). Materials and Methods All experiments were approved by the local institutional review board and compliant with HIPAA. Informed consent was obtained from all subjects. To measure fractional ventilation, the authors administered a series of identical normoxic hyperpolarized gas breaths to the subject; after each inspiration, an image was acquired during a short breath hold. Signal intensity buildup was fit to a recursive model that regionally solves for fractional ventilation. This measurement was successfully performed in nine subjects: three healthy nonsmokers (one man, two women; mean age, 45 years ± 4), three asymptomatic smokers (three men; mean age, 51 years ± 5), and three patients with COPD (three men; mean age, 59 years ± 5). Repeated measures analysis of variance was performed, followed by post hoc tests with Bonferroni correction, to assess the differences among the three cohorts. Results Whole-lung fractional ventilation as measured with hyperpolarized (3)He in all subjects (mean, 0.24 ± 0.06) showed a strong correlation with global fractional ventilation as measured with a gas delivery device (R(2) = 0.96, P < .001). Significant differences between the means of whole-lung fractional ventilation (F2,10 = 7.144, P = .012) and fractional ventilation heterogeneity (F2,10 = 7.639, P = .010) were detected among cohorts. In patients with COPD, the protocol revealed regions wherein fractional ventilation varied substantially over multiple breaths. Conclusion Multibreath wash-in hyperpolarized (3)He MR imaging of fractional ventilation is feasible in human subjects and demonstrates very good global (whole-lung) precision. Fractional ventilation measurement with this physiologically realistic approach reveals significant differences between patients with COPD and healthy subjects. To minimize error, several sources of potential bias must be corrected when calculating fractional ventilation. (©) RSNA, 2016 Online supplemental material is available for this article.

Oxygen-weighted Hyperpolarized (3)He MR Imaging: A Short-term Reproducibility Study in Human Subjects.

PURPOSE: To determine whether hyperpolarized helium 3 magnetic resonance (MR) imaging to measure alveolar partial pressure of oxygen (Pao2) shows sufficient test-retest repeatability and between-cohort differences to be used as a reliable technique for detection of alterations in gas exchange in asymptomatic smokers. MATERIALS AND METHODS: The protocol was approved by the local institutional review board and was HIPAA compliant. Informed consent was obtained from all subjects. Two sets of MR images were obtained 10 minutes apart in 25 subjects: 10 nonsmokers (five men, five women; mean ± standard deviation age, 50 years ± 6) and 15 smokers (seven women, eight men; mean age, 50 years ± 8). A mixed-effects model was developed to identify the regional repeatability of Pao2 measurements as an intraclass correlation coefficient. Ten smokers were matched with the 10 nonsmokers on the basis of signal-to-noise ratio (SNR). Three separate models were generated: one for nonsmokers, one for the SNR-matched smokers, and one for the five remaining smokers, who were imaged with a significantly higher SNR. RESULTS: Short-term back-to-back regional reproducibility was assessed by using intraclass correlation coefficients, which were 0.67 and 0.65 for SNR case-matched nonsmokers and smokers, respectively. Repeatability was a strong function of SNR; a 50% increase in SNR in the remaining smokers improved the intraclass correlation coefficient to 0.82. Although repeatability was not significantly different between the SNR-matched cohorts (P = .44), the smoker group showed higher spatial and temporal variability in Pao2. CONCLUSION: The short-term test-retest repeatability of hyperpolarized gas MR imaging of regional Pao2 was good. Asymptomatic smokers exhibited greater spatial and temporal variability in Pao2 than did the nonsmokers, which suggests that this parameter allows detection of small functional alterations associated with smoking.

Alterations of regional alveolar oxygen tension in asymptomatic current smokers: assessment with hyperpolarized (3)He MR imaging.

PURPOSE: To assess the ability of helium 3 ((3)He) magnetic resonance (MR) imaging of regional alveolar partial pressure of oxygen (Pao2) to depict smoking-induced functional alterations and to compare its efficacy to that of current diagnostic techniques. MATERIALS AND METHODS: This study was approved by the local institutional review board and was compliant with HIPAA. All subjects provided informed consent. A total of 43 subjects were separated into three groups: nonsmokers, asymptomatic smokers, and symptomatic smokers. All subjects underwent a Pao2 imaging session followed by clinically standard pulmonary function tests (PFTs), the 6-minute walk test, and St George Respiratory Questionnaire (SGRQ). The whole-lung mean and standard deviation of Pao2 were compared with metrics derived from PFTs, the 6-minute walk test, and the SGRQ. A logistic regression model was developed to identify the predictors of alterations to the lungs of asymptomatic smokers. RESULTS: The whole-lung standard deviation of Pao2 correlated with PFT metrics (forced expiratory volume in 1 second [FEV1]/forced vital capacity [FVC], Pearson r = -0.69, P < .001; percentage predicted FEV1, Pearson r = -0.67, P < .001; diffusing capacity of lung for carbon monoxide [Dlco], Pearson r = -0.45, P = .003), SGRQ score (Pearson r = 0.67, P < .001), and distance walked in 6 minutes (Pearson r = -0.47, P = .002). The standard deviation of Pao2 was significantly higher in asymptomatic smokers than in nonsmokers (change in the standard deviation of Pao2 = 7.59 mm Hg, P = .041) and lower when compared with symptomatic smokers (change in the standard deviation of Pao2 = 10.72 mm Hg, P = .001). A multivariate prediction model containing FEV1/FVC and the standard deviation of Pao2 (as significant predictors of subclinical changes in smokers) and Dlco (as a confounding variable) was formulated. This model resulted in an area under the receiver operating characteristic curve with a significant increase of 29.2% when compared with a prediction model based solely on nonimaging clinical tests. CONCLUSION: The (3)He MR imaging heterogeneity metric (standard deviation of Pao2) enabled the differentiation of all three study cohorts, which indicates that it can depict smoking-related functional alterations in asymptomatic current smokers.

Small-airway obstruction and emphysema in chronic obstructive pulmonary disease.

BACKGROUND: The major sites of obstruction in chronic obstructive pulmonary disease (COPD) are small airways (<2 mm in diameter). We wanted to determine whether there was a relationship between small-airway obstruction and emphysematous destruction in COPD. METHODS: We used multidetector computed tomography (CT) to compare the number of airways measuring 2.0 to 2.5 mm in 78 patients who had various stages of COPD, as judged by scoring on the Global Initiative for Chronic Obstructive Lung Disease (GOLD) scale, in isolated lungs removed from patients with COPD who underwent lung transplantation, and in donor (control) lungs. MicroCT was used to measure the extent of emphysema (mean linear intercept), the number of terminal bronchioles per milliliter of lung volume, and the minimum diameters and cross-sectional areas of terminal bronchioles. RESULTS: On multidetector CT, in samples from patients with COPD, as compared with control samples, the number of airways measuring 2.0 to 2.5 mm in diameter was reduced in patients with GOLD stage 1 disease (P=0.001), GOLD stage 2 disease (P=0.02), and GOLD stage 3 or 4 disease (P<0.001). MicroCT of isolated samples of lungs removed from patients with GOLD stage 4 disease showed a reduction of 81 to 99.7% in the total cross-sectional area of terminal bronchioles and a reduction of 72 to 89% in the number of terminal bronchioles (P<0.001). A comparison of the number of terminal bronchioles and dimensions at different levels of emphysematous destruction (i.e., an increasing value for the mean linear intercept) showed that the narrowing and loss of terminal bronchioles preceded emphysematous destruction in COPD (P<0.001). CONCLUSIONS: These results show that narrowing and disappearance of small conducting airways before the onset of emphysematous destruction can explain the increased peripheral airway resistance reported in COPD. (Funded by the National Heart, Lung, and Blood Institute and others.).

Reducing Errors Resulting From Commonly Missed Chest Radiography Findings.

Chest radiography (CXR), the most frequently performed imaging examination, is vulnerable to interpretation errors resulting from commonly missed findings. Methods to reduce these errors are presented. A practical approach using a systematic and comprehensive visual search strategy is described. The use of a checklist for quality control in the interpretation of CXR images is proposed to avoid overlooking commonly missed findings of clinical importance. Artificial intelligence is among the emerging and promising methods to enhance detection of CXR abnormalities. Despite their potential adverse consequences, errors offer opportunities for continued education and quality improvements in patient care, if managed within a just, supportive culture.

Commonly Missed Findings on Chest Radiographs: Causes and Consequences.

Chest radiography (CXR) continues to be the most frequently performed imaging examination worldwide, yet it remains prone to frequent errors in interpretation. These pose potential adverse consequences to patients and are a leading motivation for medical malpractice lawsuits. Commonly missed CXR findings and the principal causes of these errors are reviewed and illustrated. Perceptual errors are the predominant source of these missed findings. The medicolegal implications of such errors are explained. Awareness of commonly missed CXR findings, their causes, and their consequences are important in developing approaches to reduce and mitigate these errors.

Current role of imaging in the diagnosis and management of pulmonary hypertension.

OBJECTIVE: The purpose of this review is to describe classification schemes and imaging findings in the diagnosis and management of pulmonary hypertension. CONCLUSION: Pulmonary hypertension is a complex pathophysiologic condition in which several clinical entities increase pressure in the pulmonary circulation, progressively impairing cardiopulmonary function and, if untreated, causing right ventricular failure. Current classification schemes emphasize the necessity of an early, accurate etiologic diagnosis for a tailored therapeutic approach. Imaging plays an increasingly important role in the diagnosis and management of suspected pulmonary hypertension.

A combined pulmonary-radiology workshop for visual evaluation of COPD: study design, chest CT findings and concordance with quantitative evaluation.

UNLABELLED: The purposes of this study were: to describe chest CT findings in normal non-smoking controls and cigarette smokers with and without COPD; to compare the prevalence of CT abnormalities with severity of COPD; and to evaluate concordance between visual and quantitative chest CT (QCT) scoring. METHODS: Volumetric inspiratory and expiratory CT scans of 294 subjects, including normal non-smokers, smokers without COPD, and smokers with GOLD Stage I-IV COPD, were scored at a multi-reader workshop using a standardized worksheet. There were 58 observers (33 pulmonologists, 25 radiologists); each scan was scored by 9-11 observers. Interobserver agreement was calculated using kappa statistic. Median score of visual observations was compared with QCT measurements. RESULTS: Interobserver agreement was moderate for the presence or absence of emphysema and for the presence of panlobular emphysema; fair for the presence of centrilobular, paraseptal, and bullous emphysema subtypes and for the presence of bronchial wall thickening; and poor for gas trapping, centrilobular nodularity, mosaic attenuation, and bronchial dilation. Agreement was similar for radiologists and pulmonologists. The prevalence on CT readings of most abnormalities (e.g. emphysema, bronchial wall thickening, mosaic attenuation, expiratory gas trapping) increased significantly with greater COPD severity, while the prevalence of centrilobular nodularity decreased. Concordances between visual scoring and quantitative scoring of emphysema, gas trapping and airway wall thickening were 75%, 87% and 65%, respectively. CONCLUSIONS: Despite substantial inter-observer variation, visual assessment of chest CT scans in cigarette smokers provides information regarding lung disease severity; visual scoring may be complementary to quantitative evaluation.

Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19.

Purpose: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. Approach: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). Results: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ( AUC = 0.67 [0.58 to 0.76]; accuracy = 77 % ). Model II performed the best ( AUC = 0.78 [0.71 to 0.86]; accuracy = 81 % ). Model III was equivalent ( AUC = 0.75 [0.67 to 0.84]; accuracy = 80 % ). Both models II and III outperformed model I ( AUC difference = 0.11 [0.02 to 0.19], p = 0.01 ; AUC difference = 0.08 [0.01 to 0.15], p = 0.04 , respectively). Model II and III results did not change significantly when POv was replaced by POa. Conclusions: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.

Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. MATERIALS AND METHODS: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors. RESULTS: Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%-12.47%) and 0.77 (0.70-0.82) for average of expert readers and 9.56% to 9.78% (8.83%-10.22%) and 0.78 to 0.81 (0.73-0.85) for the CNN, respectively. CONCLUSIONS: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19.

Spectrum of pulmonary neuroendocrine cell proliferation: diffuse idiopathic pulmonary neuroendocrine cell hyperplasia, tumorlet, and carcinoids.

OBJECTIVE: The objectives of this article are to review the radiologic, pathologic, and clinical features of diffuse idiopathic pulmonary neuroendocrine cell hyperplasia, tumorlet, and carcinoids and to discuss the possible role of diffuse idiopathic pulmonary neuroendocrine cell hyperplasia and tumorlet in the development of carcinoids. CONCLUSION: Given the potential significant morbidity of diffuse idiopathic pulmonary neuroendocrine cell hyperplasia and its neoplastic counterparts, it is important to understand and recognize these disease entities. A conceptual continuum of these neuroendocrine entities is suggested.

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images. In radiology, deep learning has the opportunity to provide improved accuracy of image interpretation and diagnosis. Many groups are exploring the possibility of using deep learning-based applications to solve unmet clinical needs. In chest imaging, there has been a large effort to develop and apply computer-aided detection systems for the detection of lung nodules on chest radiographs and chest computed tomography. The essential limitation to computer-aided detection is an inability to learn from new information. To overcome these deficiencies, many groups have turned to deep learning approaches with promising results. In addition to nodule detection, interstitial lung disease recognition, lesion segmentation, diagnosis and patient outcomes have been addressed by deep learning approaches. The purpose of this review article was to cover the current state of the art for deep learning approaches and its limitations, and some of the potential impact on the field of radiology, with specific reference to chest imaging.

Early changes of lung function and structure in an elastase model of emphysema--a hyperpolarized 3He MRI study.

Early changes of lung function and structure were studied in the presence of an elastase-induced model of emphysema in 35 Sprague-Dawley rats at mild (5 U/100 g) and moderate (10 U/100 g) severities. Lung ventilation was measured on a regional basis (at a planar resolution of 3.2 mm) by hyperpolarized 3He MRI at 5 and 10 wk after model induction. Subsequent to imaging, average alveolar diameter was measured from histological slices taken from the centers of each lobe. Changes of mean fractional ventilation, mean linear intercept, and intrasubject heterogeneity of ventilation were studied during disease progression. Mean fractional ventilation was significantly different between healthy controls (0.23 +/- 0.04) and emphysematous animals at both time points in the 10-unit group (0.06 +/- 0.02 and 0.12 +/- 0.05, respectively). Changes in average alveolar diameter were not statistically observable until the 10th wk between healthy (37 +/- 10 microm) and emphysematous rats (73 +/- 25 and 95 +/- 31 microm, for 5 and 10 units, respectively). Assessment of function-structure correlation suggested that the majority of the decline in fractional ventilation occurred in the first 5 wk, while enlargement of alveolar diameters appeared primarily between the 5th and 10th wk. A thresholding metric, based on the 20th percentile of fractional ventilation over the entire lung, was utilized to detect the onset of the disease with confidence, independent of whether the regional ventilation measurements were normalized with respect to the delivered tidal volume and estimated functional residual capacity of each individual rat.

Application of an optical flow method to inspiratory and expiratory lung MDCT to assess regional air trapping: a feasibility study.

OBJECTIVE: We describe the application of an optical flow method to inspiratory and expiratory high-resolution volumetric lung MDCT for the assessment of regional air trapping. CONCLUSION: Qualitative and quantitative assessment of regional air trapping is feasible using an optical flow method to align volumetric MDCT data sets.