Quantification of pulmonary perfusion abnormalities using DCE-MRI in COPD: comparison with quantitative CT and pulmonary function

Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes

Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.

Magnetic Resonance Imaging of Lung Perfusion

"Lung perfusion" in the context of imaging conventionally refers to the delivery of blood to the pulmonary capillary bed through the pulmonary arteries originating from the right ventricle required for oxygenation. The most important physiological mechanism in the context of imaging is the so-called hypoxic pulmonary vasoconstriction (HPV, also known as "Euler-Liljestrand-Reflex"), which couples lung perfusion to lung ventilation. In obstructive airway diseases such as asthma, chronic-obstructive pulmonary disease (COPD), cystic fibrosis (CF), and asthma, HPV downregulates pulmonary perfusion in order to redistribute blood flow to functional lung areas in order to conserve optimal oxygenation. Imaging of lung perfusion can be seen as a reflection of lung ventilation in obstructive airway diseases. Other conditions that primarily affect lung perfusion are pulmonary vascular diseases, pulmonary hypertension, or (chronic) pulmonary embolism, which also lead to inhomogeneity in pulmonary capillary blood distribution. Several magnetic resonance imaging (MRI) techniques either dependent on exogenous contrast materials, exploiting periodical lung signal variations with cardiac action, or relying on intrinsic lung voxel attributes have been demonstrated to visualize lung perfusion. Additional post-processing may add temporal information and provide quantitative information related to blood flow. The most widely used and robust technique, dynamic-contrast enhanced MRI, is available in clinical routine assessment of COPD, CF, and pulmonary vascular disease. Non-contrast techniques are important research tools currently requiring clinical validation and cross-correlation in the absence of a viable standard of reference. First data on many of these techniques in the context of observational studies assessing therapy effects have just become available. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 5.

Echo time-dependent observed T1 and quantitative perfusion in chronic obstructive pulmonary disease using magnetic resonance imaging

Introduction

Due to hypoxic vasoconstriction, perfusion is interesting in the lungs. Magnetic Resonance Imaging (MRI) perfusion imaging based on Dynamic Contrast Enhancement (DCE) has been demonstrated in patients with Chronic Obstructive Pulmonary Diseases (COPD) using visual scores, and quantification methods were recently developed further. Inter-patient correlations of echo time-dependent observed T1 [T1(TE)] have been shown with perfusion scores, pulmonary function testing, and quantitative computed tomography. Here, we examined T1(TE) quantification and quantitative perfusion MRI together and investigated both inter-patient and local correlations between T1(TE) and quantitative perfusion.

Methods

22 patients (age 68.0 ± 6.2) with COPD were examined using morphological MRI, inversion recovery multi-echo 2D ultra-short TE (UTE) in 1-2 slices for T1(TE) mapping, and 4D Time-resolved angiography With Stochastic Trajectories (TWIST) for DCE. T1(TE) maps were calculated from 2D UTE at five TEs from 70 to 2,300 μs. Pulmonary Blood Flow (PBF) and perfusion defect (QDP) maps were produced from DCE measurements. Lungs were automatically segmented on UTE images and morphological MRI and these segmentations registered to DCE images. DCE images were separately registered to UTE in corresponding slices and divided into corresponding subdivisions. Spearman's correlation coefficients were calculated for inter-patient correlations using the entire segmented slices and for local correlations separately using registered images and subdivisions for each TE. Median T1(TE) in normal and defect areas according to QDP maps were compared.

Results

Inter-patient correlations were strongest on average at TE2 = 500 μs, reaching up to |ρ| = 0.64 for T1 with PBF and |ρ| = 0.76 with QDP. Generally, local correlations of T1 with PBF were weaker at TE2 than at TE1 or TE3 and with maximum values of |ρ| = 0.66 (from registration) and |ρ| = 0.69 (from subdivision). In 18 patients, T1 was shorter in defect areas than in normal areas, with the relative difference smallest at TE2.

Discussion

The inter-patient correlations of T1 with PBF and QDP found show similar strength and TE-dependence as those previously reported for visual perfusion scores and quantitative computed tomography. The local correlations and median T1 suggest that not only base T1 but also the TE-dependence of observed T1 in normal areas is closer to that found previously in healthy volunteers than in defect areas.

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.

[Magnetic resonance imaging of the lung : State of the art]

Due to the low proton density of the lung parenchyma and the rapid signal decay at the air-tissue interfaces, for a long time the lungs were difficult to access using magnetic resonance imaging (MRI); however, technical advances could address most of these obstacles. Pulmonary alterations associated with tissue proliferation ("plus pathologies"), can now be detected with high diagnostic accuracy because of the locally increased proton density. Compared to computed tomography (CT), MRI provides a comprehensive range of functional imaging procedures (respiratory mechanics, perfusion and ventilation). In addition, as a radiation-free noninvasive examination modality, it enables repeated examinations for assessment of the course or monitoring of the effects of treatment, even in children. This article discusses the technical aspects, gives suggestions for protocols and explains the role of MRI of the lungs in the routine assessment of various diseases.

Imaging human lung perfusion with contrast media: A meta-analysis

PURPOSE

To pool and summarise published data of pulmonary blood flow (PBF), pulmonary blood volume (PBV) and mean transit time (MTT) of the human lung, obtained with perfusion MRI or CT to provide reliable reference values of healthy lung tissue. In addition, the available data regarding diseased lung was investigated.

METHODS

PubMed was systematically searched to identify studies that quantified PBF/PBV/MTT in the human lung by injection of contrast agent, imaged by MRI or CT. Only data analysed by 'indicator dilution theory' were considered numerically. Weighted mean (wM), weighted standard deviation (wSD) and weighted coefficient of variance (wCoV) were obtained for healthy volunteers (HV), weighted according to the size of the datasets. Signal to concentration conversion method, breath holding method and presence of 'pre-bolus' were noted.

RESULTS

PBV was obtained from 313 measurements from 14 publications (wM: 13.97 ml/100 ml, wSD: 4.21 ml/100 ml, wCoV 0.30). MTT was obtained from 188 measurements from 10 publications (wM: 5.91 s, wSD: 1.84 s wCoV 0.31). PBF was obtained from 349 measurements from 14 publications (wM: 246.26 ml/100 ml ml/min, wSD: 93.13 ml/100 ml ml/min, wCoV 0.38). PBV and PBF were higher when the signal was normalised than when it was not. No significant differences were found for PBV and PBF between breathing states or between pre-bolus and no pre-bolus. Data for diseased lung were insufficient for meta-analysis.

CONCLUSION

Reference values for PBF, MTT and PBV were obtained in HV. The literature data are insufficient to draw strong conclusions regarding disease reference values.

MRI Compared with Low-Dose CT for Incidental Lung Nodule Detection in COPD: A Multicenter Trial

Purpose

To investigate morphofunctional chest MRI for the detection and management of incidental pulmonary nodules in participants with chronic obstructive pulmonary disease (COPD).

Materials and Methods

In this prospective study, 567 participants (mean age, 66 years ± 9 [SD]; 340 men) underwent same-day contrast-enhanced MRI and nonenhanced low-dose CT (LDCT) in a nationwide multicenter trial (clinicaltrials.gov: NCT01245933). Nodule dimensions, morphologic features, and Lung Imaging Reporting and Data System (Lung-RADS) category were assessed at MRI by two blinded radiologists, and consensual LDCT results served as the reference standard. Comparisons were performed using the Student t test, and agreements were assessed using the Cohen weighted κ.

Results

A total of 525 nodules larger than 3 mm in diameter were detected at LDCT in 178 participants, with a mean diameter of 7.2 mm ± 6.1 (range, 3.1-63.1 mm). Nodules were not detected in the remaining 389 participants. Sensitivity and positive predictive values with MRI for readers 1 and 2, respectively, were 63.0% and 84.8% and 60.2% and 83.9% for solid nodules (n = 495), 17.6% and 75.0% and 17.6% and 60.0% for part-solid nodules (n = 17), and 7.7% and 100% and 7.7% and 50.0% for ground-glass nodules (n = 13). For nodules 6 mm or greater in diameter, sensitivity and positive predictive values were 73.3% and 92.2% for reader 1 and 71.4% and 93.2% for reader 2, respectively. Readers underestimated the long-axis diameter at MRI by 0.5 mm ± 1.7 (reader 1) and 0.5 mm ± 1.5 (reader 2) compared with LDCT (P < .001). For Lung-RADS categorization per nodule using MRI, there was substantial to perfect interreader agreement (κ = 0.75-1.00) and intermethod agreement compared with LDCT (κ = 0.70-1.00 and 0.69-1.00).

Conclusion

In a multicenter setting, morphofunctional MRI showed moderate sensitivity for detection of incidental pulmonary nodules in participants with COPD but high agreement with LDCT for Lung-RADS classification of nodules.Clinical trial registration no. NCT01245933 and NCT02629432Keywords: MRI, CT, Thorax, Lung, Chronic Obstructive Pulmonary Disease, Screening© RSNA, 2023 Supplemental material is available for this article.

MR imaging of the airways

The need for airway imaging is defined by the limited sensitivity of common clinical tests like spirometry, lung diffusion (DLCO) and blood gas analysis to early changes of peripheral airways and to inhomogeneous regional distribution of lung function deficits. Therefore, X-ray and computed tomography (CT) are frequently used to complement the standard tests.As an alternative, magnetic resonance imaging (MRI) offers radiation-free lung imaging, but at lower spatial resolution. Non-contrast enhanced MRI shows healthy airways down to the first subsegmental level/4^th order (CT: eighth). Bronchiectasis can be identified by wall thickening and fluid accumulation. Smaller airways become visible, when altered by peribronchiolar inflammation or mucus retention (tree-in-bud sign).The strength of MRI is functional imaging. Dynamic, time-resolved MRI directly visualizes expiratory airway collapse down to the lobar level (CT: segmental level). Obstruction of even smaller airways becomes visible as air trapping on the expiratory scans. MRI with hyperpolarized noble gases (³He, ¹²⁹Xe) directly shows the large airways and peripheral lung ventilation. Dynamic contrast-enhanced MRI (DCE MRI) indirectly shows airway dysfunction as perfusion deficits resulting from hypoxic vasoconstriction of the dependent lung volumes. Further promising scientific approaches such as non-contrast enhanced, ventilation-/perfusion-weighted MRI from periodic signal changes of respiration and blood flow are in development.In summary, MRI of the lungs and airways excels with its unique combination of morphologic and functional imaging capacities for research (e.g., in chronic obstructive lung disease or asthma) as well as for clinical imaging (e.g., in cystic fibrosis).

[What have we learned from the German COPD cohort COSYCONET and where do we go from here?]

COSYCONET 1 is the only German COPD cohort which is large enough to be internationally comparable. The recruitment, which started in 2010 and ended in December 2013, comprised 2741 patients with the diagnosis of COPD who were subsequently investigated in regular follow-up visits. All visits included a comprehensive functional and clinical characterisation. On the basis of this detailed data set, it was possible to address a large number of clinical questions. These questions ranged from the prescription of medication, the detailed analysis of comorbidities, in particular cardiovascular disease, and biomarker assessment to radiological and health-economic aspects. Currently, more than 60 publications of COSYCONET data are internationally available. The present overview provides a description of all the results that were obtained, focussing on the relationship between different clinical and functional aspects as well as their potential practical consequences. In addition, information on the follow-up study COSYCONET 2 is given.

Lung perfusion disturbances in nonhospitalized post-COVID with dyspnea-A magnetic resonance imaging feasibility study

BACKGROUND

Dyspnea is common after COVID-19. Though the underlying mechanisms are largely unknown, lung perfusion abnormalities could contribute to lingering dyspnea.

OBJECTIVES

To detect pulmonary perfusion disturbances in nonhospitalized individuals with the post-COVID condition and persistent dyspnea 4-13 months after the disease onset.

METHODS

Individuals with dyspnea and matched healthy controls were recruited for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), a 6-min walk test, and an assessment of dyspnea. The DCE-MRI was quantified using two parametric values: mean time to peak (TTP) and TTP ratio, reflecting the total lung perfusion resistance and the fraction of lung with delayed perfusion, respectively.

RESULTS

Twenty-eight persons with persistent dyspnea (mean age 46.5 ± 8.0 years, 75% women) and 22 controls (mean age 44.1 ± 10.8 years, 73% women) were included. There was no systematic sex difference in dyspnea. The post-COVID group had no focal perfusion deficits but had higher mean pulmonary TTP (0.43 ± 0.04 vs. 0.41 ± 0.03, p = 0.011) and TTP ratio (0.096 ± 0.052 vs. 0.068 ± 0.027, p = 0.032). Post-COVID males had the highest mean TTP of 0.47 ± 0.02 and TTP ratio of 0.160 ± 0.039 compared to male controls and post-COVID females (p = 0.001 and p < 0.001, respectively). Correlations between dyspnea and perfusion parameters were demonstrated in males (r = 0.83, p < 0.001 for mean TTP; r = 0.76, p = 0.003 for TTP ratio), but not in females.

CONCLUSIONS

DCE-MRI demonstrated late contrast bolus arrival in males with post-COVID dyspnea, suggestive of primary vascular lesions or secondary effects of hypoxic vasoconstriction. Since this effect was not regularly observed in female patients, our findings suggest sex differences in the mechanisms underlying post-COVID dyspnea, which warrants further investigation in dedicated trials.

Unsupervised clustering algorithms improve the reproducibility of dynamic contrast-enhanced magnetic resonance imaging pulmonary perfusion quantification in muco-obstructive lung diseases

Background

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows the assessment of pulmonary perfusion, which may play a key role in the development of muco-obstructive lung disease. One problem with quantifying pulmonary perfusion is the high variability of metrics. Quantifying the extent of abnormalities using unsupervised clustering algorithms in residue function maps leads to intrinsic normalization and could reduce variability.

Purpose

We investigated the reproducibility of perfusion defects in percent (QDP) in clinically stable patients with cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD).

Methods

15 CF (29.3 ± 9.3y, FEV1%predicted = 66.6 ± 15.8%) and 20 COPD (66.5 ± 8.9y, FEV1%predicted = 42.0 ± 13.3%) patients underwent DCE-MRI twice 1 month apart. QDP, pulmonary blood flow (PBF), and pulmonary blood volume (PBV) were computed from residue function maps using an in-house quantification pipeline. A previously validated MRI perfusion score was visually assessed by an expert reader.

Results

Overall, mean QDP, PBF, and PBV did not change within 1 month, except for QDP in COPD (p < 0.05). We observed smaller limits of agreement (± 1.96 SD) related to the median for QDP (CF: ± 38%, COPD: ± 37%) compared to PBF (CF: ± 89%, COPD: ± 55%) and PBV (CF: ± 55%, COPD: ± 51%). QDP correlated moderately with the MRI perfusion score in CF (r = 0.46, p < 0.05) and COPD (r = 0.66, p < 0.001). PBF and PBV correlated poorly with the MRI perfusion score in CF (r =−0.29, p = 0.132 and r =−0.35, p = 0.067, respectively) and moderately in COPD (r =−0.57 and r =−0.57, p < 0.001, respectively).

Conclusion

In patients with muco-obstructive lung diseases, QDP was more robust and showed a higher correlation with the MRI perfusion score compared to the traditionally used perfusion metrics PBF and PBV.

Semiautomatic assessment of respiratory dynamics using cine MRI in chronic obstructive pulmonary disease

Purpose

The quantitative assessment of impaired lung motions and their association with the clinical characteristics of COPD patients is challenging. The aim of this study was to measure respiratory kinetics, including asynchronous movements, and to analyze the relationship between lung area and other clinical parameters.

Materials and methods

This study enrolled 10 normal control participants and 21 COPD patients who underwent dynamic MRI and pulmonary function testing (PFT). The imaging program was implemented using MATLAB®. Each lung area was detected semi-automatically on a coronal image (imaging level at the aortic valve) from the inspiratory phase to the expiratory phase. The Dice index of the manual measurements was calculated, with the relationship between lung area ratio and other clinical parameters, including PFTs then evaluated. The asynchronous movements of the diaphragm were also evaluated using a sagittal image.

Results

The Dice index for the lung region using the manual and semi-automatic extraction methods was high (Dice index = 0.97 ± 0.03). A significant correlation was observed between the time corrected lung area ratio and percentage of forced expiratory volume in 1 s (FEV₁%pred) and residual volume percentage (RV%pred) (r = −0.54, p = 0.01, r = 0.50, p = 0.03, respectively). The correlation coefficient between each point of the diaphragm in the group with visible see-saw like movements was significantly lower than that in the group without see-saw like movements (value = −0.36 vs 0.95, p = 0.001).

Conclusion

Semi-automated extraction of lung area from Cine MRI might be useful for detecting impaired respiratory kinetics in patients with COPD.