Functional Lung MRI in Chronic Obstructive Pulmonary Disease: Comparison of T1 Mapping, Oxygen-Enhanced T1 Mapping and Dynamic Contrast Enhanced Perfusion

Purpose Monitoring of regional lung function in interventional COPD trials requires alternative end-points beyond global parameters such as FEV1. T1 relaxation times of the lung might allow to draw conclusions on tissue composition, blood volume and oxygen fraction. The aim of this study was to evaluate the potential value of lung Magnetic resonance imaging (MRI) with native and oxygen-enhanced T1 mapping for the assessment of COPD patients in comparison with contrast enhanced perfusion MRI. Materials and Methods 20 COPD patients (GOLD I-IV) underwent a coronal 2-dimensional inversion recovery snapshot flash sequence (8 slices/lung) at room air and during inhalation of pure oxygen, as well as dynamic contrast-enhanced first-pass perfusion imaging. Regional distribution of T1 at room air (T1), oxygen-induced T1 shortening (Delta T1) and peak enhancement were rated by 2 chest radiologists in consensus using a semi-quantitative 3-point scale in a zone-based approach. Results Abnormal T1 and Delta T1 were highly prevalent in the patient cohort. T1 and Delta T1 correlated positively with perfusion abnormalities (r = 0.81 and r = 0.80;p&0.001), and with each other (r = 0.80;p< 0.001). In GOLD stages I and II Delta T1 was normal in 16/29 lung zones with mildly abnormal perfusion (15/16 with abnormal T1). The extent of T1 (r = 0.45;p< 0.05), T1 (r = 0.52;p< 0.05) and perfusion abnormalities (r = 0.52;p< 0.05) showed a moderate correlation with GOLD stage. Conclusion Native and oxygen-enhanced T1 mapping correlated with lung perfusion deficits and severity of COPD. Under the assumption that T1 at room air correlates with the regional pulmonary blood pool and that oxygen-enhanced T1 reflects lung ventilation, both techniques in combination are principally suitable to characterize ventilation-perfusion imbalance. This appears valuable for the assessment of regional lung characteristics in COPD trials without administration of i. v. contrast

Reproducibility of pulmonary magnetic resonance angiography in adults with muco-obstructive pulmonary disease

Background Recent studies support magnetic resonance angiography (MRA) as a diagnostic tool for pulmonary arterial disease. Purpose To determine MRA image quality and reproducibility, and the dependence of MRA image quality and reproducibility on disease severity in patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). Material and Methods Twenty patients with COPD (mean age 66.5 ± 8.9 years; FEV1% = 42.0 ± 13.3%) and 15 with CF (mean age 29.3 ± 9.3 years; FEV1% = 66.6 ± 15.8%) underwent morpho-functional chest magnetic resonance imaging (MRI) including time-resolved MRA twice one month apart (MRI1, MRI2), and COPD patients underwent non-contrast computed tomography (CT). Image quality was assessed visually using standardized subjective 5-point scales. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were measured by regions of interest. Disease severity was determined by spirometry, a well-evaluated chest MRI score, and by computational CT emphysema index (EI) for COPD. Results Subjective image quality was diagnostic for all MRA at MRI1 and MRI2 (mean score = 4.7 ± 0.6). CNR and SNR were 4 43.8 ± 8.7 and 50.5 ± 8.7, respectively. Neither image quality score nor CNR or SNR correlated with FEV1% or chest MRI score for COPD and CF (r = 0.239–0.248). CNR and SNR did not change from MRI1 to MRI2 (P = 0.434–0.995). Further, insignificant differences in CNR and SNR between MRA at MRI1 and MRI2 did not correlate with FEV1% nor chest MRI score in COPD and CF (r = −0.238–0.183), nor with EI in COPD (r = 0.100–0.111). Conclusion MRA achieved diagnostic quality in COPD and CF patients and was highly reproducible irrespective of disease severity. This supports MRA as a robust alternative to CT in patients with underlying muco-obstructive lung disease

Qualitative and quantitative evaluation of computed tomography changes in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor: a retrospective observational study

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor–tezacaftor–ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response.Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI).Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p &lt; 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p &lt; 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p &lt; 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p &lt; 0.001), and MLD decreased (p &lt; 0.001) through a reduction of ground glass opacity areas (p &lt; 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p &lt; 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = −0.606, p = 0.003) and with WT (r = −0.538, p = 0.010).Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.

OBJECTIVES To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS A total of 251 subjects (median [IQR] age, 65 (57-73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (-766 vs. -790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62-0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules

High resolution propagation-based lung imaging at clinically relevant X-ray dose levels

Absorption-based clinical computed tomography (CT) is the current imaging method of choice in the diagnosis of lung diseases. Many pulmonary diseases are affecting microscopic structures of the lung, such as terminal bronchi, alveolar spaces, sublobular blood vessels or the pulmonary interstitial tissue. As spatial resolution in CT is limited by the clinically acceptable applied X-ray dose, a comprehensive diagnosis of conditions such as interstitial lung disease, idiopathic pulmonary fibrosis or the characterization of small pulmonary nodules is limited and may require additional validation by invasive lung biopsies. Propagation-based imaging (PBI) is a phase sensitive X-ray imaging technique capable of reaching high spatial resolutions at relatively low applied radiation dose levels. In this publication, we present technical refinements of PBI for the characterization of different artificial lung pathologies, mimicking clinically relevant patterns in ventilated fresh porcine lungs in a human-scale chest phantom. The combination of a very large propagation distance of 10.7 m and a photon counting detector with [Formula: see text] pixel size enabled high resolution PBI CT with significantly improved dose efficiency, measured by thermoluminescence detectors. Image quality was directly compared with state-of-the-art clinical CT. PBI with increased propagation distance was found to provide improved image quality at the same or even lower X-ray dose levels than clinical CT. By combining PBI with iodine k-edge subtraction imaging we further demonstrate that, the high quality of the calculated iodine concentration maps might be a potential tool for the analysis of lung perfusion in great detail. Our results indicate PBI to be of great value for accurate diagnosis of lung disease in patients as it allows to depict pathological lesions non-invasively at high resolution in 3D. This will especially benefit patients at high risk of complications from invasive lung biopsies such as in the setting of suspected idiopathic pulmonary fibrosis (IPF)

Effects of Lumacaftor/Ivacaftor on Cystic Fibrosis Disease Progression in Children 2 through 5 Years of Age Homozygous for F508del-CFTR: A Phase 2 Placebo-controlled Clinical Trial.

RATIONALE Lumacaftor/ivacaftor (LUM/IVA) was shown to be safe and well tolerated in children 2 through 5 years of age with cystic fibrosis (CF) homozygous for F508del-CFTR in a phase 3 open-label study. Improvements in sweat chloride concentration, markers of pancreatic function, and lung clearance index2.5 (LCI2.5), along with increases in growth parameters, suggested the potential for early disease modification with LUM/IVA treatment. OBJECTIVE To further assess the effects of LUM/IVA on CF disease progression in children 2 through 5 years of age using chest magnetic resonance imaging (MRI). METHODS This phase 2 study had two parts: a 48-week, randomized, double-blind, placebo-controlled treatment period in which children 2 through 5 years of age with CF homozygous for F508del-CFTR received either LUM/IVA or placebo (Part 1) followed by an open-label period in which all children received LUM/IVA for an additional 48 weeks (Part 2). We report results from Part 1. The primary endpoint was absolute change from baseline in chest MRI global score at Week 48. Secondary endpoints included absolute change in LCI2.5 through Week 48 and absolute changes in weight-for-age, stature-for-age, and body mass index-for-age z-scores at Week 48. Additional endpoints included absolute changes in sweat chloride concentration, fecal elastase-1 levels, serum immunoreactive trypsinogen, and fecal calprotectin through Week 48. The primary endpoint was analyzed using Bayesian methods, where the actual Bayesian posterior probability of LUM/IVA being superior to placebo in the MRI global chest score at Week 48 was calculated using a vague normal prior distribution; secondary and additional endpoints were analyzed using descriptive summary statistics. RESULTS Fifty-one children were enrolled and received LUM/IVA (n=35) or placebo (n=16). For the change in MRI global chest score at Week 48, the Bayesian posterior probability of LUM/IVA being better than placebo (treatment difference <0; higher score indicating greater abnormality) was 76%; the mean treatment difference was -1.5 (95% credible interval, -5.5 to 2.6). Treatment with LUM/IVA also led to within-group numerical improvements in LCI2.5, growth parameters, and biomarkers of pancreatic function as well as greater decreases in sweat chloride concentration compared with placebo from baseline through Week 48. Safety data were consistent with the established safety profile of LUM/IVA. CONCLUSIONS This placebo-controlled study suggests the potential for early disease modification with LUM/IVA treatment, including that assessed by chest MRI, in children as young as 2 years. Clinical trial registered with ClinicalTrials.gov (NCT03625466)

Reproducibility and comparison of oxygen-enhanced T-1 quantification in COPD and asthma patients

T1 maps have been shown to yield useful diagnostic information on lung function in patients with chronic obstructive pulmonary disease (COPD) and asthma, both for native T1 and Delta T1, the relative reduction while breathing pure oxygen. As parameter quantification is particularly interesting for longitudinal studies, the purpose of this work was both to examine the reproducibility of lung T1 mapping and to compare T1 found in COPD and asthma patients using IRSnapShotFLASH embedded in a full MRI protocol. 12 asthma and 12 COPD patients (site 1) and further 15 COPD patients (site 2) were examined on two consecutive days. In each patient, T1 maps were acquired in 8 single breath-hold slices, breathing first room air, then pure oxygen. Maps were partitioned into 12 regions each to calculate average values. In asthma patients, the average T-1,T-RA = 1206ms (room air) was reduced to T-1,T-O2 = 1141ms under oxygen conditions (Delta T1 = 5.3%, p < 5.10(-4)), while in COPD patients both native T-1,T-RA = 1125ms was significantly shorter (p < 10(-3)) and the relative reduction to T-1,T-O2 = 1081ms on average Delta T1 = 4.2%(p < 10(-5)). On the second day, with T-1,T-RA = 1186ms in asthma and T-1,T-RA = 1097ms in COPD, observed values were slightly shorter on average in all patient groups. Delta T1 reduction was the least repeatable parameter and varied from day to day by up to 23% in individual asthma and 30% in COPD patients. While for both patient groups T1 was below the values reported for healthy subjects, the T1 and Delta T1 found in asthmatics lies between that of the COPD group and reported values for healthy subjects, suggesting a higher blood volume fraction and better ventilation. However, it could be demonstrated that lung T1 quantification is subject to notable inter-examination variability, which here can be attributed both to remaining contrast agent from the previous day and the increased dependency of lung T1 on perfusion and thus current lung state

FAIR Development of Data-integrated AI to Detect Breathing Motion in Dynamic Lung MRI

The FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles describe requirements for sustainable research data. To implement the FAIR principles for artificial intelligence (AI) methods, it is necessary to record the connections between raw data, model architecture, hyperparameters, and the results so that this knowledge can be later used to track the thought process and to enable comparative and transfer studies. We propose and implemented a concept that automatizes research data management for artificial intelligence, minimizing the overhead for the individual researcher. An AI project, which had the goal to develop a neural network for automatically detecting breathing motion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images of the lung, was managed using the proposed concept as a proof of concept. Three different model architectures, a regular Convolutional Neural Network (CNN), a two-branch CNN and a hybrid model consisting of a time-distributed CNN followed by a Long short-term Memory (LSTM) network were trained and compared. As a result of using the proposed concept we were able to record rich metadata and links between entities and automatically generate a knowledge graph for data provenance of the AI work packages of this project

FAIR Development of Data-integrated AI to Detect Breathing Motion in Dynamic Lung MRI

The FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles describe requirements for sustainable research data. To implement the FAIR principles for artificial intelligence (AI) methods, it is necessary to record the connections between raw data, model architecture, hyperparameters, and the results so that this knowledge can be later used to track the thought process and to enable comparative and transfer studies. We propose and implemented a concept that automatizes research data management for artificial intelligence, minimizing the overhead for the individual researcher. An AI project, which had the goal to develop a neural network for automatically detecting breathing motion in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images of the lung, was managed using the proposed concept as a proof of concept. Three different model architectures, a regular Convolutional Neural Network (CNN), a two-branch CNN and a hybrid model consisting of a time-distributed CNN followed by a Long short-term Memory (LSTM) network were trained and compared. As a result of using the proposed concept we were able to record rich metadata and links between entities and automatically generate a knowledge graph for data provenance of the AI work packages of this project