Multicenter Standardization of Phase-Resolved Functional Lung MRI in Patients With Suspected Chronic Thromboembolic Pulmonary Hypertension

BACKGROUND Detection of pulmonary perfusion defects is the recommended approach for diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This is currently achieved in a clinical setting using scintigraphy. Phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) is an alternative technique for evaluating regional ventilation and perfusion without the use of ionizing radiation or contrast media. PURPOSE To assess the feasibility and image quality of PREFUL-MRI in a multicenter setting in suspected CTEPH. STUDY TYPE This is a prospective cohort sub-study. POPULATION Forty-five patients (64 ± 16 years old) with suspected CTEPH from nine study centers. FIELD STRENGTH/SEQUENCE 1.5 T and 3 T/2D spoiled gradient echo/bSSFP/T2 HASTE/3D MR angiography (TWIST). ASSESSMENT Lung signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between study centers with different MRI machines. The contrast between normally and poorly perfused lung areas was examined on PREFUL images. The perfusion defect percentage calculated using PREFUL-MRI (QDP$_{PREFUL}$ ) was compared to QDP from the established dynamic contrast-enhanced MRI technique (QDP$_{DCE}$ ). Furthermore, QDP$_{PREFUL}$ was compared between a patient subgroup with confirmed CTEPH or chronic thromboembolic disease (CTED) to other clinical subgroups. STATISTICAL TESTS t-Test, one-way analysis of variance (ANOVA), Pearson's correlation. Significance level was 5%. RESULTS Significant differences in lung SNR and CNR were present between study centers. However, PREFUL perfusion images showed a significant contrast between normally and poorly perfused lung areas (mean delta of normalized perfusion -4.2% SD 3.3) with no differences between study sites (ANOVA: P = 0.065). QDP$_{PREFUL}$ was significantly correlated with QDP$_{DCE}$ (r = 0.66), and was significantly higher in 18 patients with confirmed CTEPH or CTED (57.9 ± 12.2%) compared to subgroups with other causes of PH or with excluded PH (in total 27 patients with mean ± SD QDP$_{PREFUL}$  = 33.9 ± 17.2%). DATA CONCLUSION PREFUL-MRI could be considered as a non-invasive method for imaging regional lung perfusion in multicenter studies. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 1

Pulmonary Image Segmentation and Registration Algorithms: Towards Regional Evaluation of Obstructive Lung Disease

Pulmonary imaging, including pulmonary magnetic resonance imaging (MRI) and computed tomography (CT), provides a way to sensitively and regionally measure spatially heterogeneous lung structural-functional abnormalities. These unique imaging biomarkers offer the potential for better understanding pulmonary disease mechanisms, monitoring disease progression and response to therapy, and developing novel treatments for improved patient care. To generate these regional lung structure-function measurements and enable broad clinical applications of quantitative pulmonary MRI and CT biomarkers, as a first step, accurate, reproducible and rapid lung segmentation and registration methods are required. In this regard, we first developed a 1H MRI lung segmentation algorithm that employs complementary hyperpolarized 3He MRI functional information for improved lung segmentation. The 1H-3He MRI joint segmentation algorithm was formulated as a coupled continuous min-cut model and solved through convex relaxation, for which a dual coupled continuous max-flow model was proposed and a max-flow-based efficient numerical solver was developed. Experimental results on a clinical dataset of 25 chronic obstructive pulmonary disease (COPD) patients ranging in disease severity demonstrated that the algorithm provided rapid lung segmentation with high accuracy, reproducibility and diminished user interaction. We then developed a general 1H MRI left-right lung segmentation approach by exploring the left-to-right lung volume proportion prior. The challenging volume proportion-constrained multi-region segmentation problem was approximated through convex relaxation and equivalently represented by a max-flow model with bounded flow conservation conditions. This gave rise to a multiplier-based high performance numerical implementation based on convex optimization theories. In 20 patients with mild- to-moderate and severe asthma, the approach demonstrated high agreement with manual segmentation, excellent reproducibility and computational efficiency. Finally, we developed a CT-3He MRI deformable registration approach that coupled the complementary CT-1H MRI registration. The joint registration problem was solved by exploring optical-flow techniques, primal-dual analyses and convex optimization theories. In a diverse group of patients with asthma and COPD, the registration approach demonstrated lower target registration error than single registration and provided fast regional lung structure-function measurements that were strongly correlated with a reference method. Collectively, these lung segmentation and registration algorithms demonstrated accuracy, reproducibility and workflow efficiency that all may be clinically-acceptable. All of this is consistent with the need for broad and large-scale clinical applications of pulmonary MRI and CT

White matter lesions characterise brain involvement in moderate to severe chronic obstructive pulmonary disease, but cerebral atrophy does not.

BACKGROUND: Brain pathology is relatively unexplored in chronic obstructive pulmonary disease (COPD). This study is a comprehensive investigation of grey matter (GM) and white matter (WM) changes and how these relate to disease severity and cognitive function. METHODS: T1-weighted and fluid-attenuated inversion recovery images were acquired for 31 stable COPD patients (FEV1 52.1% pred., PaO2 10.1 kPa) and 24 age, gender-matched controls. T1-weighted images were segmented into GM, WM and cerebrospinal fluid (CSF) tissue classes using a semi-automated procedure optimised for use with this cohort. This procedure allows, cohort-specific anatomical features to be captured, white matter lesions (WMLs) to be identified and includes a tissue repair step to correct for misclassification caused by WMLs. Tissue volumes and cortical thickness were calculated from the resulting segmentations. Additionally, a fully-automated pipeline was used to calculate localised cortical surface and gyrification. WM and GM tissue volumes, the tissue volume ratio (indicator of atrophy), average cortical thickness, and the number, size, and volume of white matter lesions (WMLs) were analysed across the whole-brain and regionally - for each anatomical lobe and the deep-GM. The hippocampus was investigated as a region-of-interest. Localised (voxel-wise and vertex-wise) variations in cortical gyrification, GM density and cortical thickness, were also investigated. Statistical models controlling for age and gender were used to test for between-group differences and within-group correlations. Robust statistical approaches ensured the family-wise error rate was controlled in regional and local analyses. RESULTS: There were no significant differences in global, regional, or local measures of GM between patients and controls, however, patients had an increased volume (p = 0.02) and size (p = 0.04) of WMLs. In patients, greater normalised hippocampal volume positively correlated with exacerbation frequency (p = 0.04), and greater WML volume was associated with worse episodic memory (p = 0.05). A negative relationship between WML and FEV1 % pred. approached significance (p = 0.06). CONCLUSIONS: There was no evidence of cerebral atrophy within this cohort of stable COPD patients, with moderate airflow obstruction. However, there were indications of WM damage consistent with an ischaemic pathology. It cannot be concluded whether this represents a specific COPD, or smoking-related, effect

Quantitative Evaluation of Pulmonary Emphysema Using Magnetic Resonance Imaging and x-ray Computed Tomography

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality affecting at least 600 million people worldwide. The most widely used clinical measurements of lung function such as spirometry and plethysmography are generally accepted for diagnosis and monitoring of the disease. However, these tests provide only global measures of lung function and they are insensitive to early disease changes. Imaging tools that are currently available have the potential to provide regional information about lung structure and function but at present are mainly used for qualitative assessment of disease and disease progression. In this thesis, we focused on the application of quantitative measurements of lung structure derived from 1H magnetic resonance imaging (MRI) and high resolution computed tomography (CT) in subjects diagnosed with COPD by a physician. Our results showed that significant and moderately strong relationship exists between 1H signal intensity (SI) and 3He apparent diffusion coefficient (ADC), as well as between 1H SI and CT measurements of emphysema. This suggests that these imaging methods may be quantifying the same tissue changes in COPD, and that pulmonary 1H SI may be used effectively to monitor emphysema as a complement to CT and noble gas MRI. Additionally, our results showed that objective multi-threshold analysis of CT images for emphysema scoring that takes into account the frequency distribution of each Hounsfield unit (HU) threshold was effective in correctly classifying the patient into COPD and healthy subgroups. Finally, we found a significant correlation between whole lung average subjective and objective emphysema scores with high inter-observer agreement. It is concluded that 1H MRI and high resolution CT can be used to quantitatively evaluate lung tissue alterations in COPD subjects

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

PURPOSE To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI. METHODS Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. RESULTS Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. CONCLUSION Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses

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

Towards quantitative perfusion MRI of the lung in COPD: The problem of short-term repeatability

Purpose 4D perfusion magnetic resonance imaging (MRI) with intravenous injection of contrast agent allows for a radiation-free assessment of regional lung function. It is therefore a valuable method to monitor response to treatment in patients with chronic obstructive pulmonary disease (COPD). This study was designed to evaluate its potential for monitoring short-term response to hyperoxia in COPD patients. Materials and methods 19 prospectively enrolled COPD patients (median age 66y) underwent paired dynamic contrast-enhanced 4D perfusion MRI within 35min, first breathing 100% oxygen (injection 1, O-2) and then room air (injection 2, RA), which was repeated on two consecutive days (day 1 and 2). Post-processing software was employed to calculate mean transit time (MTT), pulmonary blood volume (PBV) and pulmonary blood flow (PBF), based on the indicator dilution theory, for the automatically segmented whole lung and 12 regions of equal volume. Results Comparing O-2 with RA conditions, PBF and PBV were found to be significantly lower at O-2, consistently on both days (p<10-8). Comparing day 2 to day 1, MTT was shorter by 0.59 +/- 0.63 s (p<10-8), PBF was higher by 22 +/- 80 ml/min/100ml (p<3.10-4), and PBV tended to be lower by 0.2 +/- 7.2 ml/100ml (p = 0.159) at both, RA and O-2, conditions. Conclusion The second injection (RA) yielded higher PBF and PBV, which apparently contradicts the established hypothesis that hyperoxia increases lung perfusion. Quantification of 4D perfusion MRI by current software approaches may thus be limited by residual circulating contrast agent in the short-term and even the next day

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets.

PURPOSE To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI. METHODS Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. RESULTS Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. CONCLUSION Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses