Chronic Obstructive Pulmonary Disease: Lobar Analysis with Hyperpolarized 129Xe MR Imaging.

Purpose To compare lobar ventilation and apparent diffusion coefficient (ADC) values obtained with hyperpolarized xenon 129 ((129)Xe) magnetic resonance (MR) imaging to quantitative computed tomography (CT) metrics on a lobar basis and pulmonary function test (PFT) results on a whole-lung basis in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods The study was approved by the National Research Ethics Service Committee; written informed consent was obtained from all patients. Twenty-two patients with COPD (Global Initiative for Chronic Obstructive Lung Disease stage II-IV) underwent hyperpolarized (129)Xe MR imaging at 1.5 T, quantitative CT, and PFTs. Whole-lung and lobar (129)Xe MR imaging parameters were obtained by using automated segmentation of multisection hyperpolarized (129)Xe MR ventilation images and hyperpolarized (129)Xe MR diffusion-weighted images after coregistration to CT scans. Whole-lung and lobar quantitative CT-derived metrics for emphysema and bronchial wall thickness were calculated. Pearson correlation coefficients were used to evaluate the relationship between imaging measures and PFT results. Results Percentage ventilated volume and average ADC at lobar (129)Xe MR imaging showed correlation with percentage emphysema at lobar quantitative CT (r = -0.32, P < .001 and r = 0.75, P < .0001, respectively). The average ADC at whole-lung (129)Xe MR imaging showed moderate correlation with PFT results (percentage predicted transfer factor of the lung for carbon monoxide [Tlco]: r = -0.61, P < .005) and percentage predicted functional residual capacity (r = 0.47, P < .05). Whole-lung quantitative CT percentage emphysema also showed statistically significant correlation with percentage predicted Tlco (r = -0.65, P < .005). Conclusion Lobar ventilation and ADC values obtained from hyperpolarized (129)Xe MR imaging demonstrated correlation with quantitative CT percentage emphysema on a lobar basis and with PFT results on a whole-lung basis. (©) RSNA, 2016

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) inhale

The application of advanced imaging techniques for the assessment of paediatric chest disease

Introduction – Cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) both result in chronic suppurative lung disease with significant resulting morbidity and early mortality. Many clinical and academic groups advocate biennial or even annual CT surveillance from as early as 2 years of age, but new therapies and increasing life expectancy lead to concerns over the use of repeated CT imaging. There are many recent studies showing promise of MRI for structural lung imaging MRI based measures of lung function. Both CF and PCD result in multisystem disease and whilst much of the morbidity results from lung disease, monitoring of extrathoracic disease is likely also relevant. Aims and objectives – 1) To set up a clinically feasible, multisystem (lung, sinonasal and upper abdominal visceral) quantitative MRI examination for the investigation and follow up of CSLD 2) To evaluate novel imaging biomarkers of CF and PCD disease severity Hypotheses – 1) Combined structural and quantitative MRI assessment of the thorax can provide comparable information to CT such that follow up imaging via CT could be replaced with MRI. 2) Quantitative MR measures of ventilation correlate with established clinical measures of ventilation (LCI and FEV1) and provide additional spatial information. 3) A multisystem MRI assessment can provide new extra-thoracic imaging biomarkers of CF and PCD disease severity whilst being better tolerated by patients than current multimodality imaging follow up. Methods – People with CF or PCD referred for clinically indicated lung CT were prospectively recruited to undergo MR imaging of the lungs, liver and paranasal sinuses. Structural lung imaging was optimised for speed of acquisition using T2 BLADE imaging, in axial and coronal plane, during breath holds rather than more conventional respiratory triggering. Images were scored by two observers using the Eichinger scoring system and compared to CT structural scores using the CFCT scoring system. Lung T1 mapping was performed via free breathing IR-HASTE and T1 and T2 mapping performed via breath hold ufbSSFP imaging. Functional lung imaging was performed via pre and post hyperoxygenation ufbSSFP T1 mapping, free breathing dynamic oxygen enhanced IR-HASTE imaging (OE-MRI) and non-contrast ufbSSFP-based matrix pencil decomposition imaging of ventilation and pulmonary perfusion. Lung T1 maps included the superior portion of the liver enabling simultaneous liver T1 mapping. A multiparametric paranasal sinus protocol was devised containing structural (T1 and T2 TSE), susceptibility and diffusion weighted sequences for the calculation of sinus volume, mucus volume and mucosal volume, presence or absence of artefact associated with infective micro-organisms and calculation of mucus and mucosal diffusion. Participant tolerability of MR imaging assessed via a bespoke questionnaire, completed before and after both CT and MR imaging. Multiple breath wash-out testing was performed on the day of the MRI and spirometry, antibiotic usage, abdominal ultrasound and sheer wave elastography collected retrospectively from the electronic patient record. Results – 22 participants were recruited, all of whom completed the hour-long MRI protocol. The median age was 14 years (range 6 – 35). 2-plane structural lung imaging was acquired in a total of 2 minutes 4 seconds with only a single participant reporting difficulties with the required breath holds. Interclass Correlation Coefficients of interobserver variability in MRI scores were comparable to CT (0.877-0.965 compared to 0.877-0.989 respectively) suggesting good image quality with strong correlation between MR and CT component scores (bronchiectasis/bronchial wall thickening r=0.828,p<0.001; mucus plugging r=0.812, p<0.001; parenchymal score r=0.564 – 0.729, p<0.001 – 0.006). Median lung T1 did not correlate with clinical markers of disease severity, but median lung T2 demonstrated strong correlation with CT bronchial wall thickening (r=-0.655, p=0.001) and LCI2.5 (r=-0.540, p=0.046), most likely representing a surrogate of pulmonary perfusion (most pulmonary T2 signal likely originates from the pulmonary blood pool). Significant ufbSSFP enhancement was demonstrated post hyperoxygenation, but the degree of enhancement did not correlate significantly with clinical measures of disease severity. There was, however, very strong correlations between matrix pencil decomposition ventilation fraction and LCI2.5 (r=0.831, p=0.001) and CFCT scores (r= up to 0.731, p=<0.001). Significant correlation was also demonstrated between measures of ventilation heterogeneity (oxygen wash-out time skew and kurtosis) and both LCI2.5 (r=0.591, p=0.013) and CFCT component scores (r= up to 0.718, p<0.001). Liver T1 values did not correlate with evidence of liver disease on liver function tests or ultrasound imaging, but interpretation was severely limited by the very small number of recruits with CF liver disease. Sinus imaging was the last part of the protocol with failed analysis in only one patient from too much motion (a 6 year old). Association was demonstrated between exacerbation frequency and opacification of maxillary sinuses by mucusa (p=0.074), between CT hyperinflation score and increasing levels of mucus susceptibility artefact (0=0.028), between exacerbation frequency, CT bronchial wall thickening and mucus plugging and increased sinus mucus diffusion (r=0.581, p=0.048, r=0.744, p=0.006 and r=0.633, p=0.019 respectively) and between CT hyperinflation, bronchiectasis and bronchial wall thickening scores and increased sinus mucosal diffusion (r=-0.847, p=0.016; r=-0.542, p=0.017 and r=-0.427, p=0.069 respectively). A third of recruits stated that they would opt for MR imaging over CT imaging in the future and whilst 41% reported difficulties staying still for the MRI, respiratory image post processing was successful in all participants, with no parts of the MRI studies repeated. Conclusion – Multisystem lung, liver and sinus MRI is feasible, well tolerated by people with CF or PCD, down to the age of 6 years, and provides gross structural imaging of sufficient quality to replace CT for lung imaging surveillance. Furthermore, the addition of functional lung imaging provides quantitative outputs which correlate well with clinically established lung function tests with the benefit of spatially localised lung function and additional quantitative measures of relevant extrapulmonary disease, within a single ionising radiation free examination. The data from this study have supported funding for future work addressing short, medium and long-term repeatability and longitudinal trends both in times of disease stability and over the course of an infective exacerbation.Open Acces

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

Hyperpolarized 3He Magnetic Resonance Imaging Phenotypes of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the world. Identifying clinically relevant COPD phenotypes has the potential to reduce the global burden of COPD by helping to alleviate symptoms, slow disease progression and prevent exacerbation by stratifying patient cohorts and forming targeted treatment plans. In this regard, quantitative pulmonary imaging with hyperpolarized 3He magnetic resonance imaging (MRI) and thoracic computed tomography (CT) have emerged as ways to identify and measure biomarkers of lung structure and function. 3He MRI may be used as a tool to probe both functional and structural properties of the lung whereby static-ventilation maps allow the direct visualization of ventilated lung regions and 3He apparent diffusion coefficient maps show the lung microstructure at alveolar scales. At the same time, thoracic CT provides quantitative measurements of lung density and airway wall and lumen dimensions. Together, MRI and CT may be used to characterize the relative contributions of airways disease and emphysema on overall lung function, providing a way to phenotype underlying disease processes in a way that conventional measurements of airflow, taken at the mouth, cannot. Importantly, structure-function measurements obtained from 3He MRI and CT can be extracted from the whole-lung or from individual lung lobes, providing direct information on specific lung regions. In this thesis, my goal was to identify pulmonary imaging phenotypes to provide a better understanding of COPD pathophysiology in ex-smokers with and without airflow limitation. This thesis showed: 1) ex-smokers without airflow limitation had imaging evidence of subclinical lung and vascular disease, 2) pulmonary abnormalities in ex- smokers without airflow limitation were spatially related to airways disease and very mild emphysema, and, 3) in ex-smokers with COPD, there were distinct apical-basal lung phenotypes associated with disease severity. Collectively, these findings provide strong evidence that quantitative pulmonary imaging phenotypes may be used to characterize the underlying pathophysiology of very mild or early COPD and in patients with severe disease

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

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

Structure and Function of Asthma Evaluated Using Pulmonary Imaging

Asthma has been understood to affect the airways in a spatially heterogeneous manner for over six decades. Computational models of the asthmatic lung have suggested that airway abnormalities are diffusely and randomly distributed throughout the lung, however these mechanisms have been challenging to measure in vivo using current clinical tools. Pulmonary structure and function are still clinically characterized by the forced expiratory volume in one-second (FEV1) – a global measurement of airflow obstruction that is unable to capture the underlying regional heterogeneity that may be responsible for symptoms and disease worsening. In contrast, pulmonary magnetic resonance imaging (MRI) provides a way to visualize and quantify regional heterogeneity in vivo, and preliminary MRI studies in patients suggest that airway abnormalities in asthma are spatially persistent and not random. Despite these disruptive results, imaging has played a limited clinical role because the etiology of ventilation heterogeneity in asthma and its long-term pattern remain poorly understood. Accordingly, the objective of this thesis was to develop a deeper understanding of the pulmonary structure and function of asthma using functional MRI in conjunction with structural computed tomography (CT) and oscillometry, to provide a foundation for imaging to guide disease phenotyping, personalized treatment and prediction of disease worsening. We first evaluated the biomechanics of ventilation heterogeneity and showed that MRI and oscillometry explained biomechanical differences between asthma and other forms of airways disease. We then evaluated the long-term spatial and temporal nature of airway and ventilation abnormalities in patients with asthma. In nonidentical twins, we observed a spatially-matched CT airway and MRI ventilation abnormality that persisted for seven-years; we estimated the probability of an identical defect occurring in time and space to be 1 in 130,000. In unrelated asthmatics, ventilation defects were spatially-persistent over 6.5-years and uniquely predicted longitudinal bronchodilator reversibility. Finally, we investigated the entire CT airway tree and showed that airways were truncated in severe asthma related to thickened airway walls and worse MRI ventilation heterogeneity. Together, these results advance our understanding of asthma as a non-random disease and support the use of MRI ventilation to guide clinical phenotyping and treatment decisions

Multi-Nuclear Magnetic Resonance Imaging of Obstructive Lung Disease

Obstructive lung diseases such as chronic-obstructive-lung-disease (COPD), bronchiectasis, and asthma are characterized by airflow obstruction. They affect over six million Canadians costing the economy $12 billion/year. Despite decades of research, therapies that modify obstructive-lung-disease progression and control are lacking because patient diagnosis, monitoring, and response to therapy are currently made using airflow measurements that may conceal the independent contributions of underlying pathologies. One goal of obstructive-lung-disease research is to develop ways to identify patients with specific underlying pathological phenotypes to improve patient care and outcomes. Thoracic computed-tomography (CT) and magnetic-resonance-imaging (MRI) provide ways to regionally identify the underlying pathologies associated with obstructive-lung-disease, and offer quantitative biomarkers of obstructive-lung-disease (e.g. lung-density, airway dimensions, ventilation abnormalities, and lung microstructure). As the first step to identify patients with specific underlying pathological phenotypes, it is important to understand the physiological and clinical consequences of these imaging derived measurements. Accordingly, our objective was to evaluate lung structure and function using multi-nuclear pulmonary MRI in aging and obstructive-lung-disease to provide a better understanding of MR-derived biomarkers. In older never-smokers, the majority of subjects had 3He MR ventilation abnormalities that were not responsive to bronchodilation. 3He ventilation abnormalities were related to airflow obstruction and airways resistance, but not occupational exposure or exercise limitation. We then developed and evaluated ultra-short-echo-time MRI in COPD subjects with and without bronchiectasis. This work demonstrated that ultra-short-echo-time MR-derived measurements were reproducible and significantly related to CT tissue-density measurements. In the COPD subjects with bronchiectasis, ultra-short-echo-time signal-intensity was related to airway measurements. In COPD subjects without bronchiectasis, ultra-short-echo-time signal-intensity was related to the severity of emphysema. Finally, based on the ultra-short-echo-time MR biomarkers developed in patients with COPD and bronchiectasis, patients that share some of the airway and inflammatory features common in asthmatics, we produced ultra-short-echo-time MR measurements in asthma. These measurements not only provided similar information as CT, but also information about regional ventilation deficits. These results demonstrated that ultra-short-echo-time MR biomarkers may reflect ventilation heterogeneity and/or gas-trapping in asthma. These important findings indicate that multi-nuclear pulmonary MRI has the potential to quantitatively evaluate the different pathologies of obstructive-lung-disease

Texture Analysis and Machine Learning to Predict Pulmonary Ventilation from Thoracic Computed Tomography

Chronic obstructive pulmonary disease (COPD) leads to persistent airflow limitation, causing a large burden to patients and the health care system. Thoracic CT provides an opportunity to observe the structural pathophysiology of COPD, whereas hyperpolarized gas MRI provides images of the consequential ventilation heterogeneity. However, hyperpolarized gas MRI is currently limited to research centres, due to the high cost of gas and polarization equipment. Therefore, I developed a pipeline using texture analysis and machine learning methods to create predicted ventilation maps based on non-contrast enhanced, single-volume thoracic CT. In a COPD cohort, predicted ventilation maps were qualitatively and quantitatively related to ground-truth MRI ventilation, and both maps were related to important patient lung function and quality-of-life measures. This study is the first to demonstrate the feasibility of predicting hyperpolarized MRI-based ventilation from single-volume, breath-hold thoracic CT, which has potential to translate pulmonary ventilation information to widely available thoracic CT imaging