Age, sex, and lung volume dependence of dissolved xenon-129 MRI gas exchange metrics.

PURPOSE: To characterize the dependence of Xe-MRI gas transfer metrics upon age, sex, and lung volume in a group of healthy volunteers. METHODS: Sixty-five subjects with no history of chronic lung disease were assessed with 129Xe-MRI using a four-echo 3D radial spectroscopic imaging sequence and a dose of xenon titrated according to subject height that was inhaled from a lung volume of functional residual capacity (FRC). Imaging was repeated in 34 subjects at total lung capacity (TLC). Regional maps of the fractions of dissolved xenon in red blood cells (RBC), membrane (M), and airspace (Gas) were acquired at an isotropic resolution of 2 cm, from which global averages of the ratios RBC:M, RBC:Gas, and M:Gas were computed. RESULTS: Data from 26 males and 36 females with a median age of 43 y (range: 20-69 y) were of sufficient quality to analyze. Age (p = 0.0006) and sex (p < 0.0001) were significant predictors for RBC:M, and a linear regression showed higher values and steeper decline in males: RBC:M(Males) = -0.00362 × Age + 0.60 (p = 0.01, R2 = 0.25); RBC:M(Females) = -0.00170 × Age + 0.44 (p = 0.02, R2 = 0.15). Similarly, age and sex were significant predictors for RBC:Gas but not for M:Gas. RBC:M, M:Gas and RBC:Gas were significantly lower at TLC than at FRC (plus inhaled volume), with an average 9%, 30% and 35% decrease, respectively. CONCLUSION: Expected age and sex dependence of pulmonary function concurs with 129Xe RBC:M imaging results, demonstrating that these variables must be considered when reporting Xe-MRI metrics. Xenon doses and breathing maneuvers should be controlled due to the strong dependence of Xe-MRI metrics upon lung volume.

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.

Explainable deep learning-based survival prediction for non-small cell lung cancer patients undergoing radical radiotherapy.

BACKGROUND AND PURPOSE: Survival is frequently assessed using Cox proportional hazards (CPH) regression; however, CPH may be too simplistic as it assumes a linear relationship between covariables and the outcome. Alternative, non-linear machine learning (ML)-based approaches, such as random survival forests (RSFs) and, more recently, deep learning (DL) have been proposed; however, these techniques are largely black-box in nature, limiting explainability. We compared CPH, RSF and DL to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients receiving radiotherapy using pre-treatment covariables. We employed explainable techniques to provide insights into the contribution of each covariable on OS prediction. MATERIALS AND METHODS: The dataset contained 471 stage I-IV NSCLC patients treated with radiotherapy. We built CPH, RSF and DL OS prediction models using several baseline covariable combinations. 10-fold Monte-Carlo cross-validation was employed with a split of 70%:10%:20% for training, validation and testing, respectively. We primarily evaluated performance using the concordance index (C-index) and integrated Brier score (IBS). Local interpretable model-agnostic explanation (LIME) values, adapted for use in survival analysis, were computed for each model. RESULTS: The DL method exhibited a significantly improved C-index of 0.670 compared to the CPH and a significantly improved IBS of 0.121 compared to the CPH and RSF approaches. LIME values suggested that, for the DL method, the three most important covariables in OS prediction were stage, administration of chemotherapy and oesophageal mean radiation dose. CONCLUSION: We show that, using pre-treatment covariables, a DL approach demonstrates superior performance over CPH and RSF for OS prediction and use explainable techniques to provide transparency and interpretability.

Review of Hyperpolarized Pulmonary Functional 129 Xe MR for Long-COVID.

The respiratory consequences of acute COVID-19 infection and related symptoms tend to resolve 4 weeks post-infection. However, for some patients, new, recurrent, or persisting symptoms remain beyond the acute phase and persist for months, post-infection. The symptoms that remain have been referred to as long-COVID. A number of research sites employed 129 Xe magnetic resonance imaging (MRI) during the pandemic and evaluated patients post-infection, months after hospitalization or home-based care as a way to better understand the consequences of infection on 129 Xe MR gas-exchange and ventilation imaging. A systematic review and comprehensive search were employed using MEDLINE via PubMed (April 2023) using the National Library of Medicine's Medical Subject Headings and key words: post-COVID-19, MRI, 129 Xe, long-COVID, COVID pneumonia, and post-acute COVID-19 syndrome. Fifteen peer-reviewed manuscripts were identified including four editorials, a single letter to the editor, one review article, and nine original research manuscripts (2020-2023). MRI and MR spectroscopy results are summarized from these prospective, controlled studies, which involved small sample sizes ranging from 9 to 76 participants. Key findings included: 1) 129 Xe MRI gas-exchange and ventilation abnormalities, 3 months post-COVID-19 infection, and 2) a combination of MRI gas-exchange and ventilation abnormalities alongside persistent symptoms in patients hospitalized and not hospitalized for COVID-19, 1-year post-infection. The persistence of respiratory symptoms and 129 Xe MRI abnormalities in the context of normal or nearly normal pulmonary function test results and chest computed tomography (CT) was consistent. Longitudinal improvements were observed in long-term follow-up of long-COVID patients but mean 129 Xe gas-exchange, ventilation heterogeneity values and symptoms remained abnormal, 1-year post-infection. Pulmonary functional MRI using inhaled hyperpolarized 129 Xe gas has played a role in detecting gas-exchange and ventilation abnormalities providing complementary information that may help develop our understanding of the root causes of long-COVID. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Voxel-Wise Comparison of Co-Registered Quantitative CT and Hyperpolarised Gas Diffusion-Weighted MRI Measurements in IPF.

The patterns of idiopathic pulmonary fibrosis (IPF) lung disease that directly correspond to elevated hyperpolarised gas diffusion-weighted (DW) MRI metrics are currently unknown. This study aims to develop a spatial co-registration framework for a voxel-wise comparison of hyperpolarised gas DW-MRI and CALIPER quantitative CT patterns. Sixteen IPF patients underwent 3He DW-MRI and CT at baseline, and eleven patients had a 1-year follow-up DW-MRI. Six healthy volunteers underwent 129Xe DW-MRI at baseline only. Moreover, 3He DW-MRI was indirectly co-registered to CT via spatially aligned 3He ventilation and structural 1H MRI. A voxel-wise comparison of the overlapping 3He apparent diffusion coefficient (ADC) and mean acinar dimension (LmD) maps with CALIPER CT patterns was performed at baseline and after 1 year. The abnormal lung percentage classified with the LmD value, based on a healthy volunteer 129Xe LmD, and CALIPER was compared with a Bland-Altman analysis. The largest DW-MRI metrics were found in the regions classified as honeycombing, and longitudinal DW-MRI changes were observed in the baseline-classified reticular changes and ground-glass opacities regions. A mean bias of -15.3% (95% interval -56.8% to 26.2%) towards CALIPER was observed for the abnormal lung percentage. This suggests DW-MRI may detect microstructural changes in areas of the lung that are determined visibly and quantitatively normal by CT.

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices.

Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.

The Left Atrial Area Derived Cardiovascular Magnetic Resonance Left Ventricular Filling Pressure Equation Shows Superiority over Integrated Echocardiography.

Background and objectives: Evaluating left ventricular filling pressure (LVFP) plays a crucial role in diagnosing and managing heart failure (HF). While traditional assessment methods involve multi-parametric transthoracic echocardiography (TTE) or right heart catheterisation (RHC), cardiovascular magnetic resonance (CMR) has emerged as a valuable diagnostic tool in HF. This study aimed to assess a simple CMR-derived model to estimate pulmonary capillary wedge pressure (PCWP) in a cohort of patients with suspected or proven heart failure and to investigate its performance in risk-stratifying patients. Materials and methods: A total of 835 patients with breathlessness were evaluated using RHC and CMR and split into derivation (85%) and validation cohorts (15%). Uni-variate and multi-variate linear regression analyses were used to derive a model for PCWP estimation using CMR. The model's performance was evaluated by comparing CMR-derived PCWP with PCWP obtained from RHC. Results: A CMR-derived PCWP incorporating left ventricular mass and the left atrial area (LAA) demonstrated good diagnostic accuracy. The model correctly reclassified 66% of participants whose TTE was 'indeterminate' or 'incorrect' in identifying raised filling pressures. On survival analysis, the CMR-derived PCWP model was predictive for mortality (HR 1.15, 95% CI 1.04-1.28, p = 0.005), which was not the case for PCWP obtained using RHC or TTE. Conclusions: The simplified CMR-derived PCWP model provides an accurate and practical tool for estimating PCWP in patients with suspected or proven heart failure. Its predictive value for mortality suggests the ability to play a valuable adjunctive role in echocardiography, especially in cases with unclear echocardiographic assessment.

A Framework for Predicting X-Nuclei Transmitter Gain Using 1H Signal.

Commercial human MR scanners are optimised for proton imaging, containing sophisticated prescan algorithms with setting parameters such as RF transmit gain and power. These are not optimal for X-nuclear application and are challenging to apply to hyperpolarised experiments, where the non-renewable magnetisation signal changes during the experiment. We hypothesised that, despite the complex and inherently nonlinear electrodynamic physics underlying coil loading and spatial variation, simple linear regression would be sufficient to accurately predict X-nuclear transmit gain based on concomitantly acquired data from the proton body coil. We collected data across 156 scan visits at two sites as part of ongoing studies investigating sodium, hyperpolarised carbon, and hyperpolarised xenon. We demonstrate that simple linear regression is able to accurately predict sodium, carbon, or xenon transmit gain as a function of position and proton gain, with variation that is less than the intrasubject variability. In conclusion, sites running multinuclear studies may be able to remove the time-consuming need to separately acquire X-nuclear reference power calibration, inferring it from the proton instead.

Initial feasibility and challenges of hyperpolarized 129 Xe MRI in neonates with bronchopulmonary dysplasia.

PURPOSE: The underlying functional and microstructural lung disease in neonates who are born preterm (bronchopulmonary dysplasia, BPD) remains poorly characterized. Moreover, there is a lack of suitable techniques to reliably assess lung function in this population. Here, we report our preliminary experience with hyperpolarized 129 Xe MRI in neonates with BPD. METHODS: Neonatal intensive care patients with established BPD were recruited (N = 9) and imaged at a corrected gestational age of median:40.7 (range:37.1, 44.4) wk using a 1.5T neonatal scanner. 2D 129 Xe ventilation and diffusion-weighted images and dissolved phase spectroscopy were acquired, alongside 1 H 3D radial UTE. 129 Xe images were acquired during a series of short apneic breath-holds (˜3 s). 1 H UTE images were acquired during tidal breathing. Ventilation defects were manually identified and qualitatively compared to lung structures on UTE. ADCs were calculated on a voxel-wise basis. The signal ratio of the 129 Xe red blood cell (RBC) and tissue membrane (M) resonances from spectroscopy was determined. RESULTS: Spiral-based 129 Xe ventilation imaging showed good image quality and sufficient sensitivity to detect mild ventilation abnormalities in patients with BPD. 129 Xe ADC values were elevated above that expected given healthy data in older children and adults (median:0.046 [range:0.041, 0.064] cm2 s-1 ); the highest value obtained from an extremely pre-term patient. 129 Xe spectroscopy revealed a low RBC/M ratio (0.14 [0.06, 0.21]). CONCLUSION: We have demonstrated initial feasibility of 129 Xe lung MRI in neonates. With further data, the technique may help guide management of infant lung diseases in the neonatal period and beyond.

Hyperpolarised xenon-129 diffusion-weighted magnetic resonance imaging for assessing lung microstructure in idiopathic pulmonary fibrosis.

Background: Hyperpolarised 129-xenon (129Xe) magnetic resonance imaging (MRI) shows promise in monitoring the progression of idiopathic pulmonary fibrosis (IPF) due to the lack of ionising radiation and the ability to quantify functional impairment. Diffusion-weighted (DW)-MRI with hyperpolarised gases can provide information about lung microstructure. The aims were to compare 129Xe DW-MRI measurements with pulmonary function tests (PFTs), and to assess whether they can detect early signs of disease progression in patients with newly diagnosed IPF. Methods: This is a prospective, single-centre, observational imaging study of patients presenting with IPF to Northern General Hospital (Sheffield, UK). Hyperpolarised 129Xe DW-MRI was performed at 1.5 T on a whole-body General Electric HDx scanner and PFTs were performed on the same day as the MRI scan. Results: There was an increase in global 129Xe apparent diffusion coefficient (ADC) between the baseline and 12-month visits (mean 0.043 cm2·s-1, 95% CI 0.040-0.047 cm2·s-1 versus mean 0.045 cm2·s-1, 95% CI 0.040-0.049 cm2·s-1; p=0.044; n=20), with no significant change in PFTs over the same time period. There was also an increase in 129Xe ADC in the lower zone (p=0.027), and an increase in 129Xe mean acinar dimension in the lower zone (p=0.033) between the baseline and 12-month visits. 129Xe DW-MRI measurements correlated strongly with diffusing capacity of the lung for carbon monoxide (% predicted), transfer coefficient of the lung for carbon monoxide (KCO) and KCO (% predicted). Conclusions: 129Xe DW-MRI measurements appear to be sensitive to early changes of microstructural disease that are consistent with progression in IPF at 12 months. As new drug treatments are developed, the ability to quantify subtle changes using 129Xe DW-MRI could be particularly valuable.

Establishing minimally important differences for cardiac MRI end-points in pulmonary arterial hypertension.

BACKGROUND: Cardiac magnetic resonance (CMR) is the gold standard technique to assess biventricular volumes and function, and is increasingly being considered as an end-point in clinical studies. Currently, with the exception of right ventricular (RV) stroke volume and RV end-diastolic volume, there is only limited data on minimally important differences (MIDs) reported for CMR metrics. Our study aimed to identify MIDs for CMR metrics based on US Food and Drug Administration recommendations for a clinical outcome measure that should reflect how a patient "feels, functions or survives". METHODS: Consecutive treatment-naïve patients with pulmonary arterial hypertension (PAH) between 2010 and 2022 who had two CMR scans (at baseline prior to treatment and 12 months following treatment) were identified from the ASPIRE registry. All patients were followed up for 1 additional year after the second scan. For both scans, cardiac measurements were obtained from a validated fully automated segmentation tool. The MID in CMR metrics was determined using two distribution-based (0.5sd and minimal detectable change) and two anchor-based (change difference and generalised linear model regression) methods benchmarked to how a patient "feels" (emPHasis-10 quality of life questionnaire), "functions" (incremental shuttle walk test) or "survives" for 1-year mortality to changes in CMR measurements. RESULTS: 254 patients with PAH were included (mean±sd age 53±16 years, 79% female and 66% categorised as intermediate risk based on the 2022 European Society of Cardiology/European Respiratory Society risk score). We identified a 5% absolute increase in RV ejection fraction and a 17 mL decrease in RV end-diastolic or end-systolic volumes as the MIDs for improvement. Conversely, a 5% decrease in RV ejection fraction and a 10 mL increase in RV volumes were associated with worsening. CONCLUSIONS: This study establishes clinically relevant CMR MIDs for how a patient "feels, functions or survives" in response to PAH treatment. These findings provide further support for the use of CMR as a clinically relevant clinical outcome measure and will aid trial size calculations for studies using CMR.

Plain language summaryPulmonary arterial hypertension (PAH) is a disease of the vessels of the lung that causes their narrowing and stiffening. As a result, the heart pumping blood into these diseased lung vessels has to work harder and eventually gets worn out. PAH can affect patients' ability to function in daily activities and impact their quality of life. It also reduces their life expectancy dramatically. Patients are, therefore, often monitored and undergo several investigations to adapt treatment according to their situation. These investigations include a survey of how a patient feels (the emPHasis-10 questionnaire), functions (walking test) and how well the heart is coping with the disease (MRI of the heart). Until now, it is unclear how changes on MRI of the heart reflect changes in how a patient feels and functions. Our study identified patients that had the emPHasis-10 questionnaire, walking test and MRI of the heart at both the time of PAH diagnosis and one year later. This allowed us to compare how the changes in the different tests relate to each other. And because previous research identified thresholds for important changes in the emPHasis-10 questionnaire and the walking tests, we were able to use these tests as a benchmark for changes in the MRI of the heart. Our study identified thresholds for change on heart MRI that might indicate whether a patient has improved or worsened. This finding might have implications for how patients are monitored in clinical practice and future research on PAH treatments.

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping.

Personalising airway clearance in chronic suppurative lung diseases: a scoping review.

Background: Personalised airway clearance techniques are commonly recommended to augment mucus clearance in chronic suppurative lung diseases. It is unclear what current literature tells us about how airway clearance regimens should be personalised. This scoping review explores current research on airway clearance technique in chronic suppurative lung diseases, to establish the extent and type of guidance in this area, identify knowledge gaps and determine the factors which physiotherapists should consider when personalising airway clearance regimens. Methods: Systematic searching of online databases (MEDLINE, EMBASE, CINAHL, PEDro, Cochrane, Web of Science) was used to identify full-text publications in the last 25 years that described methods of personalising airway clearance techniques in chronic suppurative lung diseases. Items from the TIDieR framework provided a priori categories which were modified based on the initial data to develop a "Best-fit" framework for data charting. The findings were subsequently transformed into a personalisation model. Results: A broad range of publications were identified, most commonly general review papers (44%). The items identified were grouped into seven personalisation factors: physical, psychosocial, airway clearance technique (ACT) type, procedures, dosage, response and provider. As only two divergent models of ACT personalisation were found, the personalisation factors identified were then used to develop a model for physiotherapists. Conclusions: The personalisation of airway clearance regimens is widely discussed in the current literature, which provides a range of factors that should be considered. This review summarises the current literature, organising findings into a proposed airway clearance personalisation model, to provide clarity in this field.

Non-invasive detection of severe PH in lung disease using magnetic resonance imaging.

Introduction: Severe pulmonary hypertension (mean pulmonary artery pressure ≥35 mmHg) in chronic lung disease (PH-CLD) is associated with high mortality and morbidity. Data suggesting potential response to vasodilator therapy in patients with PH-CLD is emerging. The current diagnostic strategy utilises transthoracic Echocardiography (TTE), which can be technically challenging in some patients with advanced CLD. The aim of this study was to evaluate the diagnostic role of MRI models to diagnose severe PH in CLD. Methods: 167 patients with CLD referred for suspected PH who underwent baseline cardiac MRI, pulmonary function tests and right heart catheterisation were identified. In a derivation cohort (n = 67) a bi-logistic regression model was developed to identify severe PH and compared to a previously published multiparameter model (Whitfield model), which is based on interventricular septal angle, ventricular mass index and diastolic pulmonary artery area. The model was evaluated in a test cohort. Results: The CLD-PH MRI model [= (-13.104) + (13.059 * VMI)-(0.237 * PA RAC) + (0.083 * Systolic Septal Angle)], had high accuracy in the test cohort (area under the ROC curve (0.91) (p < 0.0001), sensitivity 92.3%, specificity 70.2%, PPV 77.4%, and NPV 89.2%. The Whitfield model also had high accuracy in the test cohort (area under the ROC curve (0.92) (p < 0.0001), sensitivity 80.8%, specificity 87.2%, PPV 87.5%, and NPV 80.4%. Conclusion: The CLD-PH MRI model and Whitfield model have high accuracy to detect severe PH in CLD, and have strong prognostic value.

Functional imaging in asthma and COPD: design of the NOVELTY ADPro substudy.

The NOVEL observational longiTudinal studY (NOVELTY; ClinicalTrials.gov identifier NCT02760329) is a global, prospective, observational study of ∼12 000 patients with a diagnosis of asthma and/or COPD. Here, we describe the design of the Advanced Diagnostic Profiling (ADPro) substudy of NOVELTY being conducted in a subset of ∼180 patients recruited from two primary care sites in York, UK. ADPro is employing a combination of novel functional imaging and physiological and metabolic modalities to explore structural and functional changes in the lungs, and their association with different phenotypes and endotypes. Patients participating in the ADPro substudy will attend two visits at the University of Sheffield, UK, 12±2 months apart, at which they will undergo imaging and physiological lung function testing. The primary end-points are the distributions of whole lung functional and morphological measurements assessed with xenon-129 magnetic resonance imaging, including ventilation, gas transfer and airway microstructural indices. Physiological assessments of pulmonary function include spirometry, bronchodilator reversibility, static lung volumes via body plethysmography, transfer factor of the lung for carbon monoxide, multiple-breath nitrogen washout and airway oscillometry. Fractional exhaled nitric oxide will be measured as a marker of type-2 airways inflammation. Regional and global assessment of lung function using these techniques will enable more precise phenotyping of patients with physician-assigned asthma and/or COPD. These techniques will be assessed for their sensitivity to markers of early disease progression.

Longitudinal Lung Function Assessment of Patients Hospitalized With COVID-19 Using 1H and 129Xe Lung MRI.

BACKGROUND: Microvascular abnormalities and impaired gas transfer have been observed in patients with COVID-19. The progression of pulmonary changes in these patients remains unclear. RESEARCH QUESTION: Do patients hospitalized with COVID-19 without evidence of architectural distortion on structural imaging exhibit longitudinal improvements in lung function measured by using 1H and 129Xe MRI between 6 and 52 weeks following hospitalization? STUDY DESIGN AND METHODS: Patients who were hospitalized with COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25, and 51 weeks following hospital admission in a prospective cohort study between November 2020 and February 2022. The imaging protocol was as follows: 1H ultra-short echo time, contrast-enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion-weighted, and 129Xe spectroscopic imaging of gas exchange. RESULTS: Nine patients were recruited (age 57 ± 14 [median ± interquartile range] years; six of nine patients were male). Patients underwent MRI at 6 (n = 9), 12 (n = 9), 25 (n = 6), and 51 (n = 8) weeks following hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients exhibited impaired 129Xe gas transfer (RBC to membrane fraction), but lung microstructure was not increased (apparent diffusion coefficient and mean acinar airway dimensions). Minor ventilation abnormalities present in four patients were largely resolved in the 6- to 25-week period. At 12 weeks, all patients with lung perfusion data (n = 6) showed an increase in both pulmonary blood volume and flow compared with 6 weeks, although this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared with 6-week examinations; however, 129Xe gas transfer remained abnormally low at weeks 12, 25, and 51. INTERPRETATION: 129Xe gas transfer was impaired up to 1 year following hospitalization in patients who were hospitalized with COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation was normal at 52 weeks.

A hybrid model- and deep learning-based framework for functional lung image synthesis from multi-inflation CT and hyperpolarized gas MRI.

BACKGROUND: Hyperpolarized gas MRI is a functional lung imaging modality capable of visualizing regional lung ventilation with exceptional detail within a single breath. However, this modality requires specialized equipment and exogenous contrast, which limits widespread clinical adoption. CT ventilation imaging employs various metrics to model regional ventilation from non-contrast CT scans acquired at multiple inflation levels and has demonstrated moderate spatial correlation with hyperpolarized gas MRI. Recently, deep learning (DL)-based methods, utilizing convolutional neural networks (CNNs), have been leveraged for image synthesis applications. Hybrid approaches integrating computational modeling and data-driven methods have been utilized in cases where datasets are limited with the added benefit of maintaining physiological plausibility. PURPOSE: To develop and evaluate a multi-channel DL-based method that combines modeling and data-driven approaches to synthesize hyperpolarized gas MRI lung ventilation scans from multi-inflation, non-contrast CT and quantitatively compare these synthetic ventilation scans to conventional CT ventilation modeling. METHODS: In this study, we propose a hybrid DL configuration that integrates model- and data-driven methods to synthesize hyperpolarized gas MRI lung ventilation scans from a combination of non-contrast, multi-inflation CT and CT ventilation modeling. We used a diverse dataset comprising paired inspiratory and expiratory CT and helium-3 hyperpolarized gas MRI for 47 participants with a range of pulmonary pathologies. We performed six-fold cross-validation on the dataset and evaluated the spatial correlation between the synthetic ventilation and real hyperpolarized gas MRI scans; the proposed hybrid framework was compared to conventional CT ventilation modeling and other non-hybrid DL configurations. Synthetic ventilation scans were evaluated using voxel-wise evaluation metrics such as Spearman's correlation and mean square error (MSE), in addition to clinical biomarkers of lung function such as the ventilated lung percentage (VLP). Furthermore, regional localization of ventilated and defect lung regions was assessed via the Dice similarity coefficient (DSC). RESULTS: We showed that the proposed hybrid framework is capable of accurately replicating ventilation defects seen in the real hyperpolarized gas MRI scans, achieving a voxel-wise Spearman's correlation of 0.57 ± 0.17 and an MSE of 0.017 ± 0.01. The hybrid framework significantly outperformed CT ventilation modeling alone and all other DL configurations using Spearman's correlation. The proposed framework was capable of generating clinically relevant metrics such as the VLP without manual intervention, resulting in a Bland-Altman bias of 3.04%, significantly outperforming CT ventilation modeling. Relative to CT ventilation modeling, the hybrid framework yielded significantly more accurate delineations of ventilated and defect lung regions, achieving a DSC of 0.95 and 0.48 for ventilated and defect regions, respectively. CONCLUSION: The ability to generate realistic synthetic ventilation scans from CT has implications for several clinical applications, including functional lung avoidance radiotherapy and treatment response mapping. CT is an integral part of almost every clinical lung imaging workflow and hence is readily available for most patients; therefore, synthetic ventilation from non-contrast CT can provide patients with wider access to ventilation imaging worldwide.

Imaging gas-exchange lung function and brain tissue uptake of hyperpolarized 129 Xe using sampling density-weighted MRSI.

PURPOSE: Imaging of the different resonances of hyperpolarized 129 Xe in the brain and lungs was performed using a 3D sampling density-weighted MRSI technique in healthy volunteers. METHODS: Four volunteers underwent dissolved-phase hyperpolarized 129 Xe imaging in the lung with the MRSI technique, which was designed to improve the point-spread function while preserving SNR (1799 phase-encoding steps, 14-s breath hold, 2.1-cm isotropic resolution). A frequency-tailored RF excitation pulse was implemented to reliably excite both the 129 Xe gas and dissolved phase (tissue/blood signal) with 0.1° and 10° flip angles, respectively. Images of xenon gas in the lung airspaces and xenon dissolved in lung tissue/blood were used to generate quantitative signal ratio maps. The method was also optimized and used for imaging dissolved resonances of 129 Xe in the brain in 2 additional volunteers. RESULTS: High-quality regional spectra of hyperpolarized 129 Xe were achieved in both the lung and the brain. Ratio maps of the different xenon resonances were obtained in the lung with sufficient SNR (> 10) at both 1.5 T and 3 T, making a triple Lorentzian fit possible and enabling the measurement of relaxation times and xenon frequency shifts on a voxel-wise basis. The imaging technique was successfully adapted for brain imaging, resulting in the first demonstration of 3D xenon brain images with a 2-cm isotropic resolution. CONCLUSION: Density-weighted MRSI is an SNR and encoding-efficient way to image 129 Xe resonances in the lung and the brain, providing a valuable tool to quantify regional spectroscopic information.

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1 H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. PURPOSE: Develop a generalizable CNN for lung segmentation in 1 H-MRI, robust to pathology, acquisition protocol, vendor, and center. STUDY TYPE: Retrospective. POPULATION: A total of 809 1 H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1 H-MRI. ASSESSMENT: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. STATISTICAL TESTS: Kruskal-Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland-Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. RESULTS: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880-0.987), Average HD of 1.63 mm (0.65-5.45) and XOR of 0.079 (0.025-0.240) on the testing set and a DSC of 0.973 (0.866-0.987), Average HD of 1.11 mm (0.47-8.13) and XOR of 0.054 (0.026-0.255) on external validation data. DATA CONCLUSION: The 3D CNN generated accurate 1 H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.