New disagreement metrics incorporating spatial detail – applications to lung imaging

Evaluation of medical image segmentation is increasingly important. While set-based agreement metrics are widespread, they assess the absolute overlap, but fail to account for any spatial information related to the differences or to the shapes being analyzed. In this paper, we propose a family of new metrics that can be tailored to deal with a broad class of assessment needs

Patterns of regional lung physiology in cystic fibrosis using ventilation magnetic resonance imaging and multiple-breath washout

Hyperpolarised helium-3 (3He) ventilation magnetic resonance imaging (MRI) and multiple-breath washout (MBW) are sensitive methods for detecting lung disease in cystic fibrosis (CF). We aimed to explore their relationship across a broad range of CF disease severity and patient age, as well as assess the effect of inhaled lung volume on ventilation distribution.32 children and adults with CF underwent MBW and 3He-MRI at a lung volume of end-inspiratory tidal volume (EIVT). In addition, 28 patients performed 3He-MRI at total lung capacity. 3He-MRI scans were quantitatively analysed for ventilation defect percentage (VDP), ventilation heterogeneity index (VHI) and the number and size of individual contiguous ventilation defects. From MBW, the lung clearance index, convection-dependent ventilation heterogeneity (Scond) and convection-diffusion-dependent ventilation heterogeneity (Sacin) were calculated.VDP and VHI at EIVT strongly correlated with lung clearance index (r=0.89 and r=0.88, respectively), Sacin (r=0.84 and r=0.82, respectively) and forced expiratory volume in 1 s (FEV1) (r=-0.79 and r=-0.78, respectively). Two distinct 3He-MRI patterns were highlighted: patients with abnormal FEV1 had significantly (p<0.001) larger, but fewer, contiguous defects than those with normal FEV1, who tended to have numerous small volume defects. These two MRI patterns were delineated by a VDP of ∼10%. At total lung capacity, when compared to EIVT, VDP and VHI reduced in all subjects (p<0.001), demonstrating improved ventilation distribution and regions of volume-reversible and nonreversible ventilation abnormalities

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

A dual-channel deep learning approach for lung cavity estimation from hyperpolarized gas and proton MRI

Background Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives. Purpose We hypothesized that a DL-based dual-channel approach, leveraging both 1H-MRI and Xenon-129-MRI (129Xe-MRI), can generate LCEs more accurately than single-channel alternatives. Study Type Retrospective. Population A total of 480 corresponding 1H-MRI and 129Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8–71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6–83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets. Field Strength/Sequence 1.5-T, three-dimensional (3D) spoiled gradient-recalled 1H-MRI and 3D steady-state free-precession 129Xe-MRI. Assessment We developed a multimodal DL approach, integrating 129Xe-MRI and 1H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs. Statistical Tests Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland–Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant. Results The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867–0.978), 1.68 mm (37.0–0.778), and 0.066 (0.246–0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710). Data Conclusion Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention. Evidence Level 4. Technical Efficacy Stage 1

Xenon ventilation MRI in difficult asthma; initial experience in a clinical setting

Background: Hyperpolarised gas MRI can be used to assess ventilation patterns. Previous studies have shown the image derived metric of ventilation defect percent (VDP) to correlate with FEV1/FVC and FEV1 in asthma. Objectives: To explore the utility of hyperpolarised xenon-129 (129Xe) ventilation MRI in clinical care and examine its relationship with spirometry and other clinical metrics in people seen in a severe asthma service. Methods: 26 people referred from a severe asthma clinic for MRI scanning were assessed by contemporaneous 129Xe MRI and spirometry. A sub-group of 18 patients also underwent reversibility testing with spirometry and MRI. Quantitative MRI measures of ventilation were calculated; VDP and the ventilation heterogeneity index (VHI), and compared to spirometry, ACQ7 and blood eosinophil count. Images were reviewed by a multidisciplinary team. Results: VDP and VHI correlated with FEV1, FEV1/FVC and FEF25–75% but not with ACQ7 or blood eosinophil count. Discordance of MRI imaging and symptoms and/or pulmonary function tests also occurred, prompting diagnostic re-evaluation in some cases. Conclusion Hyperpolarised gas MRI provides a complementary method of assessment in people with difficult to manage asthma in a clinical setting. When used as a tool supporting clinical care in a severe asthma service, occurrences of discordance between symptoms, spirometry and MRI scanning indicate how MRI scanning may add to a management pathway

Reproducibility of 19F‐MR ventilation imaging in healthy volunteers

Purpose To assess the reproducibility of percentage ventilated lung volume (%VV) measurements in healthy volunteers acquired by fluorine (19F)‐MRI of inhaled perfluoropropane, implemented at two research sites. Methods In this prospective, ethically approved study, 40 healthy participants were recruited (May 2018‐June 2019) to one of two research sites. Participants underwent a single MRI scan session on a 3T scanner, involving periodic inhalation of a 79% perfluoropropane/21% oxygen gas mixture. Each gas inhalation session lasted about 30 seconds, consisting of three deep breaths of gas followed by a breath‐hold. Four 19F‐MR ventilation images were acquired per participant, each separated by approximately 6 minutes. The value of %VV was determined by registering separately acquired 1H images to ventilation images before semi‐automated image segmentation, performed independently by two observers. Reproducibility of %VV measurements was assessed by components of variance, intraclass correlation coefficients, coefficients of variation (CoV), and the Dice similarity coefficient. Results The MRI scans were well tolerated throughout, with no adverse events. There was a high degree of consistency in %VV measurements for each participant (CoVobserver1 = 0.43%; CoVobserver2 = 0.63%), with overall precision of %VV measurements determined to be within ± 1.7% (95% confidence interval). Interobserver agreement in %VV measurements revealed a high mean Dice similarity coefficient (SD) of 0.97 (0.02), with only minor discrepancies between observers. Conclusion We demonstrate good reproducibility of %VV measurements in a group of healthy participants using 19F‐MRI of inhaled perfluoropropane. Our methods have been successfully implemented across two different study sites, supporting the feasibility of performing larger multicenter clinical studies

129Xe and free-breathing 1H ventilation MRI in patients with cystic fibrosis: a dual-center study

Background Free-breathing 1H ventilation MRI shows promise but only single-center validation has yet been performed against methods which directly image lung ventilation in patients with cystic fibrosis (CF). Purpose To investigate the relationship between 129Xe and 1H ventilation images using data acquired at two centers. Study type Sequence comparison. Population Center 1; 24 patients with CF (12 female) aged 9–47 years. Center 2; 7 patients with CF (6 female) aged 13–18 years, and 6 healthy controls (6 female) aged 21–31 years. Data were acquired in different patients at each center. Field Strength/Sequence 1.5 T, 3D steady-state free precession and 2D spoiled gradient echo. Assessment Subjects were scanned with 129Xe ventilation and 1H free-breathing MRI and performed pulmonary function tests. Ventilation defect percent (VDP) was calculated using linear binning and images were visually assessed by H.M., L.J.S., and G.J.C. (10, 5, and 8 years' experience). Statistical Tests Correlations and linear regression analyses were performed between 129Xe VDP, 1H VDP, FEV1, and LCI. Bland–Altman analysis of 129Xe VDP and 1H VDP was carried out. Differences in metrics were assessed using one-way ANOVA or Kruskal–Wallis tests. Results 129Xe VDP and 1H VDP correlated strongly with; each other (r = 0.84), FEV1 z-score (129Xe VDP r = −0.83, 1H VDP r = −0.80), and LCI (129Xe VDP r = 0.91, 1H VDP r = 0.82). Bland–Altman analysis of 129Xe VDP and 1H VDP from both centers had a bias of 0.07% and limits of agreement of −16.1% and 16.2%. Linear regression relationships of VDP with FEV1 were not significantly different between 129Xe and 1H VDP (P = 0.08), while 129Xe VDP had a stronger relationship with LCI than 1H VDP. Data Conclusion 1H ventilation MRI shows large-scale agreement with 129Xe ventilation MRI in CF patients with established lung disease but may be less sensitive to subtle ventilation changes in patients with early-stage lung disease. Evidence Level 2 Technical Efficacy Stage

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

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 (1H)-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 1H-MRI, robust to pathology, acquisition protocol, vendor, and center. Study type Retrospective. Population A total of 809 1H-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 1H-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 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. Evidence Level 4. Technical Efficacy Stage 1

Longitudinal lung function assessment of patients hospitalised 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 hospitalised due to COVID-19 without evidence of architectural distortion on structural imaging show longitudinal improvements in lung function measured using 1H and 129Xe magnetic resonance imaging between 6-52 weeks after hospitalisation? STUDY DESIGN AND METHODS: Patients who were hospitalised due to COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25 and 51 weeks after hospital admission in a prospective cohort study between 11/2020 and 02/2022. Imaging protocol: 1H ultra-short echo time, contrast enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion weighted and 129Xe spectroscopic imaging of gas exchange. RESULTS: 9 patients were recruited (57±14 [median±interquartile range] years, 6/9 male). Patients underwent MRI at 6 (N=9), 12 (N=9), 25 (N=6) and 51 (N=8) weeks after hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients demonstrated impaired 129Xe gas transfer (red blood cell 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 6-25 week period. At 12 weeks, all patients with lung perfusion data (N=6) showed an increase in both pulmonary blood volume and flow when compared to 6 weeks, though this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared to 6-week examinations, however 129Xe gas transfer remained abnormally low at weeks 12, 25 and 51. INTERPRETATION: 129Xe gas transfer was impaired up to one year after hospitalisation in patients who were hospitalised due to COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation wa normal at 52 weeks