Search

Purpose: To evaluate the feasibility and utility of a deep learning (DL)‐based reconstruction for improving the SNR of hyperpolarized 129Xe lung ventilation MRI. Methods: 129Xe lung ventilation MRI data acquired from patients with asthma and/or chronic obstructive pulmonary disease (COPD) were retrospectively reconstructed with a commercial DL reconstruction pipeline at five different denoising levels. Quantitative imaging metrics of lung ventilation including ventilation defect percentage (VDP) and ventilation heterogeneity index (VHI) were compared between each set of DL‐reconstructed images and alternative denoising strategies including: filtering, total variation denoising and higher‐order singular value decomposition. Structural similarity between the denoised and original images was assessed. In a prospective study, the feasibility of using SNR gains from DL reconstruction to allow natural‐abundance xenon MRI was evaluated in healthy volunteers. Results: 129Xe ventilation image SNR was improved with DL reconstruction when compared with conventionally reconstructed images. In patients with asthma and/or COPD, DL‐reconstructed images exhibited a slight positive bias in ventilation defect percentage (1.3% at 75% denoising) and ventilation heterogeneity index (˜1.4) when compared with conventionally reconstructed images. Additionally, DL‐reconstructed images preserved structural similarity more effectively than data denoised using alternative approaches. DL reconstruction greatly improved image SNR (greater than threefold), to a level that 129Xe ventilation imaging using natural‐abundance xenon appears feasible. Conclusion: DL‐based image reconstruction significantly improves 129Xe ventilation image SNR, preserves structural similarity, and leads to a minor bias in ventilation metrics that can be attributed to differences in the image sharpness. This tool should help facilitate cost‐effective 129Xe ventilation imaging with natural‐abundance xenon in the future. [ABSTRACT FROM AUTHOR]

Background: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)‐MRI lung segmentation.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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 2 [ABSTRACT FROM AUTHOR]

Rationale: Preterm birth is associated with low lung function in childhood, but little is known about the lung microstructure in childhood. Objectives: We assessed the differential associations between the historical diagnosis of bronchopulmonary dysplasia (BPD) and current lung function phenotypes on lung ventilation and microstructure in preterm-born children using hyperpolarized 129Xe ventilation and diffusion-weighted magnetic resonance imaging (MRI) and multiple-breath washout (MBW). Methods: Data were available from 63 children (aged 9-13 yr), including 44 born preterm (<34 weeks' gestation) and 19 termborn control subjects (>37 weeks' gestation). Preterm-born children were classified, using spirometry, as prematurityassociated obstructive lung disease (POLD; FEV1, lower limit of normal [LLN] and FEV1/FVC,LLN), prematurity-associated preserved ratio of impaired spirometry (FEV1,LLN and FEV1/FVC>LLN), preterm-(FEV1>LLN) and term-born control subjects, and those with and without BPD. Ventilation heterogeneity metrics were derived from 129Xe ventilation MRI and SF6 MBW. Alveolar microstructural dimensions were derived from 129Xe diffusion-weighted MRI. Measurements and Main Results: 129Xe ventilation defect percentage and ventilation heterogeneity index were significantly increased in preterm-born children with POLD. In contrast, mean 129Xe apparent diffusion coefficient, 129Xe apparent diffusion coefficient interquartile range, and 129Xe mean alveolar dimension interquartile range were significantly increased in preterm-born children with BPD, suggesting changes of alveolar dimensions. MBW metrics were all significantly increased in the POLD group compared with preterm- and term-born control subjects. Linear regression confirmed the differential effects of obstructive disease on ventilation defects and BPD on lung microstructure. Conclusion: We show that ventilation abnormalities are associated with POLD, and BPD in infancy is associated with abnormal lung microstructure. [ABSTRACT FROM AUTHOR]