The recovery process of COVID-19 patients is unclear. Some recovered patients complain of continued shortness of breath. Vasculopathy has been reported in COVID-19, stressing the importance of probing microstructure and function of lungs at the alveolar-capillary interface. While CT detects structural abnormalities, little is known about the impact of disease on lung function. 129Xe MRI is a technique uniquely capable of assessing ventilation, microstructure and gas exchange. Using 129Xe MRI, we found COVID-19 patients have higher ventilation defects percentage (5.9% vs 3.7%), unchanged microstructure, longer gas-blood exchange time (43.5 ms vs 32.5 ms), and reduced RBC/TP (0.279 vs 0.330) compared with healthy subjects. These findings suggest regional ventilation and alveolar airspace dimensions are relatively normal around the time of discharge, while gas-blood exchange function is diminished. This study establishes the feasibility of localized lung function measurement in COVID-19 patients. Such readouts could be useful as a supplement to structural imaging.

Our study has shown that HP Xe multi-b diffusion MRI with CS could be beneficial in lung microstructural assessments by acquiring less data while maintaining the consistent results with the FS acquisitions.

Purpose To fast and accurately reconstruct human lung gas MRI from highly undersampled k‐space using deep learning. Methods The scheme was comprised of coarse‐to‐fine nets (C‐net and F‐net). Zero‐filling images from retrospectively undersampled k‐space at an acceleration factor of 4 were used as input for C‐net, and then output intermediate results which were fed into F‐net. During training, a L2 loss function was adopted in C‐net, while a function that united L2 loss with proton prior knowledge was used in F‐net. The 871 hyperpolarized 129Xe pulmonary ventilation images from 72 volunteers were randomly arranged as training (90%) and testing (10%) data. Ventilation defect percentage comparisons were implemented using a paired 2‐tailed Student's t‐test and correlation analysis. Furthermore, prospective acquisitions were demonstrated in 5 healthy subjects and 5 asymptomatic smokers. Results Each image with size of 96 × 84 could be reconstructed within 31 ms (mean absolute error was 4.35% and structural similarity was 0.7558). Compared with conventional compressed sensing MRI, the mean absolute error decreased by 17.92%, but the structural similarity increased by 6.33%. For ventilation defect percentage, there were no significant differences between the fully sampled and reconstructed images through the proposed algorithm (P = 0.932), but had significant correlations (r = 0.975; P < 0.001). The prospectively undersampled results validated a good agreement with fully sampled images, with no significant differences in ventilation defect percentage but significantly higher signal‐to‐noise ratio values. Conclusion The proposed algorithm outperformed classical undersampling methods, paving the way for future use of deep learning in real‐time and accurate reconstruction of gas MRI.

Objectives Multiple b-value gas diffusion-weighted MRI (DW-MRI) enables non-invasive and quantitative assessment of lung morphometry, but its long acquisition time is not well-tolerated by patients. We aimed to accelerate multiple b-value gas DW-MRI for lung morphometry using deep learning. Methods A deep cascade of residual dense network (DC-RDN) was developed to reconstruct high-quality DW images from highly undersampled k-space data. Hyperpolarized 129 Xe lung ventilation images were acquired from 101 participants and were retrospectively collected to generate synthetic DW-MRI data to train the DC-RDN. Afterwards, the performance of the DC-RDN was evaluated on retrospectively and prospectively undersampled multiple b-value 129 Xe MRI datasets. Results Each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms. For the retrospective test data, the DC-RDN showed significant improvement on all quantitative metrics compared with the conventional reconstruction methods (p < 0.05). The apparent diffusion coefficient (ADC) and morphometry parameters were not significantly different between the fully sampled and DC-RDN reconstructed images (p > 0.05). For the prospectively accelerated acquisition, the required breath-holding time was reduced from 17.8 to 4.7 s with an acceleration factor of 4. Meanwhile, the prospectively reconstructed results showed good agreement with the fully sampled images, with a mean difference of −0.72% and −0.74% regarding global mean ADC and mean linear intercept (L m ) values. Conclusions DC-RDN is effective in accelerating multiple b-value gas DW-MRI while maintaining accurate estimation of lung microstructural morphometry, facilitating the clinical potential of studying lung diseases with hyperpolarized DW-MRI. Key Points• The deep cascade of residual dense network allowed fast and high-quality reconstruction of multiple b-value gas diffusionweighted MRI at an acceleration factor of 4. • The apparent diffusion coefficient and morphometry parameters were not significantly different between the fully sampled images and the reconstructed results (p > 0.05). • The required breath-holding time was reduced from 17.8 to 4.7 s and each slice with size of 64 × 64 × 5 could be reconstructed within 7.2 ms.

Nano contrast agents (Nano CA) are nanomaterials used to increase contrast in the medical magnetic resonance imaging (MRI). However, the related relaxation mechanism of the Nano CA is not clear yet and little significant breakthrough in relaxivity enhancement has been achieved. Herein, a new hydrophilic Gd-DOTA complex functionalized with different chain length of PEG was synthesized and incorporated into graphene quantum dots (GQD) to obtain paramagnetic graphene quantum dots (PGQD). We performed a variable-temperature and variable-field intensity NMR study in aqueous solution on the water exchange and rotational dynamics of three different chain lengths of PGQD. The optimal GQD with paramagnetic chain length shows a great improvement in performance on 1H NMR relaxometric studies. In vitro results demonstrated that the relaxivity of the designed PGQD could be controlled by regulating the PEG length, and its relaxivity was ∼16 times higher than that of current commercial MRI contrast agents (e.g., Gd-DTPA), on a “per Gd” basis. The relaxivity of the Nano CA can be rationally tuned to obtain unmatched potentials in MR imaging, exemplified by preparation of the paramagnetic GQD with the enhanced T 1 relaxivity. The fabricated PGQDs with suitable PEG length got the best relaxivity at 1.5 T. After intravenous injection, its feeding process by solid tumor could even be monitored by clinically used 1.5 T MRI scanners. This research will also provide an excellent platform for the design and synthesis of highly effective MR contrast agents.

Pulmonary diseases usually result in changes of the blood-gas exchange function in the early stages. Gas exchange across the respiratory membrane and gas diffusion in the alveoli can be quantified using hyperpolarized 129 Xe MR via chemical shift saturation recovery (CSSR) and diffusion-weighted imaging (DWI), respectively.Generally, CSSR and DWI data have been collected in separate breaths in humans.Unfortunately, the lung inflation level cannot be the exactly same in different breaths, which causes fluctuations in blood-gas exchange and pulmonary microstructure. Here we combine CSSR and DWI obtained with compressed sensing, to evaluate the gas diffusion and exchange function within a single breath-hold in humans. A new parameter, namely the perfusion factor of the respiratory membrane (SVR d/g ), is proposed to evaluate the gas exchange function. Hyperpolarized 129 Xe MR data are compared with pulmonary function tests and computed tomography examinations in healthy young, age-matched control, and chronic obstructive pulmonary disease human cohorts. SVR d/g decreases as the ventilation impairment and emphysema index increase. Our results indicate that the proposed method has the potential to detect the extent of lung parenchyma destruction caused by age and pulmonary diseases, and it would be useful in the early diagnosis of pulmonary diseases in clinical practice.

No abstract

Objectives To visualize and quantitatively assess regional lung function of survivors of COVID-19 who were hospitalized using pulmonary free-breathing 1 H MRI. Methods A total of 12 healthy volunteers and 27 COVID-19 survivors (62.4 ± 8.1 days between infection and image acquisition) were recruited in this prospective study and performed chest 1 H MRI acquisitions with free tidal breathing. Then, conventional Fourier decomposition ventilation (FD-V) and global fractional ventilation (FV Global ) were analyzed. Besides, a modified PREFUL (mPREFUL) method was developed to adapt to COVID-19 survivors and generate dynamic ventilation maps and parameters. All the ventilation maps and parameters were analyzed using Student’s t -test. Pearson’s correlation and a Bland-Altman plot between FV Global and mPREFUL were analyzed. Results There was no significant difference between COVID-19 and healthy groups regarding a static FD-V map (0.47 ± 0.12 vs 0.42 ± 0.08; p = .233). However, mPREFUL demonstrated lots of regional high ventilation areas (high ventilation percentage (HVP): 23.7% ± 10.6%) existed in survivors. This regional heterogeneity (i.e., HVP) in survivors was significantly higher than in healthy volunteers ( p = .003). The survivors breathed deeper (flow-volume loop: 5375 ± 3978 vs 1688 ± 789; p = .005), and breathed more air in respiratory cycle (total amount: 62.6 ± 19.3 vs 37.3 ± 9.9; p < .001). Besides, mPREFUL showed both good Pearson’s correlation ( r = 0.74; p < .001) and Bland-Altman consistency (mean bias = −0.01) with FV Global . Conclusions Dynamic ventilation imaging using pulmonary free-breathing 1 H MRI found regional abnormity of dynamic ventilation function in COVID-19 survivors. Key Points • Pulmonary free-breathing 1 H MRI was used to visualize and quantitatively assess regional lung ventilation function of COVID-19 survivors. • Dynamic ventilation maps generated from 1 H MRI were more sensitive to distinguish the COVID-19 and healthy groups (total air amount: 62.6 ± 19.3 vs 37.3 ± 9.9; p < .001), compared with static ventilation maps (FD-V value: 0.47 ± 0.12 vs 0.42 ± 0.08; p = .233). • COVID-19 survivors had larger regional heterogeneity (high ventilation percentage: 23.7% ± 10.6% vs 13.1% ± 7.9%; p = .003), and breathed deeper (flow-volume loop: 5375 ± 3978 vs 1688 ± 789; p = .005) than healthy volunteers. Supplementary Information The online version c...