Dual-Signal Chemical Exchange Saturation Transfer (Dusi-CEST): An Efficient Strategy for Visualizing Drug Delivery Monitoring in Living Cells.

We report a dual-signal chemical exchange saturation transfer (Dusi-CEST) strategy for drug delivery and detection in living cells. The two signals can be detected by operators in complex environments. This strategy is demonstrated on a cucurbit[6]uril (CB[6]) nanoparticle probe, as an example. The CB[6] probe is equipped with two kinds of hydrophobic cavities: one is found inside CB[6] itself, whereas the other exists inside the nanoparticle. When the probe is dispersed in aqueous solution as part of a hyperpolarized 129Xe NMR experiment, two signals appear at two different chemical shifts (100 and 200 ppm). These two resonances correspond to the NMR signals of 129Xe in the two different cavities. Upon loading with hydrophobic drugs, such as paclitaxel, for intracellular drug delivery, the two resonances undergo significant changes upon drug loading and cargo release, giving rise to a metric enabling the assessment of drug delivery success. The simultaneous change of Dusi-CEST likes a mobile phone that can receive both LTE and Wi-Fi signals, which can help reduce the occurrence of false positives and false negatives in complex biological environments and help improve the accuracy and sensitivity of single-shot detection.

Dynamic evaluation of acute lung injury using hyperpolarized 129 Xe magnetic resonance.

Prognosticating acute lung injury (ALI) is challenging, in part because of a lack of sensitive biomarkers. Hyperpolarized gas magnetic resonance (MR) has unique advantages in pulmonary function measurement and can provide promising biomarkers for the assessment of lung injuries. Herein, we employ hyperpolarized 129 Xe MRI and generate a number of imaging biomarkers to detect the pulmonary physiological and morphological changes during the progression of ALI in an animal model. We find the measured ratio of 129 Xe in red blood cells to interstitial tissue/plasma (RBC/TP) is significantly lower in the ALI group on the second (0.32 ± 0.03, p = 0.004), seventh (0.23 ± 0.03, p < 0.001), and 14th (0.29 ± 0.04, p = 0.001) day after lipopolysaccharide treatment compared with that in the control group (0.41 ± 0.04). In addition, significant differences are also observed for RBC/TP measurements between the second and seventh day (p = 0.001) and between the seventh and 14th day (p = 0.018) in the ALI group after treatment. Besides RBC/TP, significant differences are also observed in the measured exchange time constant (T) on the second (p = 0.038) and seventh day (p = 0.009) and in the measured apparent diffusion coefficient (ADC) and alveolar surface-to-volume ratio (SVR) on the 14th day (ADC: p = 0.009 and SVR: p = 0.019) after treatment in the ALI group compared with that in the control group. These findings indicate that the parameters measured with 129 Xe MR can detect the dynamic changes in pulmonary structure and function in an ALI animal model.

Photoactivated Nanohybrid for Dual-Nuclei MR/US/PA Multimodal-Guided Photothermal Therapy.

Nanohybrids have gained immense popularity for the diagnosis and chemotherapy of lung cancer for their excellent biocompatibility, biodegradability, and targeting ability. However, most of them suffer from limited imaging information, low tumor-to-background ratios, and multidrug resistance, limiting their potential clinical application. Herein, we engineered a photoresponsive nanohybrid by assembling polypyrrole@bovine serum albumin (PPy@BSA) encapsulating perfluoropentane (PFP)/129Xe for selective magnetic resonance (MR)/ultrasonic (US)/photoacoustic (PA) trimodal imaging and photothermal therapy of lung cancer, overcoming these drawbacks of single imaging modality and chemotherapy. The nanohybrid exhibited superior US, PA, and MR multimodal imaging performance for lung cancer detection. The high sensitivity of the nanohybrid to near-infrared light (NIR) resulted in a rapid increase in temperature in a low-intensity laser state, which initiated the phase transition of liquid PFP into the gas. The ultrasound signal inside the tumor, which is almost zero initially, is dramatically increased. Beyond this, it led to the complete depression of 19F/129Xe Hyper-CEST (chemical exchange saturation transfer) MRI during laser irradiation, which can precisely locate lung cancer. In vitro and in vivo results of the nanohybrid exhibited a successful therapeutic effect on lung cancer. Under the guidance of imaging results, a sound effect of photothermal therapy (PTT) for lung cancer was achieved. We expect this nanohybrid and photosensitive behavior will be helpful as fundamental tools to decipher lung cancer in an earlier stage through trimodality imaging methods.

Abnormal dynamic ventilation function of COVID-19 survivors detected by pulmonary free-breathing proton MRI.

OBJECTIVES: To visualize and quantitatively assess regional lung function of survivors of COVID-19 who were hospitalized using pulmonary free-breathing 1H 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 1H MRI acquisitions with free tidal breathing. Then, conventional Fourier decomposition ventilation (FD-V) and global fractional ventilation (FVGlobal) 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 FVGlobal 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 FVGlobal. CONCLUSIONS: Dynamic ventilation imaging using pulmonary free-breathing 1H MRI found regional abnormity of dynamic ventilation function in COVID-19 survivors. KEY POINTS: • Pulmonary free-breathing1H MRI was used to visualize and quantitatively assess regional lung ventilation function of COVID-19 survivors. • Dynamic ventilation maps generated from 1H 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.

Accelerate gas diffusion-weighted MRI for lung morphometry with deep learning.

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 129Xe 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 129Xe 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 (Lm) 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 diffusion-weighted 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.

AAV13 Enables Precise Targeting of Local Neural Populations.

As powerful tools for local gene delivery, adeno-associated viruses (AAVs) are widely used for neural circuit studies and therapeutical purposes. However, most of them have the characteristics of large diffusion range and retrograde labeling, which may result in off-target transduction during in vivo application. Here, in order to achieve precise gene delivery, we screened AAV serotypes that have not been commonly used as gene vectors and found that AAV13 can precisely transduce local neurons in the brain, with a smaller diffusion range than AAV2 and rigorous anterograde labeling. Then, AAV13-based single-viral and dual-viral strategies for sparse labeling of local neurons in the brains of C57BL/6 or Cre transgenic mice were developed. Additionally, through the neurobehavioral test in the ventral tegmental area, we demonstrated that AAV13 was validated for functional monitoring by means of carrying Cre recombinase to drive the expression of Cre-dependent calcium-sensitive indicator. In summary, our study provides AAV13-based toolkits for precise local gene delivery, which can be used for in situ small nuclei targeting, sparse labeling and functional monitoring.

Improvement in the signal amplitude and bandwidth of an optical atomic magnetometer via alignment-to-orientation conversion.

We evaluated the alignment-to-orientation conversion (AOC) at the cesium D1 line to improve a nonlinear magneto-optical rotation (NMOR) optical atomic magnetometer's signal amplitude and bandwidth. For the 6 2S1/2 F = 3 → 6 2P1/2 F' = 4 transition, the AOC-related NMOR achieves a 1.7-fold enhancement in signal amplitude compared to the conventional NMOR, benefiting from narrow linewidth and ultraweak power broadening. Therefore, an effective amplitude-to-linewidth ratio is maintained in the high-laser-power region. This method is beneficial for detecting high-frequency magnetic signals in nuclear magnetic resonance and biomagnetism, as the NMOR magnetometer bandwidth increases with laser power.

Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging.

PURPOSE: In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT. METHODS: COVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions. RESULTS: A total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2-12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R2 = 0.80; P < 0.001) and good consistency in the Bland-Altman plot (mean bias = 0.04 cm3). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R2 = 0.76; P < 0.001) with true lesion enlargements. CONCLUSION: The present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance.

Quantitative evaluation of lung injury caused by PM2.5 using hyperpolarized gas magnetic resonance.

PURPOSE: To demonstrate the feasibility of 129 Xe MR in evaluating the pulmonary physiological changes caused by PM2.5 in animal models. METHODS: Six rats were treated with PM2.5 solution (16.2 mg/kg) by intratracheal instillation twice a week for 4 weeks, and another six rats treated with normal saline served as the control cohort. Pulmonary function tests, hyperpolarized 129 Xe multi-b diffusion-weighted imaging, and chemical shift saturation recovery MR spectroscopy were performed on all rats, and the pulmonary structure and functional parameters were obtained from hyperpolarized 129 Xe MR data. Additionally, histological analysis was performed on all rats to evaluate alveolar septal thickness. Statistical analysis of all the obtained parameters was performed using unpaired 2-tailed t tests. RESULTS: Compared with the control group, the measured exchange time constant increased from 11.74 ± 2.39 to 14.00 ± 2.84 ms (P < .05), and the septal wall thickness increased from 6.17 ± 0.48 to 6.74 ± 0.52 µm (P < .05) in the PM2.5 cohort by 129 Xe MR spectroscopy, which correlated well with that obtained using quantitative histology (increased from 5.52 ± 0.32 to 6.20 ± 0.36 µm). Additionally, the mean TP/GAS ratio increased from 0.828 ± 0.115 to 1.019 ± 0.140 in the PM2.5 cohort (P = .021). CONCLUSIONS: Hyperpolarized 129 Xe MR could quantify the changes in gas exchange physiology caused by PM2.5 , indicating that the technique has the potential to be a useful tool for evaluation of pulmonary injury caused by air pollution in the future.

Fast and accurate reconstruction of human lung gas MRI with deep learning.

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 129 Xe 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.

Single breath-hold measurement of pulmonary gas exchange and diffusion in humans with hyperpolarized 129 Xe MR.

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 (SVRd/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. SVRd/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.

Quantitative evaluation of pulmonary gas-exchange function using hyperpolarized 129 Xe CEST MRS and MRI.

Hyperpolarized 129 Xe gas MR has been a powerful tool for evaluating pulmonary structure and function due to the extremely high enhancement in spin polarization, the good solubility in the pulmonary parenchyma, and the excellent chemical sensitivity to its surrounding environment. Generally, the quantitative structural and functional information of the lung are evaluated using hyperpolarized 129 Xe by employing the techniques of chemical shift saturation recovery (CSSR) and xenon polarization transfer contrast (XTC). Hyperpolarized 129 Xe chemical exchange saturation transfer (Hyper-CEST) is another method for quantifying the exchange information of hyperpolarized 129 Xe by using the exchange of xenon signals according to its different chemical shifts, and it has been widely used in biosensor studies in vitro. However, the feasibility of using hyperpolarized 129 Xe CEST to quantify the pulmonary gas exchange function in vivo is still unclear. In this study, the technique of CEST was used to quantitatively evaluate the gas exchange in the lung globally and regionally via hyperpolarized 129 Xe MRS and MRI, respectively. A new parameter, the pulmonary apparent gas exchange time constant (Tapp ), was defined, and it increased from 0.63 s to 0.95 s in chronic obstructive pulmonary disease (COPD) rats (induced by cigarette smoke and lipopolysaccharide exposure) versus the controls with a significant difference (P = 0.001). Additionally, the spatial distribution maps of Tapp in COPD rats' pulmonary parenchyma showed a regionally obvious increase compared with healthy rats. These results indicated that hyperpolarized 129 Xe CEST MR was an effective method for globally and regionally quantifying the pulmonary gas exchange function, which would be helpful in diagnosing lung diseases that are related to gas exchange, such as COPD.

Detection of smoke-induced pulmonary lesions by hyperpolarized 129 Xe diffusion kurtosis imaging in rat models.

PURPOSE: To demonstrate that hyperpolarized (HP) xenon diffusion kurtosis imaging (DKI) is able to detect smoke-induced pulmonary lesions in rat models. METHODS: Multi-b DKI with hyperpolarized xenon was used for the first time in five smoke-exposed rats and five healthy rats. Additionally, DKI with b values of up to 80 s/cm2 were used in two healthy rats to probe the critical b value (a limit beyond which the DKI cannot describe the non-Gaussian diffusion). RESULTS: The mean apparent diffusion coefficient (Dapp ) and diffusion kurtosis (Kapp ) extracted by the DKI model revealed significant changes in the smoke-exposed rats compared with those in the control group (P = 0.027 and 0.039, respectively), exhibiting strong correlations with mean linear intercept (Lm ) from the histology. Although the maximum b value was increased to 80 s/cm2 , the DKI could still describe the non-Gaussian diffusion (R2 > 0.97). CONCLUSION: DKI with hyperpolarized xenon exhibited sensitivity in the detection of pulmonary lesions induced by smoke, including moderate emphysema and small airway diseases. The critical b value was rarely exceeded in DKI of the lungs due to the limited gradient strength of the MRI scanner used in our study. Magn Reson Med 78:1891-1899, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Simultaneous assessment of both lung morphometry and gas exchange function within a single breath-hold by hyperpolarized 129 Xe MRI.

During the measurement of hyperpolarized 129 Xe magnetic resonance imaging (MRI), the diffusion-weighted imaging (DWI) technique provides valuable information for the assessment of lung morphometry at the alveolar level, whereas the chemical shift saturation recovery (CSSR) technique can evaluate the gas exchange function of the lungs. To date, the two techniques have only been performed during separate breaths. However, the request for multiple breaths increases the cost and scanning time, limiting clinical application. Moreover, acquisition during separate breath-holds will increase the measurement error, because of the inconsistent physiological status of the lungs. Here, we present a new method, referred to as diffusion-weighted chemical shift saturation recovery (DWCSSR), in order to perform both DWI and CSSR within a single breath-hold. Compared with sequential single-breath schemes (namely the 'CSSR + DWI' scheme and the 'DWI + CSSR' scheme), the DWCSSR scheme is able to significantly shorten the breath-hold time, as well as to obtain high signal-to-noise ratio (SNR) signals in both DWI and CSSR data. This scheme enables comprehensive information on lung morphometry and function to be obtained within a single breath-hold. In vivo experimental results demonstrate that DWCSSR has great potential for the evaluation and diagnosis of pulmonary diseases.

Detection of the mild emphysema by quantification of lung respiratory airways with hyperpolarized xenon diffusion MRI.

PURPOSE: To demonstrate the feasibility to quantify the lung respiratory airway in vivo with hyperpolarized xenon diffusion magnetic resonance imaging (MRI), which is able to detect mild emphysema in the rat model. MATERIALS AND METHODS: The lung respiratory airways were quantified in vivo using hyperpolarized xenon diffusion MRI (7T) with eight b values (5, 10, 15, 20, 25, 30, 35, 40 s/cm2 ) in five control rats and five mild emphysematous rats, which were induced by elastase. The morphological results from histology were acquired and used for comparison. RESULTS: The parameters DL (longitudinal diffusion coefficient), r (internal radius), h (alveolar sleeve depth), Lm (mean linear intercept), and S/V (surface area to lung volume ratio) derived from the hyperpolarized xenon diffusion MRI in the emphysematous group showed significant differences from those in the control group (P < 0.05). Additionally, these parameters correlated well with the Lm obtained by the traditional histological sections (Pearson's correlation coefficients >0.8). CONCLUSION: The lung respiratory airways can be quantified by hyperpolarized xenon diffusion MRI, showing the potential for mild emphysema diagnosis. Also, the study suggested that the hyperpolarized xenon DL is more sensitive than DT (transverse diffusion coefficient) to detect mild emphysema. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:879-888.

Quantitative evaluation of radiation-induced lung injury with hyperpolarized xenon magnetic resonance.

PURPOSE: To demonstrate the feasibility of quantitative and comprehensive global evaluation of pulmonary function and microstructural changes in rats with radiation-induced lung injury (RILI) using hyperpolarized xenon MR. METHODS: Dissolved xenon spectra were dynamically acquired using a modified chemical shift saturation recovery pulse sequence in five rats with RILI (bilaterally exposed by 6-MV x-ray with a dose of 14 Gy 3 mo. prior to MR experiments) and five healthy rats. The dissolved xenon signals were quantitatively analyzed, and the pulmonary physiological parameters were extracted with the model of xenon exchange. RESULTS: The obtained pulmonary physiological parameters and the ratio of (129) Xe signal in red blood cells (RBCs) versus barrier showed a significant difference between the groups. In RILI rats versus controls, the exchange time increased from 44.5 to 112 ms, the pulmonary capillary transit time increased from 0.51 to 1.48 s, and the ratio of (129) Xe spectroscopic signal in RBCs versus barrier increased from 0.294 to 0.484. CONCLUSION: Hyperpolarized xenon MR is effective for quantitative and comprehensive global evaluation of pulmonary function and structural changes without the use of radiation. This may open the door for its use in the diagnosis of lung diseases that are related to gas exchange. Magn Reson Med 76:408-416, 2016. © 2015 Wiley Periodicals, Inc.

Oxygen-dependent hyperpolarized (129) Xe brain MR.

Hyperpolarized (HP) (129) Xe MR offers unique advantages for brain functional imaging (fMRI) because of its extremely high sensitivity to different chemical environments and the total absence of background noise in biological tissues. However, its advancement and applications are currently plagued by issues of signal strength. Generally, xenon atoms found in the brain after inhalation are transferred from the lung via the bloodstream. The longitudinal relaxation time (T1 ) of HP (129) Xe is inversely proportional to the pulmonary oxygen concentration in the lung because oxygen molecules are paramagnetic. However, the T1 of (129) Xe is proportional to the pulmonary oxygen concentration in the blood, because the higher pulmonary oxygen concentration will result in a higher concentration of diamagnetic oxyhemoglobin. Accordingly, there should be an optimal pulmonary oxygen concentration for a given quantity of HP (129) Xe in the brain. In this study, the relationship between pulmonary oxygen concentration and HP (129) Xe signal in the brain was analyzed using a theoretical model and measured through in vivo experiments. The results from the theoretical model and experiments in rats are found to be in good agreement with each other. The optimal pulmonary oxygen concentration predicted by the theoretical model was 21%, and the in vivo experiments confirmed the presence of such an optimal ratio by reporting measurements between 25% and 35%. These findings are helpful for improving the (129) Xe signal in the brain and make the most of the limited spin polarization available for brain experiments. Copyright © 2016 John Wiley & Sons, Ltd.

Atomic filter based on stimulated Raman transition at the rubidium D1 line.

We report on a 795 nm atomic filter consisting of a stimulated Raman gain amplifier together with normal Faraday anomalous dispersion optical filtering (FADOF) at the rubidium D1 line. The filter is operated with a single transmission peak. The gain of the filter's transmission light signal is enhanced up to 85-fold compared to case operating without a stimulated Raman transition. Based on atomic coherence, the filter's minimum transmission bandwidth is less than 22 MHz. In each filtering channel, the signal light's frequency can be tuned by changing the detuning of the coupling light. Such a filter with stimulated Raman gain is more efficient in extracting weak signals in the presence of a strong light background compared with the normal FADOF. This expands the range of potential applications in optical communications and lidar technology. This filtering method can also be extended to the lines of other atoms.

Complementation-reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration.

BACKGROUND: Hyperpolarized (HP) gas MRI enables the clear visualization of lung structure and function. Clinically relevant biomarkers, such as ventilated defect percentage (VDP) derived from this modality can quantify lung ventilation function. However, long imaging time leads to image quality degradation and causes discomfort to the patients. Although accelerating MRI by undersampling k-space data is available, accurate reconstruction and segmentation of lung images are quite challenging at high acceleration factors. PURPOSE: To simultaneously improve the performance of reconstruction and segmentation of pulmonary gas MRI at high acceleration factors by effectively utilizing the complementary information in different tasks. METHODS: A complementation-reinforced network is proposed, which takes the undersampled images as input and outputs both the reconstructed images and the segmentation results of lung ventilation defects. The proposed network comprises a reconstruction branch and a segmentation branch. To effectively exploit the complementary information, several strategies are designed in the proposed network. Firstly, both branches adopt the encoder-decoder architecture, and their encoders are designed to share convolutional weights for facilitating knowledge transfer. Secondly, a designed feature-selecting block discriminately feeds shared features into decoders of both branches, which can adaptively pick suitable features for each task. Thirdly, the segmentation branch incorporates the lung mask obtained from the reconstructed images to enhance the accuracy of the segmentation results. Lastly, the proposed network is optimized by a tailored loss function that efficiently combines and balances these two tasks, in order to achieve mutual benefits. RESULTS: Experimental results on the pulmonary HP 129 Xe MRI dataset (including 43 healthy subjects and 42 patients) show that the proposed network outperforms state-of-the-art methods at high acceleration factors (4, 5, and 6). The peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and Dice score of the proposed network are enhanced to 30.89, 0.875, and 0.892, respectively. Additionally, the VDP obtained from the proposed network has good correlations with that obtained from fully sampled images (r = 0.984). At the highest acceleration factor of 6, the proposed network promotes PSNR, SSIM, and Dice score by 7.79%, 5.39%, and 9.52%, respectively, in comparison to the single-task models. CONCLUSION: The proposed method effectively enhances the reconstruction and segmentation performance at high acceleration factors up to 6. It facilitates fast and high-quality lung imaging and segmentation, and provides valuable support in the clinical diagnosis of lung diseases.

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI.

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep convolutional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 complex CNN employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully sampled images. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care.