Incorporation of Prior Knowledge of Signal Behavior Into the Reconstruction to Accelerate the Acquisition of Diffusion MRI Data

Pulmonary delivery of nano-particles for lung cancer diagnosis and therapy: Recent advances and future prospects

Although our understanding of lung cancer has significantly improved in the past decade, it is still a disease with a high incidence and mortality rate. The key reason is that the efficacy of the therapeutic drugs is limited, mainly due to insufficient doses of drugs delivered to the lungs. To achieve precise lung cancer diagnosis and treatment, nano-particles (NPs) pulmonary delivery techniques have attracted much attention and facilitate the exploration of the potential of those in inhalable NPs targeting tumor lesions. Since the therapeutic research focusing on pulmonary delivery NPs has rapidly developed and evolved substantially, this review will mainly discuss the current developments of pulmonary delivery NPs for precision lung cancer diagnosis and therapy. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Respiratory Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Undersampled Diffusion-Weighted 129Xe MRI Morphometry of Airspace Enlargement: Feasibility in Chronic Obstructive Pulmonary Disease

Multi-b diffusion-weighted hyperpolarized gas MRI measures pulmonary airspace enlargement using apparent diffusion coefficients (ADC) and mean linear intercepts (L_m). Rapid single-breath acquisitions may facilitate clinical translation, and, hence, we aimed to develop single-breath three-dimensional multi-b diffusion-weighted ¹²⁹Xe MRI using k-space undersampling. We evaluated multi-b (0, 12, 20, 30 s/cm²) diffusion-weighted ¹²⁹Xe ADC/morphometry estimates using a fully sampled and retrospectively undersampled k-space with two acceleration-factors (AF = 2 and 3) in never-smokers and ex-smokers with chronic obstructive pulmonary disease (COPD) or alpha-one anti-trypsin deficiency (AATD). For the three sampling cases, mean ADC/L_m values were not significantly different (all p > 0.5); ADC/L_m values were significantly different for the COPD subgroup (0.08 cm²s^-1/580 µm, AF = 3; all p < 0.001) as compared to never-smokers (0.05 cm²s^-1/300 µm, AF = 3). For never-smokers, mean differences of 7%/7% and 10%/7% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and L_m values, respectively. For the COPD subgroup, mean differences of 3%/4% and 11%/10% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and L_m, respectively. There was no relationship between acceleration factor with ADC or L_m (p = 0.9); voxel-wise ADC/L_m measured using AF = 2 and AF = 3 were significantly and strongly related to fully-sampled values (all p < 0.0001). Multi-b diffusion-weighted ¹²⁹Xe MRI is feasible using two different acceleration methods to measure pulmonary airspace enlargement using L_m and ADC in COPD participants and never-smokers.

Assessment of pulmonary morphometry using hyperpolarized 129 Xe diffusion-weighted MRI with variable-sampling-ratio compressed sensing patterns

BACKGROUND

Hyperpolarized (HP) ¹²⁹ Xe multiple b-values diffusion-weighted magnetic resonance imaging (DW-MRI) has been widely used for quantifying pulmonary microstructural morphometry. However, the technique requires long acquisition times, making it hard to apply in patients with severe pulmonary diseases, who cannot sustain long breath holds.

PURPOSE

To develop and evaluate the technique of variable-sampling-ratio compressed sensing (VCS) patterns for accelerating HP ¹²⁹ Xe multiple b-values DW-MRI in humans.

METHODS

Optimal variable sampling ratios and corresponding k-space undersampling patterns for each b-value were obtained by retrospective simulations based on the fully sampled (FS) DW-MRI dataset acquired from six young healthy volunteers. Then, the FS datasets were retrospectively undersampled using both VCS patterns and conventional compressed sensing (CS) pattern with a similar average acceleration factor. The quality of reconstructed images with retrospective VCS (rVCS) and CS (rCS) datasets were quantified using mean absolute error (MAE) and structural similarity (SSIM). Pulmonary morphometric parameters were also evaluated between rVCS and FS datasets. In addition, prospective VCS multiple b-values ¹²⁹ Xe DW-MRI datasets were acquired from 14 cigarette smokers and 13 age-matched healthy volunteers. The differences of lung morphological parameters obtained with the proposed method were compared between the groups using independent samples t-test. Pearson correlation coefficient was also utilized for evaluating the correlation of the pulmonary physiological parameters obtained with VCS DW-MRI and pulmonary function tests.

RESULTS

Lower MAE and higher SSIM values were found in the reconstructed images with rVCS measurement when compared to those using conventional rCS measurement. The details and quality of the images obtained with rVCS and FS measurements were found to be comparable. The mean values of the morphological parameters derived from rVCS and FS datasets showed no significant differences (p > 0.05), and the mean differences of measured acinar duct radius, mean linear intercept, surface-to-volume ratio, and apparent diffusion coefficient with cylinder model were -0.87%, -2.42%, 2.04%, and -0.50%, respectively. By using the VCS technique, significant differences were delineated between the pulmonary morphometric parameters of healthy volunteers and cigarette smokers (p < 0.001), while the acquisition time was reduced by four times.

CONCLUSION

A fourfold reduction in acquisition time was achieved using the proposed VCS method while preserving good image quality. Our preliminary results demonstrated that the proposed method can be used for evaluating pulmonary injuries caused by cigarette smoking and may prove to be helpful in diagnosing lung diseases in clinical practice.

Feasibility of Dynamic Inhaled Gas MRI-Based Measurements Using Acceleration Combined with the Stretched Exponential Model

Dynamic inhaled gas (³He/¹²⁹Xe/¹⁹F) MRI permits the acquisition of regional fractional-ventilation which is useful for detecting gas-trapping in lung-diseases such as lung fibrosis and COPD. Deninger's approach used for analyzing the wash-out data can be substituted with the stretched-exponential-model (SEM) because signal-intensity is attenuated as a function of wash-out-breath in ¹⁹F lung imaging. Thirteen normal-rats were studied using ³He/¹²⁹Xe and ¹⁹F MRI and the ventilation measurements were performed using two 3T clinical-scanners. Two Cartesian-sampling-schemes (Fast-Gradient-Recalled-Echo/X-Centric) were used to test the proposed method. The fully sampled dynamic wash-out images were retrospectively under-sampled (acceleration-factors (AF) of 10/14) using a varying-sampling-pattern in the wash-out direction. Mean fractional-ventilation maps using Deninger's and SEM-based approaches were generated. The mean fractional-ventilation-values generated for the fully sampled k-space case using the Deninger method were not significantly different from other fractional-ventilation-values generated for the non-accelerated/accelerated data using both Deninger and SEM methods (p > 0.05 for all cases/gases). We demonstrated the feasibility of the SEM-based approach using retrospective under-sampling, mimicking AF = 10/14 in a small-animal-cohort from the previously reported dynamic-lung studies. A pixel-by-pixel comparison of the Deninger-derived and SEM-derived fractional-ventilation-estimates obtained for AF = 10/14 (≤16% difference) has confirmed that even at AF = 14, the accuracy of the estimates is high enough to consider this method for prospective measurements.

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 (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 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.

Fast reconstruction of non-uniform sampling multidimensional NMR spectroscopy via a deep neural network

Multidimensional nuclear magnetic resonance (NMR) spectroscopy is used to examine the chemical structures of the studied systems. Unfortunately, the application of NMR spectra is limited by their long acquisition time, especially for 3D, 4D, and higher dimensional spectra. Non-uniform sampling (NUS) has been widely recognized as a powerful tool to reduce the NMR experimental time. But the quality of NUS spectra depends on appropriate reconstruction algorithms. As an effective data processing method, deep learning has been widely used in many fields in recent years. In this work, a deep learning-based strategy for fast reconstruction of non-uniform sampling NMR spectra is proposed. In our experiments, the proposed deep neural network has better performance in removing artifacts and preserving weak peaks than typical convolutional neural networks of U-Net and DenseNet. Besides, a novel approach of generating training data is utilized to reduce the computational burden of neural networks, and thus training our network can be easier and faster than previous deep learning-based works. Compared with the two currently available methods, SMILE and hmsIST, our strategy can provide comparable reconstruction quality in terms of peak intensities and the fidelity of peak shape. The reconstruction time of our methods is also comparable to or faster than the two methods, especially for 3D spectra.

Application of a stretched-exponential model for morphometric analysis of accelerated diffusion-weighted 129Xe MRI of the rat lung

OBJECTIVE

Diffusion-weighted, hyperpolarized ¹²⁹Xe MRI is useful for the characterization of microstructural changes in the lung. A stretched exponential model was proposed for morphometric extraction of the mean chord length (L_m) from diffusion-weighted data. The stretched exponential model enables accelerated mapping of L_m in a single-breathhold using compressed sensing. Our purpose was to compare L_m maps obtained from stretched-exponential model analysis of accelerated versus unaccelerated diffusion-weighted ¹²⁹Xe MRI data obtained from healthy/injured rat lungs.

MATERIAL AND METHODS

L_m maps were generated using a stretched-exponential model analysis of previously acquired fully sampled diffusion-weighted ¹²⁹Xe rat data (b values = 0 … 110 s/cm²) and compared to L_m maps generated from retrospectively undersampled data simulating acceleration factors of 7/10. The data included four control rats and five rats receiving whole-lung irradiation to mimic radiation-induced lung injury. Mean L_m obtained from the accelerated/unaccelerated maps were compared to histological mean linear intercept.

RESULTS

Accelerated L_m estimates were similar to unaccelerated L_m estimates in all rats, and similar to those previously reported (< 12% different). L_m was significantly reduced (p < 0.001) in the irradiated rat cohort (90 ± 20 µm/90 ± 20 µm) compared to the control rats (110 ± 20 µm/100 ± 15 µm) and agreed well with histological mean linear intercept.

DISCUSSION

Accelerated mapping of L_m using a stretched-exponential model analysis is feasible, accurate and agrees with histological mean linear intercept. Acceleration reduces scan time, thus should be considered for the characterization of lung microstructural changes in humans where breath-hold duration is short.

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 (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.

Highly and Adaptively Undersampling Pattern for Pulmonary Hyperpolarized 129Xe Dynamic MRI

Hyperpolarized (HP) gas (e.g., ³He or ¹²⁹Xe) dynamic MRI could visualize the lung ventilation process, which provides characteristics regarding lung physiology and pathophysiology. Compressed sensing (CS) is generally used to increase the temporal resolution of such dynamic MRI. Nevertheless, the acceleration factor of CS is constant, which results in difficulties in precisely observing and/or measuring dynamic ventilation process due to bifurcating network structure of the lung. Here, an adaptive strategy is proposed to highly undersample pulmonary HP dynamic k-space data, according to the characteristics of both lung structure and gas motion. After that, a valid reconstruction algorithm is developed to reconstruct dynamic MR images, considering the low-rank, global sparsity, gas-inflow effects, and joint sparsity. Both the simulation and the in vivo results verify that the proposed approach outperforms the state-of-the-art methods both in qualitative and quantitative comparisons. In particular, the proposed method acquires 33 frames within 6.67 s (more than double the temporal resolution of the recently proposed strategy), and achieves high-image quality [the improvements are 29.63%, 3.19%, 2.08%, and 13.03% regarding the mean absolute error (MAE), structural similarity index (SSIM), quality index based on local variance (QILV), and contrast-to-noise ratio (CNR) comparisons]. This provides accurate structural and functional information for early detection of obstructive lung diseases.