Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS: Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS: • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

Learning from synthetic data for reference-free Nyquist ghost correction and parallel imaging reconstruction of echo planar imaging.

BACKGROUND: Echo planar imaging (EPI) suffers from Nyquist ghost caused by eddy currents and other non-ideal factors. Deep learning has received interest for EPI ghost correction. However, large datasets with qualified labels are usually unavailable, especially for the under-sampled EPI data due to the imperfection of traditional ghost correction algorithms. PURPOSE: To develop a multi-coil synthetic-data-based deep learning method for the Nyquist ghost correction and reconstruction of under-sampled EPI. METHODS: Our network is trained purely with synthetic data. The labels of the training samples are generated by combining a public magnetic resonance imaging dataset and a few pre-collected coil sensitivity maps. The input is synthesized by under-sampling (for the accelerated case) and adding phase errors between the even and odd echoes of the label. To bridge the gap between synthetic data and data from real acquisition, linear and non-linear 2D phase errors are considered during the training data generation. RESULTS: The proposed method outperformed the existing mainstream approaches in several experiments. The average ghost-to-signal ratios of our/3-line navigator-based methods were 0.51%/5.36% and 0.42%/8.64% in fully-sampled and under-sampled in vivo experiments, respectively. In the sagittal experiments, our method successfully corrected higher-order and 2D phase errors. Our method also outperformed other reference-based methods on motion-corrupted data. In the simulation experiments, the peak signal-to-noise ratios were 37.6/38.3 dB for 2D linear/non-linear simulated phase errors, indicating that our method was consistently reliable for different kinds of phase errors. CONCLUSION: Our method achieves superb ghost correction and parallel imaging reconstruction without any calibration information, and can be readily adapted to other EPI-based applications.

Ultrasensitive airflow sensor prepared by electrostatic flocking for sound recognition and motion monitoring.

Recently, airflow sensors have attracted great attention due to their unique characteristics. However, the preparation of high-performance airflow sensors via extraordinarily simple, controllable and cost-effective methods remains a great challenge. Herein, inspired by the fluff system of the spider, an ultrasensitive fluffy-like airflow sensor with carbon fibers (CFs) uniformly and firmly planted on the surface of a polyvinyl alcohol (PVA) fibrous substrate has been easily fabricated using electrostatic flocking technology. The fluffy-like structure endows the airflow sensor with superior properties including ultra-sensitivity, fast response time (0.103 s), low airflow velocity detection limit (0.068 m s-1), ultra-sensitive detection in a wide airflow range (0.068-16 m s-1), and multi-directional consistent response to airflow. This sensor can be used to accurately recognize sound waves and voiceless speech and detect human and object motions in different postures and speeds. This work presents insights into designing and preparing high-performance airflow sensors on a large-scale for sound recognition, motion monitoring, and assisting the disabled.

Cone-shaped titanate immobilized on polyacrylonitrile nanofibers: hierarchical architecture for effective photocatalytic activity.

A new photocatalytic composite material, based on flexible functional polyacrylonitrile nanofibers (denoted as f-PAN NF), was developed by depositing composite layers of α-TiO2 and cone-shaped titanate (H2Ti5O11·3H2O) successively. The α-TiO2 coated on f-PAN NF as a seed accelerated the nucleation of titanate. Cone-shaped titanate deposited on α-TiO2@f-PAN NF tightly at 35 °C with the assistance of cyanuric acid via Ostwald ripening. Due to the uniform distribution of cone-shaped titanate, the photocatalytic performance of hybrid f-PAN NF was remarkable under LED light irradiation and yielded additional photocatalytic applications as well. In addition, the composite photocatalyst exhibited better reusability and retrievability because of the special design involving a bonding between the nanofibers and layers.

Fabrication of polyvinyl alcohol hydrogels with excellent shape memory and ultraviolet-shielding behavior via the introduction of tea polyphenols.

Shape-memory hydrogels are expected to be used not only in an ordinary environment, but also in some special environments, such as under ultraviolet (UV) irradiation. Developing novel shape-memory polyvinyl alcohol (PVA)/tea polyphenol (TP) hydrogels with UV shielding performance is realistically important in application fields. Herein, we designed functional PVA/TP hydrogels with excellent UV-shielding ability and improved the shape memory on hot water stimuli. This study shows that the abundant hydrogen bonds between PVA and TP are the source of shape memory. The PVA hydrogels with 8 wt% TP loading could approximately recover their original shape after deformation when immersed in water at 50 °C for 30 s. Meanwhile, the hydrogels also had excellent UV shielding capacity. After ageing under UV for 16 days, the observed shape of the hydrogel with 8 wt% TP loading retained 74.7% of the original, and the hydrogel could effectively protect the skin of mice from damage under 10 mW cm-2 UV irradiation. With the understanding of the UV-shielding behavior of hydrogels, this study has been able to generate biomedical materials for human skin protection, specifically skin covering the joint areas, where shape memory of the applied materials is essential.

Polystyrene-heterojunction semiconductor composite spheres prepared by a hydrothermal synthesis process: a recyclable photocatalyst under visible light irradiation for removing organic dyes from aqueous solution.

A composite photocatalyst, with a BiOI/TiO2 heterojunction supported on carboxyl polystyrene spheres, was prepared using a two step hydrothermal reaction method. The products were characterized by X-ray diffraction, scanning electron microscopy and energy-dispersive X-ray spectroscopy. The results show that the polystyrene microspheres were covered with smaller-sized BiOI microspheres, while nano-sized TiO2 particles were deposited on the surface of the flower-like BiOI microspheres. As an internal electric field exists when BiOI is hybridized with TiO2, the photogenerated carriers could be separated effectively and the resulting composite photocatalyst PS/BiOI/TiO2 had better performance for degrading rhodamine B in aqueous solution under visible-light irradiation with a 40 W incandescent lamp than that of PS/BiOI. Meaningfully, the composite photocatalyst can be recycled easily by filtering.

Preparation and luminescence properties of La6MoO12:Eu3+/PVA nanofibers by Pechini/electrospinning process.

The 20% concentration Eu3+-based red-emitting phosphor, nano-sized La6MoO12:Eu3+ was prepared by the Pechini method. X-ray diffraction (XRD), thermogravimetric and differential thermal analysis (TG-DTA), scanning electron microscopy (SEM), photoluminescence (PL), and decay curves were used to characterize the resulting samples. The phosphor can be efficiently excited by near UV light and exhibits an intense red luminescence corresponding to the electric dipole transition 5D0 --> 7F2 at 615 nm. When the phosphor was mixed into poly(vinyl alcohol) aqueous solution, the fluorescent nanofibers could be prepared by electrospinning process. It was suggested that the La6MoO12:Eu3+ phosphor would be a promising red component for solid-state lighting devices based on InGaN or GaN light-emitting diodes.