An Efficient Light-weight Network for Fast Reconstruction on MR Images.

BACKGROUND: In recent years, Deep Learning (DL) algorithms have emerged endlessly and achieved impressive performance, which makes it possible to accelerate Magnetic Resonance (MR) image reconstruction with DL instead of Compressed Sensing (CS) methods. However, a DL-based MR image reconstruction method has always suffered from its heavy learning parameters and poor generalization ability so far. Therefore, an efficient, light-weight network is still in desperate need of fast MR image reconstruction. METHODS: We propose an efficient and light-weight MR reconstruction network (named RecNet) that uses a Convolutional Neural Network (CNN) to fast reconstruct high-quality MR images. Specifically, the network is composed of cascade modules, and each cascade module is further divided into feature extraction blocks and a data consistency layer. The feature extraction block can not only effectively extract the features of MR images, but also do not introduce too many parameters for the whole network. To stabilize the training procedure, the correction information of image frequency is adopted in the Data Consistency (DC) layer. RESULTS: We have evaluated RecNet on a public dataset and the results show that the image quality reconstructed by RecNet is the best on the Peak Signal-To-Noise Ratio (PSNR) and structural similarity index (SSIM) evaluation standards. In addition, the pre-trained RecNet can also reconstruct high-quality MR images on an unseen dataset. CONCLUSION: The results demonstrate that the RecNet has superior reconstruction ability in various metrics than comparative methods. The RecNet can quickly generate high-quality MR images in fewer parameters. Furthermore, the RecNet has an excellent generalization ability on pathological images and different sampling rates data.

A hybrid convolutional neural network for super-resolution reconstruction of MR images.

PURPOSE: Spatial resolution is an important parameter for magnetic resonance imaging (MRI). High-resolution MR images provide detailed information and benefit subsequent image analysis. However, higher resolution MR images come at the expense of longer scanning time and lower signal-to-noise ratios (SNRs). Using algorithms to improve image resolution can mitigate these limitations. Recently, some convolutional neural network (CNN)-based super-resolution (SR) algorithms have flourished on MR image reconstruction. However, most algorithms usually adopt deeper network structures to improve the performance. METHODS: In this study, we propose a novel hybrid network (named HybridNet) to improve the quality of SR images by increasing the width of the network. Specifically, the proposed hybrid block combines a multipath structure and variant dense blocks to extract abundant features from low-resolution images. Furthermore, we fully exploit the hierarchical features from different hybrid blocks to reconstruct high-quality images. RESULTS: All SR algorithms are evaluated using three MR image datasets and the proposed HybridNet outperformed the comparative methods with peak a signal-to-noise ratio (PSNR) of 42.12 ± 0.92 dB, 38.60 ± 2.46 dB, 35.17 ± 2.96 dB and a structural similarity index (SSIM) of 0.9949 ± 0.0015, 0.9892 ± 0.0034, 0.9740 ± 0.0064, respectively. Besides, our proposed network can reconstruct high-quality images on an unseen MR dataset with PSNR of 33.27 ± 1.56 and SSIM of 0.9581 ± 0.0068. CONCLUSIONS: The results demonstrate that HybridNet can reconstruct high-quality SR images from degraded MR images and has good generalization ability. It also can be leveraged to assist the task of image analysis or processing.

Medium-chain triglyceride/water Pickering emulsion stabilized by phosphatidylcholine-kaolinite for encapsulation and controlled release of curcumin.

Pickering emulsions have received widespread attention for encapsulating lipophilic guests in the biomedical and food fields. However, control of the stabilities and demulsification of Pickering emulsions to allow the release of encapsulated species remains a challenge in gastrointestinal conditions. In this work, phosphatidylcholine-kaolinite was prepared by modification of natural kaolinite with phosphatidylcholine and was used as an emulsifier to stabilize medium-chain triglyceride (MCT)/water Pickering emulsions for encapsulating curcumin, a natural antioxidant drug. Simulated gastric and intestinal digestion and a cell uptake assay were implemented for the curcumin-loaded MCT/water Pickering emulsion to study its demulsification and the bioavailability of curcumin. The results revealed that the wettability of phosphatidylcholine-kaolinite could be tailored by controlling the modification temperature so that it could control the emulsion stability. The prepared phosphatidylcholine-kaolinite, with a three-phase contact angle of 123°, was an optimal emulsifier for the enhanced stabilization of the MCT/water Pickering emulsion, especially in the presence of gastric acid. The phosphatidylcholine-kaolinite distributed at the water-oil interface and formed a dense shell structure on the surfaces of the emulsion droplets, controlling the demulsification efficiency to release the encapsulated curcumin. Only 18.9% of the curcumin was released in the simulated gastric conditions after 120 min of digestion due to the demulsification of the MCT/water Pickering emulsion, while it was completely released after 150 min of digestion in simulated intestinal conditions, as expected. This Pickering emulsion stabilized by phosphatidylcholine-kaolinite is a promising delivery system for lipophilic foods or drugs to enhance their bioavailability.