DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction.

Magnetic resonance imaging (MRI) is non-invasive and crucial for clinical diagnosis, but it has long acquisition time and aliasing artifacts. Accelerated imaging techniques can effectively reduce the scanning time of MRI, thereby decreasing the anxiety and discomfort of patients. Vision Transformer (ViT) based methods have greatly improved MRI image reconstruction, but their computational complexity and memory requirements for the self-attention mechanism grow quadratically with image resolution, which limits their use for high resolution images.

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction.

Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image domain reconstruction with conventional CNN-based structures by full supervision.

DC-SiamNet: Deep contrastive Siamese network for self-supervised MRI reconstruction.

Reconstruction methods based on deep learning have greatly shortened the data acquisition time of magnetic resonance imaging (MRI). However, these methods typically utilize massive fully sampled data for supervised training, restricting their application in certain clinical scenarios and posing challenges to the reconstruction effect when high-quality MR images are unavailable. Recently, self-supervised methods have been developed that only undersampled MRI images participate in the network training.

A Multifunctional Tb-MOF Detector for HO, Fe, CrO , and TPA Explosive Featuring Coexistence of Binuclear and Tetranuclear Clusters.

A novel three-dimensional microporous terbium(III) metal-organic framework (Tb-MOF) named as [Tb (DBA)(OH)(HO)]·(HO) (), was successfully obtained by a solvothermal method based on terbium nitrate and 5-di(2',4'-dicarboxylphenyl) benzoic acid (HDBA). The Tb-MOF has been characterized by single crystal X-ray diffraction, elemental analysis, thermogravimetry, and fluorescence properties, and the purity was further confirmed by powder X-ray diffraction (PXRD) analysis. Structural analysis shows that there are two kinds of metal cluster species: binuclear and tetranuclear, which are linked by HDBA ligands in two μ high coordination fashions into a three-dimensional microporous framework.