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.

Cerebral small vessel disease: Pathological mechanisms and potential therapeutic targets.

Cerebral small vessel disease (CSVD) represents a diverse cluster of cerebrovascular diseases primarily affecting small arteries, capillaries, arterioles and venules. The diagnosis of CSVD relies on the identification of small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, and microbleeds using neuroimaging. CSVD is observed in 25% of strokes worldwide and is the most common pathology of cognitive decline and dementia in the elderly.

Transformed ℓ regularization for learning sparse deep neural networks.

Deep Neural Networks (DNNs) have achieved extraordinary success in numerous areas. However, DNNs often carry a large number of weight parameters, leading to the challenge of heavy memory and computation costs. Overfitting is another challenge for DNNs when the training data are insufficient.

Feature Selection With $\ell_{2,1-2}$ Regularization.

Feature selection aims to select a subset of features from high-dimensional data according to a predefined selecting criterion. Sparse learning has been proven to be a powerful technique in feature selection. Sparse regularizer, as a key component of sparse learning, has been studied for several years.

Restaurant emissions removal by a biofilter with immobilized bacteria.

Pseudomonas sp. ZD8 isolated from contaminated soil was immobilized with platane wood chips to produce packing materials for a novel biofilter system utilized to control restaurant emissions. The effects of operational parameters including retention time, temperature, and inlet gas concentration on the removal efficiency and elimination capacity were evaluated.