Multi-energy CT material decomposition using graph model improved CNN.

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD).

A novel fluorescent probe of alkyne compounds for putrescine detection based on click reaction.

Putrescine is a toxic biogenic amine produced in the process of food spoilage, and a high concentration of biogenic amines in foods will cause health problems such as abnormal blood pressure, headaches and tachycardia asthma/worsening asthma. The detection of putrescine is necessary. However, traditional putrescine detection requires specialized instruments and complex operations.

A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.

Purpose: X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images.

An x-ray crosstalk correction method using FCNN for a novel energy resolving scheme in spectral CT.

Spectral computed tomography has great potential for multi-energy imaging and anti-artifacts. The complete absorption-based energy resolving scheme of x-rays has been used for the integrity of detected information. However, this scheme is limited by the fact that the detector pixel thickness is high and fixed.