Rui Chen

Precise evaluation of the deviation of water molecule diffusion in the brain tissue from the Gaussian diffusion model is crucial for diagnosing brain disorders. Diffusion kurtosis imaging (DKI) is a technique that may accurately describe the non-Gaussian diffusion characteristics of water molecules in brain tissue. The traditional method for achieving diffusion kurtosis imaging is constrained weighted linear least squares estimation. However, the traditional constrained weighted linear least squares estimation method has greatly reduced its clinical practicability due to its excessively long data acquisition time. Consequently, we propose using a deep learning-based approach instead of the traditional constrained weighted linear least-squares estimation method. We suggest utilizing the DKI-Net model, which relies solely on 2D convolution, and the DKI-Transformer model, which has an improved self-attention mechanism module, to estimate DKI-derived metrics. DKI-Net with residual properties extracts accurate and rich blocks of feature information and highly estimates voxels. The DKI-Transformer model can extract global voxel correlation characteristics, the estimation results have a high peak signal-to-noise ratio and a high structural similarity index compared to the reference labeling and exhibit distinct boundaries of microscopic features. Compared with traditional methods, these methods utilize the strong fitting capabilities of deep learning to estimate high-quality rotationally invariant scalar measures using a small number of DWI data points, significantly decreasing the number of scans and the duration of scanning exponentially. This approach provides a perspective that promises to address the problem of long imaging times in diffusion kurtosis imaging (DKI), facilitating the resolution of the clinical limitations of DKI. Consequently, it is anticipated that the range of applications of DKI will broaden.

The particle size of powder coatings is an important factor affecting flow and fluidization performance, which in turn determines the quality of the coating. Powder coating particles, such as boron nitride, titanium dioxide, barium titanate, niobium oxide, and tungsten oxide, can be seen in SEM images as circular or polygonal shapes, with irregular edges and sizes, as well as aggregation and stacking between them. This paper introduces a novel method of automatic particle identification and size analysis applied in SEM images for the investigation of coating quality. Firstly, a fast gradient segmentation algorithm (Split–Merge) is utilized to automatically generate samples through determining the adhesive particles and particle groups. The samples are then manually checked and corrected to further segment the particles in order to form the dataset. Finally, the YOLOv5, a mature target detection algorithm, is used to train the labeled data in order to produce a multi-target detection recognition model. The model can be applied to identify more pictures of the same substance, and the particles’ long and short axes are estimated using the elliptical coverage of the particles within the identified image boundary box. Experiment results from four kinds of powders suggest that this method can provide automatic particle size analysis for industrial SEM particle images.