Comprehensive biochemical analysis and nutritional evaluation of fatty acid and amino acid profiles in eight seahorse species ( spp.).

Seahorses are increasingly recognized for their nutritional potential, which underscores the necessity for comprehensive biochemical analyses. This study aims to investigate the fatty acid and amino acid compositions of eight seahorse species, including both genders of Hippocampus trimaculatus, Hippocampus kelloggi, Hippocampus abdominalis, and , to evaluate their nutritional value. We employed Gas Chromatography-Mass Spectrometry (GC-MS) and High-Performance Liquid Chromatography (HPLC) to analyze the fatty acid and amino acid profiles of the seahorse species.

[Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging].

Objective: To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE).

Methods: Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE.

[Quantitative magnetic resonance imaging of brain iron deposition: comparison between quantitative susceptibility mapping and transverse relaxation rate (R2*) mapping].

Objective: To evaluate the accuracy and sensitivity of quantitative susceptibility mapping (QSM) and transverse relaxation rate (R2*) mapping in the measurement of brain iron deposition.

Methods: Super paramagnetic iron oxide (SPIO) phantoms and mouse models of Parkinson's disease (PD) related to iron deposition in the substantia nigra (SN) underwent 7.0 T magnetic resonance (MR) scans (Bruker, 70/16) with a multi-echo 3D gradient echo sequence, and the acquired data were processed to obtain QSM and R2*.

[A two-point Dixon technique for water-fat separation using multiresolution and region-growing algorithm].

Objective: An improved water-fat separation method based on region-growing was proposed for use in regions with low signal-noise ratio (SNR).

Methods: Region-growing method was applied to 4 sub-images acquired by a down- sampling operation on the acquired phasor maps. The spatial smoothing constraint was exploited to calculate 4 error phasor maps to construct the final smooth error phasor map, which was used in two-point Dixon technique for water-fat separation.

[Adaptive regularized super-resolution reconstruction for magnetic resonance images].

To increase the resolution and signal-to-noise ratio (SNR) of magnetic resonance (MR) images, an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice. The new regularization parameter, which allowed the cost function of the new algorithm to be locally convex within the definition region, was introduced by the piori information to enhance detail restoration of the image with a high frequency. The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.

[Research on algorithms based on Markov random models for diffusion tensor-magnetic resonance images].

With the utilization of diffusion tensor information of image voxels, a novel MRF (Markov Random Field) segmentation algorithm was proposed for diffusion tensor MRI (DT-MRI) images benefitted from the introduction of Frobenius norm. The comparison of the segmentation effects between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which showed that the new algorithm could segment the DT-MRI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DT-MRI than in conventional MRI (T2WI) image.

[Adaptive super resolution algorithm for under-sampled images].

A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.