Sinogram Restoration for Low-Dosed X-Ray Computed Tomography Using Fractional-Order Perona-Malik Diffusion

Existing integer-order Nonlinear Anisotropic Diffusion (NAD) used in noise suppressing will produce undesirable staircase effect or speckle effect. In this paper, we propose a new scheme, named Fractal-order Perona-Malik Diffusion (FPMD), which replaces the integer-order derivative of the Perona-Malik (PM) Diffusion with the fractional-order derivative using G-L fractional derivative. FPMD, which is a interpolation between integer-order Nonlinear Anisotropic Diffusion (NAD) and fourth-order partial differential equations, provides a more flexible way to balance the noise reducing and anatomical details preserving. Smoothing results for phantoms and real sinograms show that FPMD with suitable parameters can suppress the staircase effects and speckle effects efficiently. In addition, FPMD also has a good performance in visual quality and root mean square errors (RMSE)

Image Denoising Based on Dilated Singularity Prior

In order to preserve singularities in denoising, we propose a new scheme by adding dilated singularity prior to noisy images. The singularities are detected by canny operator firstly and then dilated using mathematical morphology for finding pixels “near” singularities instead of “on” singularities. The denoising results for pixels near singularities are obtained by nonlocal means in spatial domain to preserve singularities while the denoising results for pixels in smooth regions are obtained by EM algorithm constrained by a mask formed by downsampled spatial image with dilated singularity prior to suiting the sizes of the subbands of wavelets. The final denoised results are got by combining the above two results. Experimental results show that the scheme can preserve singularity well with relatively high PSNR and good visual quality

Segmental and transmural motion of the rat myocardium estimated using quantitative ultrasound with new strategies for infarct detection

Introduction: The estimation of myocardial motion abnormalities has great potential for the early diagnosis of myocardial infarction (MI). This study aims to quantitatively analyze the segmental and transmural myocardial motion in MI rats by incorporating two novel strategies of algorithm parameter optimization and transmural motion index (TMI) calculation.Methods: Twenty-one rats were randomly divided into three groups (n = 7 per group): sham, MI, and ischemia–reperfusion (IR) groups. Ultrasound radio-frequency (RF) signals were acquired from each rat heart at 1 day and 28 days after animal model establishment; thus, a total of six datasets were represented as Sham1, Sham28, MI1, MI28, IR1, and IR28. The systolic cumulative displacement was calculated using our previously proposed vectorized normalized cross-correlation (VNCC) method. A semiautomatic regional and layer-specific myocardium segmentation framework was proposed for transmural and segmental myocardial motion estimation. Two novel strategies were proposed: the displacement-compensated cross-correlation coefficient (DCCCC) for algorithm parameter optimization and the transmural motion index (TMI) for quantitative estimation of the cross-wall transmural motion gradient.Results: The results showed that an overlap value of 80% used in VNCC guaranteed a more accurate displacement calculation. Compared to the Sham1 group, the systolic myocardial motion reductions were significantly detected (p < 0.05) in the middle anteroseptal (M-ANT-SEP), basal anteroseptal (B-ANT-SEP), apical lateral (A-LAT), middle inferolateral (M-INF-LAT), and basal inferolateral (B-INF-LAT) walls as well as a significant TMI drop (p < 0.05) in the M-ANT-SEP wall in the MI1 rats; significant motion reductions (p < 0.05) were also detected in the B-ANT-SEP and A-LAT walls in the IR1 group. The motion improvements (p < 0.05) were detected in the M-INF-LAT wall in the MI28 group and the apical septal (A-SEP) wall in the IR28 group compared to the MI1 and IR1 groups, respectively.Discussion: Our results show that the MI-induced reductions and reperfusion-induced recovery in systolic myocardial contractility could be successfully evaluated using our method, and most post-MI myocardial segments could recover systolic function to various extents in the remodeling phase. In conclusion, the ultrasound-based quantitative estimation framework for estimating segmental and transmural motion of the myocardium proposed in our study has great potential for non-invasive, novel, and early MI detection

Influenza Inactive Virus Vaccine with the Fusion Peptide (rTα1- BP5) Enhances Protection Against Influenza Through Humoral and Cell-Mediated Immunity

Thymosin α1 (Tα1) and Bursopentin (BP5) are both immunopotentiators. To explore whether the thymosin α1-Bursopentin (rTα1-BP5) is an adjuvant or not, we cloned the gene of Tα1-BP5 and provided evidence that the gene of Tα1-BP5 in a recombinant prokaryotic expression plasmid was successfully expressed in Escherichia coli BL21. To evaluate the immune adjuvant properties of rTα1-BP5, chickens were immunized with rTα1-BP5 combined with H9N2 avian influenza whole-inactivated virus (WIV). The titers of HI antibody, antigen-specific antibodies, Avian influenza virus (AIV)-neutralizing antibodies, levels of Th1-type cytokines (gamma interferon (IFN-γ)) and Th2-type cytokines (interleukin 4 (IL-4)), and lymphocyte proliferation responses were determined. We found that rTα1-BP5 enhanced HI antibody and antigen-specific immunoglobulin G (IgG) antibodies titers, increased the level of AIV-neutralizing antibodies, induced the secretion of Th1- and Th2-type cytokines, and promoted the proliferation of T and B lymphocyte. Furthermore, virus challenge experiments confirmed that rTα1-BP5 contributed to the inhibition replication of the virus (H9N2 AIV (A/chicken/Jiangsu/NJ07/05) from chicken lungs. Altogether, these findings suggest that rTα1-BP5 is a novel adjuvant suitable for H9N2 avian influenza vaccine

Phase- and GVF-Based Level Set Segmentation of Ultrasonic Breast Tumors

Automatically extracting breast tumor boundaries in ultrasound images is a difficult task due to the speckle noise, the low image contrast, the variance in shapes, and the local changes of image intensity. In this paper, an improved edge-based active contour model in a variational level set formulation is proposed for semi-automatically capturing ultrasonic breast tumor boundaries. First, we apply the phase asymmetry approach to enhance the edges, and then we define a new edge stopping function, which can increase the robustness to the intensity inhomogeneities. To extend the capture range of the method and provide good convergence to boundary concavities, we use the phase information to obtain an improved edge map, which can be used to calculate the gradient vector flow (GVF). Combining the edge stopping term and the improved GVF in the level set framework, the proposed method can robustly cope with noise, and it can extract the low contrast and/or concave boundaries well. Experiments on breast ultrasound images show that the proposed method outperforms the state-of-art methods

Numerical assessment of the reduction of specific absorption rate by adding high dielectric materials for fetus MRI at 3 T

The specific absorption rate (SAR) is an important issue to be considered in fetus MRI at 3 T due to the high radiofrequency energy deposited inside the body of pregnant woman. The high dielectric material (HDM) has shown its potential for enhancing B field and reducing SAR in MRI. The aim of this study is to assess the feasibility of SAR reduction by adding an HDM to the fetus MRI. The feasibility of SAR reduction is numerically assessed in this study, using a birdcage coil in transmission loaded with an electromagnetic pregnant woman model in the SEMCAD-EM solver. The HDMs with different geometric arrangements and dielectric constants are manually optimized. The B1+ ${B-1}^ +$ homogeneity is also considered while calculating the optimized fetus 10 g local SAR among different strategies in the application of HDM. The optimum maximum fetus 10 g local SAR was obtained as 2.25 W/kg, by using two conformal pads placed left and right with the dielectric constant to be 400, reduced by 24.75% compared to that without the HDM. It indicated that the SAR can be significantly reduced with strategic placement of the HDM and the use of HDM may provide a simple, effective and low-cost method for reducing the SAR for the fetus MRI at 3 T

Multispectral remote sensing image classification algorithm based on rough set theory, in

Abstract-Rough set theory is a relatively new mathematical tool to deal with imprecise, incomplete and inconsistent data. A method of multispectral image classification using rough set theory is proposed. First, to decrease computational time and complexity, band reduction of multispectral image using attribute reduct concept in rough set theory and information entropy is performed. Then, mixture model initial parameters of remote sensing image are mapped from crude classes, which are generated using equivalent relation. Finally image cluster is obtained unsupervised with Gaussian mixture model whose parameters are refined by Expectation Maximization algorithm. The proposed method is performed on a multispectral image, and the experimental results show the feasibility and effectiveness of the algorithm by means of comparison and analysis

Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means.

International audienceThe x-ray exposure to patients has become a major concern in computed tomography (CT) and minimizing the radiation exposure has been one of the major efforts in the CT field. Due to plenty high-attenuation tissues in the human chest, under low-dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to discriminate from the attenuation information of normal tissues. This paper describes a two-step processing scheme called 'artifact suppressed large-scale nonlocal means' for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows conclusion on the efficacy of our method in improving thoracic LDCT data