Catalase-imprinted Fe<Subscript>3</Subscript>O<Subscript>4</Subscript>/Fe@fibrous SiO<Subscript>2</Subscript>/polydopamine nanoparticles: An integrated nanoplatform of magnetic targeting,magnetic resonance imaging,and dual-mode cancer therapy

Recent advances in the research on the molecular mechanism of cell death and methods for preparation of nanomaterials make the integration of various therapeutic approaches,targeting,and imaging modes into a single nanoscale complex a new trend for the development of future nanotherapeutics.Hence,a novel ellipsoidal composite nanoplatform composed of a magnetic Fe3O4/Fe nanorod core (～120 nm) enwrapped by a catalase (CAT)-imprinted fibrous SiO2/ polydopamine (F-SiO2/PDA) shell with thickness 70 nm was prepared in this work.In vitro experiments showed that the Fe3O4/Fe@F-SiO2/PDA nanoparticles can selectively inhibit the bioactivity of CAT in tumor cells by the molecular imprinting technique.As a result,the H2O2 level in tumor cells was elevated dramatically.At the same time,the Fe3O4/Fe core released Fe ions to catalyze the conversion of H2O2 to ·OH in tumor cells.Eventually,the concentration of ·OH in tumor cells rapidly rose to a lethal level thus triggering apoptosis.Combined with the remarkable near-infrared light (NIR) photothermal effect of the CATimprinted PDA layer,the Fe3O4/Fe@F-SiO2/PDA nanoparticles can effectively kill MCF-7,HeLa,and 293T tumor cells but are not toxic to nontumor cells.Furthermore,these nanoparticles show good capacity for magnetic targeting and suitability for magnetic resonance imaging (MRI).Therefore,the integrated multifunctional nanoplatform opens up new possibilities for high-efficiency visual targeted nonchemo therapy for cancer.     

EEG‐based brain source localization using visual stimuli

Electroencephalography (EEG) is widely used in variety of research and clinical applications which includes the localization of active brain sources. Brain source localization provides useful information to understand the brain's behavior and cognitive analysis. Various source localization algorithms have been developed to determine the exact locations of the active brain sources due to which electromagnetic activity is generated in brain. These algorithms are based on digital filtering, 3D imaging, array signal processing and Bayesian approaches. According to the spatial resolution provided, the algorithms are categorized as either low resolution methods or high resolution methods. In this research study, EEG data is collected by providing visual stimulus to healthy subjects. FDM is used for head modelling to solve forward problem. The low‐resolution brain electromagnetic tomography (LORETA) and standardized LORETA (sLORETA) have been used as inverse modelling methods to localize the active regions in the brain during the stimulus provided. The results are produced in the form of MRI images. The tables are also provided to describe the intensity levels for estimated current level for the inverse methods used. The higher current value or intensity level shows the higher electromagnetic activity for a particular source at certain time instant. Thus, the results obtained demonstrate that standardized method which is based on second order Laplacian (sLORETA) in conjunction with finite difference method (FDM) as head modelling technique outperforms other methods in terms of source estimation as it has higher current level and thus, current density (J) for an area as compared to others.     

Online dynamic cardiac imaging based on the elastic-net model

Purpose: The purpose of this work was to develop an online dynamic cardiac MRI model to reconstruct image frames from partial acquisition of the Cartesian k-space data, which utilizes structural knowledge of consecutive image frames. Materials and methods: Using an elastic-net model, the proposed algorithm reconstructs dynamic images using both L₁ and L₂ norm operations. The L₁ norm enforces the sparsity of the frame difference, while the L₂ norm with motion-adaptive weights catches the internal structure of frame differences. Unlike other online methods such as the Kalman filter (KF) technique, the new model requires no assumption of Gaussian noise, and can faithfully reconstruct the dynamic images within a compressive sensing framework. Results: The proposed method was evaluated using simulated dynamic phantoms with 40 frames of images (128?×?128) and a cardiac MRI cine of 25 frames (256?×?256). Both results showed that the new model offered a better performance than the online KF method in depicting simulated phantom and cardiac dynamics. Conclusion: It is concluded that the proposed imaging model can be used to capture a large variety of objects in motion from highly under-sampled k-space data, and being particularly useful for improving temporal resolution of cardiac MRI.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

Signal losses in diffusion preparation: Comparison between spin-echo, stimulated echo and SEASON

Diffusion-weighted magnetic resonance imaging and spectroscopy commonly apply a spin-echo or stimulated echo preparation including sensitizing field gradients. The article reports on a systematic numerical approach to an optimum diffusion preparation considering undesired signal losses caused by relaxation. A large range of possible applications on whole-body units and animal scanners is covered. Instructions for an optimized type and timing of the diffusion preparation are provided for the readership, based on the desired diffusion weighting (b-value), the available maximum field gradient amplitudes, the RF pulse durations and gradient ramp times, and the relaxation characteristics of the specimen (or tissue) of interest. In addition, a new type of diffusion preparation named SEASON (simultaneous Spin-Echo And Stimulated echO preparatioN) is introduced and compared with spin-echo and stimulated echo diffusion preparation. It is demonstrated that spin-echo preparation is superior to stimulated echo preparation in all cases with T₂ ≈ T₁ and in all cases with relatively low diffusion weighting resulting in short duration of diffusion sensitizing gradients δ « T₂. For tissues with T₂ « T₁ (as musculature or red bone marrow) stimulated echo preparation becomes superior to spin-echo preparation for high ratios b/A² (b-value indicates diffusion weighting,A is the maximum gradient amplitude). The new SEASON technique allows a higher yield in signal intensity compared to spin-echo or stimulated echo preparations in clinically relevant cases. © 1998 Elsevier Science B.V. All rights reserved.     

Functional and metabolic evaluation of the hypertrophied heart using MRI and31P-MRS

4. Conclusions Diastolic LV function and myocardial HEP metabolism are impaired only when LVH is caused by permanent pressure or volume overload, and not by a temporary increase in cardiac workload during part of the day as in elite athletes. Therefore, training-induced and pressure/volume-overload-induced LVH seem to represent different phenotypes of LVH, possibly related to genetic reprogramming which only occurs during permanent cardiac overload [17]. Moreover, there is an association between impaired LV diastolic function and altered myocardial HEP metabolism in patients with hypertension and in patients with aortic valve disease. Finally we did not find a correlation between myocardial HEP metabolism and LV mass in any of the groups studied. The latter indicates that LVH should be regarded as an epiphenomenon to cardiac overload, and not as a primary factor causing abnormal HEP metabolism.     

NMR investigation of experimental chemical induced brain tumors in rats, potential of a superparamagnetic contrast agent (MD3) to improve diagnosis

The different steps of development of chemically induced brain tumors were investigated in rats by MRI using a superparamagnetic contrast agent, magnetite-dextran nanoparticles (MD3). Sprague-Dawley strain pregnant female rats were injected intravenously with ethynitrosourea solution at the end of pregnancy. Offspring whelped by the inoculated mother were followed. MRI examinations were performed at 0.5 T. MD3 nanoparticles were injected intravenously at a dose of 5 mg Fe kg^-1 body weight 30 min before rat sacrifice. After sacrifice, histological slices were stained with hematoxylin-eosin. Relaxation times were measured at 40 MHz and 37°. MD3 nanoparticles act differently according to the step of the tumor development. Before tumor appearance, at a step characterized by the presence of abnormal cell clusters, relaxation time T2 increased significantly. The T2-weighted image showed a small increase in signal intensity in the lesion. Image contrast was improved by MD3 nanoparticles injection because of the decrease in healthy tissue signal intensity. The Tl-weighted image did not provide any additional information. In presence of a minute tumor, relaxation times decreased in tumor but increased in surrounding tissue. The Tl-weighted image showed a hypersignal on the border of an hyposignal. T2-weighted image showed a hypersignal in the same area. Signal intensity was not modified after MD3 nanoparticles injection. When new vascular capillaries developed in the tumor, MD3 nanoparticles cross into the cerebral parenchyma. Transmission electron microscopy showed magnetite crystals in this specific area on cytoplasm vesicles of glial cells and in tumor-specific membrane arrangements. On T2-weighted image, the hypersignal consisted of a well defined part and a second more fuzzy part, its signal being extinguished after MD3 nanoparticles injection. Necrotic areas and edema can be discriminated. The use of such a superparamagnetic contrast agent would be helpful in early detection of tumor development and in improving distinction of tumor mass from its vascular environment in patients. © 1998 Elsevier Science B.V. All rights reserved.     

基于协议干扰模型的最小路径干扰度算法

在分析了路由判据WCETT和iAWARE的基础上,指出LMR的路由算法的不足之处,得出了一种新型的基于协议干扰模型的最小路径干扰度算法-MRI算法,并经过实践,给出了仿真效果图。结果表明,本算法是非常优越的。     

New Riemannian techniques for directional and tensorial image data

This paper develops new geometrical filtering and edge detection algorithms for processing non-Euclidean image data. We view image data as residing on a Riemannian manifold, and we work with a representation based on the exponential map for this manifold together with the Riemannian weighted mean of image data. We show how the weighted mean can be efficiently computed using Newton's method, which converges faster than the gradient descent method described elsewhere in the literature. Based on geodesic distances and the exponential map, we extend the classical median filter and the Perona-Malik anisotropic diffusion technique to smooth non-Euclidean image data. We then propose an anisotropic Gaussian kernel for image filtering, and we also show how both the median filter and the anisotropic Gaussian filter can be combined to develop a new edge preserving filter, which is effective at removing both Gaussian noise and impulse noise. By using the intrinsic metric of the feature manifold, we also generalise Di Zenzo's structure tensor to non-Euclidean images for edge detection. We demonstrate the applications of our Riemannian filtering and edge detection algorithms both on directional and tensor-valued images.