Fluorescent Superparamagnetic Iron Oxide Core–Shell Nanoprobes for Multimodal Cellular Imaging

Multimodal imaging agents that combine magnetic and fluorescent imaging capabilities are desirable for the high spatial and temporal resolution. In the present work, we report the synthesis of multifunctional fluorescent ferrofluids using iron oxide as the magnetic core and rhodamine B as fluorochrome shell. The core–shell structure was designed in such a way that fluorescence quenching due to the inner magnetic core was minimized by an intermediate layer of silica. The intermediate passive layer of silica was realized by a novel method which involves the esterification reaction between the epoxy group of prehydrolysed 3-Glyidoxypropyltrimethoxysilane and the surfactant over iron oxide. The as-synthesized ferrofluids have a high saturation magnetization in the range of 62–65 emu/g and were found to emit light of wavelength 640 nm ( excitation = 446 nm). Time resolved life time decay analysis showed a bi-exponential decay pattern with an increase in the decay life time in the presence of intermediate silica layer. Cytotoxicity studies confirmed the cell viability of these materials. The in vitro MRI imaging illustrated a high contrast when these multimodal nano probes were employed and the R2 relaxivity of these ∗Author to whom correspondence should be addressed. Email: smissmis@gmail.com sample was found to be 334 mM−1s−1 which reveals its high potential as a T2 contrast enhancing agent

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.