Papers by Keyword: Magnetic Resonance Imaging (MRI)

Abstract: Superparamagnetic Iron oxide nanoparticles (SPIONs) have fascinated researchers due to their vast applications in biomedical fields such as magnetic resonance imaging, cell sorting, hyperthermia, drug delivery etc. The special properties of SPIONs depend on the method of synthesis and surface modification. Among various synthetic protocols, hydrothermal method has attracted much attention due to simplicity, uniformity and excellent magnetic properties of iron oxide nanoparticles. Magnetic properties of SPIONs could be tuned by controlling the size and shape of the particles as well as by the surface modification. Low colloidal stability and high hydrophobic nature of SPIONs result in aggregation of the particles which could be avoided by surface modification of the SPIONs using various capping agents. The size, shape and surface environment of SPIONs can also be controlled by the surface coating. SPIONs are promising contrast agents due to their non-poisonous nature, biocompatibility and large surface area. The biocompatibility of SPIONs is enhanced by the surface coating/modification. The present review focuses on the hydrothermal synthesis of SPIONs and their characterization using various techniques and the applications of SPIONs in the MRI.Table of Contents

Abstract: Accurate segmentation of brain tumor from MRI is crucial in computer aided diagnosis as well as in other medical imaging applications. Brain tumor segmentation is a challenging task due to the diverse appearance of tumor tissues. A variety of brain tumor segmentation techniques have been explored in the literature. Here, a brief review of different brain tumor segmentation techniques has been discussed with their merits and demerits. We conclude with a discussion on the trend of future research in brain tumor segmentation.

Abstract: In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

Abstract: To investigate the relationship between apparent diffusion coefficient (ADC) values measured by diffusion-weighted imaging (DWI) on a 3.0T MR unit and glomerular filtration rate (GFR) determined by renal imaging using Nuclear Medicine ⁹⁹ Tcm-DTPA. 3.0T MRI DWI and 99 Tcm-DTPA renal imaging were simultaneously performed in 30 patients with chronic renal failure. The b values set for DWI imaging were 0, 200, 400, 600, 800 and 1000 s/mm2 and the ADC values of renal cortex were measured. 60 kidneys from the patients were classified into three groups according to the measurements of GFR: mildly impaired renal function, moderately impaired renal function and severely impaired renal function. ADC values of the three groups were compared to determine whether there existed statistic difference and the correlation between ADC values and GFR was also measured. Statistical difference was found in ADC values of the three groups and a positive correlation was identified between ADC values and GFR (r = 0.623). Multi-b-value diffusion-weighted MR imaging at 3.0 T can be used to assess renal filtration function.Abbreviation used: DWI, diffusion-weighted imaging; ADC, apparent diffusion coefficient; GFR, glomerular filtration rate; NEX, number of excitation.

Abstract: Real-time magnetic resonance imaging (MRI) has many advantages as compared to traditional MRI and can be used for the visualization of dynamic moving cardiac structures and functions without cardiac gating, as the fast data acquisition apparently freezes the motion resulting from heart beating and lung breathing. During the past decades, fast pulse sequences and image reconstruction algorithms had been developed to improve the temporal resolution with acceptable spatial resolution. However, the bottle neck of current real-time MRI systems is the availability of a user-friendly prescription tool to allow a MRI technician to prescribe the 6-Degree-of-freedom imaging plane of the MRI system. To meet the needs of real-time MRI, a 3D input tool is developed which facilitates user interactive specification of the center position and plane orientation of the MRI imaging plane. This paper reports such a custom designed 6 degree-of-freedom 3D input device, which allows the user to interactively and intuitively manipulate the scan plane to direct the real-time imaging capability to a target position based on the visual feedback provided on the MRI console in the form of real-time MRI images

Abstract: Soft magnetic field samples were placed into the homogenous magnetic field of an imager based on nuclear magnetic resonance. Several samples made of a soft magnetic material (cut from a data disc) were tested. Theoretical computations on a magnetic double layer were performed. For experimental verification an MRI 0.178 Testa ESAOTE Opera imager was used. For our experiments a homogeneous circular holder (reference medium) - a container filled with doped water - was designed. The resultant image corresponds to the magnetic field variations in the vicinity of the samples. For data detection classical gradient-echo and spin-echo imaging methods, susceptible to magnetic field inhomogeneities, were used. Experiments proved that the proposed method is perspective for soft magnetic material testing using magnetic resonance imaging methods (MRI).

Abstract: Glass fibre reinforced composites (GFRC), used in the manufacture of wind turbine blades, can suffer unpredictable, post-manufacturing, in-situ structural failure. The economic cost of remediation of such blade failures is extremely high, both on land and offshore. We suggest using zero-time-to-echo (ZTE) magnetic resonance imaging (MRI) as a method for characterising glass fibre reinforced composites. Initially, we demonstrate that carbon-13 Nuclear Magnetic Resonance (NMR) spectra acquired at 800MHz provide finger-print like information and that there is sufficient hydrogen-1 NMR signal available to conduct MRI. This work explores the efficacy of using zero timetoecho (ZTE) magnetic resonance imaging (MRI) to detect the rapidly decaying Hydrogen-1 (¹H) NMR signal from a representative sample. A 400MHz surface resonator was developed made of a 20mm diameter loop formed with 1.5mm thick silver wire and designed with variable tuning and matching in order to investigate the ¹H-MRI signal at 9.4T static magnetic field strength. The thickness of the GFRC was determined from the MRI data to be 3.51±0.28mm while the physical measurement using a caliper device resulted in a measurement of 3.45mm. Hence, a high spatial resolution accuracy is provided by ZTE MRI. Very short T₂* (<20μs) of the material led to stronger blurring artefacts for the blade material compared to heat shrink used for insulating the silver wire of the detector. ¹H images of the blade material were acquired demonstrating that ZTE is a suitable technique for acquiring image data from glass fibre materials. Work is on-going in studying the relaxation time parameters and chemical frequency shifts for materials with and without structural weaknesses in order to improve the predictive power of the technique. In conclusion, ZTE-MRI can provide useful information about the mechanical properties of glass fibre reinforced composites.

Abstract: In this paper, we propose a classification method for Alzheimer’s disease from structural MRI. In the method, a specific template is firstly constructed. Then all data are registered to the template and the corresponding Jacobians are calculated. And then, a general n-dimensional principal component analysis (GND-PCA) based method is adopted to extract features from the Jacobians and the features are enhanced by the linear discriminant analysis (LDA) . Finally, the enhanced features are used for the support vector machines (SVMs) classifiers. The proposed method classifies AD and normal controls (NC) well.

Abstract: An iterative method of active shimming has been presented, which is based on the Laplaces Spherical Harmonics and the least square theory. This approach makes the inhomogeneity of magnetic field to acceptable restriction. With analysis of the method, this paper gives the computer-aided program. The simulation results approve that the method is feasible and valid. Because of its simple and easy to implements, this approach can also be used for the shimming of the other magnets such as the nuclear magnetic resonance magnets.

Abstract: We used Magnetic Resonance microimaging (MRI) to study the compressive behaviour of synthetic elastin. Compression-induced changes in the elastin sample were quantified using longitudinal and transverse spin relaxation rates (R1 and R2, respectively). Spatially-resolved maps of each spin relaxation rate were obtained, allowing the heterogeneous texture of the sample to be observed with and without compression. Compression resulted in an increase of both the mean R1 and the mean R2, but most of this increase was due to sub-locations that exhibited relatively low R1 and R2 in the uncompressed state. This behaviour can be described by differential compression, where local domains in the hydrogel with a relatively low biopolymer content compress more than those with a relatively high biopolymer content.