Magnetic Resonance Imaging Technique - Essay Example

Magnetic Resonance Imaging Magnetic Resonance Imaging This is an advanced imaging technique used in the field of medicine under radiology. The technique enhances visualization of internal structures; it uses a property of nuclear magnetism resonance to visualize nuclei of body atoms. The technique uses highly powerful magnetic field to align atomic nuclei, after which the radio frequency systematically alters the alignment of these nuclei. The activity produces a rotating magnetic field, which is detectable using a scanner. The information is then read recorded and to create an image of the scanned body. The magnetic field causes the nuclei in different locations to rotate with different speeds using gradients. By the use of gradients in various directions, 3D and 2D images can be easily focused in any arbitrary orientation (Guillermo, S. 2001.). The technique creates admirable contrast between different soft tissues in the body. This facilitates the imaging of the brain, heart, muscles, and cancers, unlike other imaging techniques such as computer tomography (CT), and the X-rays. The significant difference between this model of imaging and other methods such as the X-rays and CT is that IMR does not make use ionizing radiation (Joachim, 1997). How MRI works All bodies expose themselves to water molecules. The water molecule has two protons and hydrogen nuclei. When one is using a powerful magnetic field of a scanner, the overall magnetic moment of different protons aligns themselves in the direction of the field. Turning on of the radio frequency transmitter follows, thus, producing different electromagnetic fields. The electromagnetic field has the appropriate frequency termed as resonance frequency; the protons in the magnetic field absorb and flip the spin. After a while, when the electromagnetic field is in off status, the protons’ spins get to thermal dynamic equilibrium. The bulk magnetizations get aligned by the static field. As a result, this relaxation, radio frequency signals arise; these can be measure using receiver coils (Pottumarthi, 2006.). Additional magnetic fields can facilitate learning about the information regarding the origin of the 3D space during the scan. Fields generated through passing electrical current via gradient coils results to varying magnetic fields in reference to position of the magnet. This also alters the frequency of the signal, as it depends on the origin of the signal. Mathematically, the distribution of the signal can also be recovered from the body; however, this uses the inverse frontier transformation (Bernd and Horst, 2000). After the relaxation rates, protons in various tissues return to the equilibrium. Different tissues variables entail spin density, T1 and T2 relaxation flow and times and spectral shifts can facilitate the construction of the image. Changing the setting of the scanner leads to change of effects (Joachim, 1997). These effects can enhance contrast among different tissues or between different characteristics of body tissues, as in diffusion MRI. The Diffusion MRI came into existence from 1980s. Enhancements of Diffusion MRI The technique facilitates mapping of diffusion procedures of water, molecules, and other biological tissues in vivo and non-invasively. MRI is not in a tissue is not free, it reflects interactions from molecules with many obstacles, these include membranes, fibres and macromolecules. Water molecules facilitate revealing microscopic details regarding tissue architecture either diseased state or normal. The first diffusion image of the diseased and the normal brain is evident back in 1985. Over the last 25years, the technique of MRI has been successful. Clinical domain is the main application and dwells largely on neurological disorders, especially taking care of acute stroke patients. In addition, it is also standard diffusion tensor imaging (DTI) and white matter (Marinus, and Jacques, 2007). The capability of visualizing anatomical connection between various parts of the brain on an individual basis and non-invasively basis rose as a breakthrough of neurosciences. It is in the near past that it scientist documented that the diffusion of functional MRI could get images from neural activation of the brain. Finally, the technique of diffusion MRI proved to be sensitive to perfusion. This is as the water in the blood system mimics random processes (Bernd and Horst, 2000). In diffusion weighted images commonly known as the DWI, each image has an intensity, which reflects single best measurements in reference to the rate of diffusion of water in a certain location. The movement is highly sensitive in initial stages after every stroke. This is more than the traditional MRI measurement like the T1 or the T2 relaxation rates. When we refer to Diffusion Spectrum Imaging, which is the (DSI), variant diffusion weighted imaging facilitates deriving the connection between data sets. The DSI is variant of diffusion of weighted imaging; this means that it is sensitive to intra-voxel heterogeneities in terms of diffusion direction brought up by crossing fiber tract and therefore, allowing accurate mapping and imaging of axonal trajectories other than other diffusion imaging approaches (Pottumarthi, 2006). In terms of appropriateness, DWI is leading all over the world of physics regarding imaging and information. It is highly significant when referring to tissue dominated by isotropic water movements. These entail the grey substance in the cerebral cortex together with other key brain nuclei, or other sensitive body nuclei, where the rate of diffusion seems to take measurements on either the axis. Nevertheless, DWI is highly sensitive in terms of T1 and T2 relaxation. Entangling relaxation and diffusion impacts on image contrast and one can attain qualitative and quantitative images of the diffusion coefficients or exactly apparent diffusion coefficient. The concept aims at taking into account the fact that the diffusion processes are complex in regard to biological tissues and ultimately, reflects different tissues mechanism (Bernd and Horst, 2000). DTI, that is, the Diffusion Tensor Imaging is crucial when tissues like neural axons of white matter of the brain or muscles fibers of the heart has internal fibrous structure analogous to the anisotropy of different crystals. However, water will later diffuse rapidly in the direction of the internal structure. On the other hand, the diffusion will be slow when it is moving in the perpendicular direction, in reference with the internal structure. The ideology also means that the diffusion rate depends on the position in which the observer is observing the process. Previously, the diffusion weighted imaging 3-directional information must be in the application so as to ensure that the estimated trace is of average diffusivity. Clinically, these images prove to be extraordinarily useful in diagnosing vascular strokes of the brain. This is efficient in the early stages of the hypoxic edema (Guillermo, 2001). Highly extended DTI scans attain neural tract directional data from the information using the 3D or the multidimensional vector algorithms in regard to the gradient directions. It is also sufficient to compute diffusion tensor. Diffusion This activity produces vivo methods of magnetic resonance in the imaging process of biological tissues with sensitized characteristics of local molecular diffusion. If we focus on the perfection of protons, they move in different time intervals in pulses. This reduces the signal measured by the signal. Field gradient pulse is a method designed by a biological scientist named Stejskal Tanner. The design of the equation facilitates calculation of the reduction of the measurements made by the signal due to the pulse gradient. Below is a pictorial illustration of the equation. S0 represents the signals intensity with the absence of the diffusion weighting S represents the signal with the gradient D is the diffusion coefficient Time change between two pulses G represents the pulse gradient Duration of pulse Represents the gyromagnetic ratio Other important equations derived by other individuals are as illustrated below. Isotropic and Anisotropic Diffusion Anisotropic diffusion is a technique that helps reduce image noise, and it avoids the removal of the image content including details necessary for the interpretation of the image. This diffusion resembles the process of creation of a scale space (Bernd and Horst, 2000). Images generate successively many blurred images due to the diffusion process. Images produced, as an integration of the image and an isotropic filter, thus, as the parameter increases the width of the filter increases. The diffusion process, usually a transformation of the original image, termed as linear and space invariants. An isotropic diffusion is a general form of the diffusion processes, producing parameterized images, which is a combination of the original image, and the filter, depending on the content of the original image, making it non linear and space variants. Strictly speaking, the space variants filters, the content of the image, are approximately a function of the impulse function, which is near edges and other structures preserved in the images, as compared to resulting space of the scale, formulated by Perona in collaboration with Malik (1987). This diffusion though earlier stated by Perona and Malik, usually inhomogeneous as well as, nonlinear diffusion, due to the formulators, also referred to as the Perona Malik diffusion. A formulation, which is more, general allows the filter, adapted locally, t be anisotropic near structure like edges and lines. Also, given by the structure elongated along structures and it is narrow across. This smoothing further referred to as shape adapted smoothing, which is by authors taken as coherence enhancing diffusion. Resulting images have linear structures preserved, and at the same time, there is smoothing done along the structures. Thus, an equation developed referred to as the diffusion equation and the coefficient of diffusion is an image function rather than a constant scalar, assuming a matrix value (Bernd and Horst, 2000). Resulting combination of images, described as the original image and space variant filters combined, though the filter adapted locally together with its combination as well as the images are not practically realized. By approximation of the generalized equation of diffusion, the anisotropic diffusion thus implemented. The new image formed, and then computed through the application of the equation to a previous image. Furthermore, anisotropic diffusion can be taken to be a process where relatively unsophisticated sets of computation should be used for the computation of successive images in the family, where the process is to be continued till a degree of smoothing obtained. Isotropic diffusion is thus the opposite of anisotropic diffusion discussed earlier. Achieving Different Diffusion Weightings Different diffusion weightings can be achieved by collecting diffusion-weighted images along several slope (gradient) directions. Incorporating diffusion-sensitized MRI pulse sequences aids in collecting diffusion weighted images. An example of such pulse sequences is an echo-planar imaging (EPI). When the diffusion tensor is symmetric, only six directional measurements are compulsory, instead of nine, along with an image attained without diffusion weighting (b = 0). This leads to a typical number of gradient combinations that preserve similar space sampling and uniform b values along each direction. The gradient pulses coefficient corresponds to (x, y, z) axes and which are normalized to a given amplitude. For accuracy, this minimal set of images can be repeated because the signal to noise ration may low. It is necessary to obtain data with different diffusion weightings for each direction. In scalar diffusion, the best accuracy on diffusion co-efficient, D, can be achieved when using two b factors i.e. b1 and b2, differing by approximately 1/D. When more than two values are to be used for averaging purposes, the raw image sound to noise ratio (SNR) is used for accuracy. In anisotropic cases, the diffusion co-efficient, D, changes for each voxel in accordance with the measurement direction (Pottumarthi, 2006). In axial symmetry cases, only tetrahedral encoding is necessary (four directions). This reduces the number of images to be processed and the acquisition time. Therefore, obtaining diffusion weightings can be done by acquiring multiple diffusion directions, including image without diffusion weighting (b = 0) image, and fitting the model of interest in each voxel (Guillermo, 2001). Measurement of Diffusion Information In order to obtain diffusion information from the set of orientation or diffusion-weighted images, multiple linear regressions is applied. From diffusion tensor components, indices that reflect a degree of anisotropy or the average diffusion in each voxel can be calculated. The overall direction of diffusivities in each voxel and the diffusion values accompanied with these directions can then be determined. This is the equivalent of determining the reference frame [x’, y’, z’] where values of diffusion co-efficient, D, are null. This provides Eigen-values and Eigen-vectors, λ, which correspond to the associated diffusivities and main diffusion respectively (Guillermo, 2001). Diffusion ellipsoids do make it easy to display tensor data. These are a three-dimensional representation of diffusion distance of space covered by molecules with time. They can be displayed for each image voxel and can be calculated from the Eigen diffusivities. Eigen diffusivities represent one-dimensional diffusion co-efficients in the direction of diffusivities of the medium. Therefore, the main diffusion direction may be given by the main axis of the ellipsoid. Eccentricity of the ellipsoid represents symmetry and degree of anisotropy. A sphere would indicate isotropic diffusion (Pottumarthi, 2006). References Bernd, J. and Horst, H., 2000. Computer Vision and Applications, A Guide for Students and Practitioners. USA: Academic Press. Guillermo, S., 2001. Geometric partial differential equations and image analysis. USA: Cambridge University Press. Joachim, W., 1997. A Review of Nonlinear Diffusion Filtering. New York: Springer. Marinus, T. V. and Jacques, A. D., 2007. Magnetic Resonance Imaging: Theory and Practice. USA: Springer, 2003. Pottumarthi, V. P., 2006. Magnetic Resonance Imaging: Methods and Biologic Applications. London: Humana Press. Stewart, C. B., 2009. Magnetic resonance imaging: physical and biological principles. The University of Michigan: Mosby. Read More