Magnetic resonance images are represented by localised signal intensities. This depends on the strength of the magnetic field involved in the production of images also known as pulse sequence. Further to this, it is clear that relaxation times T1 and T2 mentioned above are responsible for the image quality. Other physical parameters that are involved in the determination of image quality include the density of mobile protons, magnetic susceptibility of tissues, physical and chemical composition and lastly the nature of blood flow in a given area of the body (Edelman & Warach, 1993).
Pulse sequence is very important when it comes to the weighting exercise since it determines the repetition and echo time as shall be observed in the practical section of this project. 1.2 Research Rational This project is mainly motivated by the urge to understand and resolve the major issues that face the improvement of images prior to analysis by medical practitioners. It has emerged that despite the advantages that MRI poses towards the medicine field there are improvements that should be made to achieve better quality images faster.
The loud noises that emerge from the MRI images is at times misleading and should be eliminated through the existing or new applicable forms of image reconstruction. This research however focuses on two main image reconstruction algorithms namely; GRAPPA and SENSE for implementation within MRI. GRAPPA is a denotation of the phrase Generalized Autocalibrating Partially Parallel Acquisitions founded which is basically utilised to reconstruct the k-space data that is deemed to be missing (Wang, et al., 2011). These combinations may be either linear on nonlinear depending on how the data acquired is combined to eliminate the pending errors.
This study is also motivated by the use of SENSE for image reconstruction in order to offer a comparative approach. This algorithm offers image correction by enhancing the acceleration factor for phase encoding purposes. The general user interface provides a reconstruction of parameters that are parallel as well as analysis of quality of the final image (Omer & Dickinson, 2010). 1.3 Research Objectives The main objectives of this study are to: i. Familiarize with the tools utilised for MRI image reconstruction through a practical approach. ii. Study the methodologies applied in reconstruction of images emanating from MRI. iii. Establish differences between the existing methods of image reconstruction through a practical approach. iv. Recommend on suitable parameters of achieving quality MRI images. 1.4 Limitations of Study This study is limited by basic limitations such as the ability to learn the methods of image reconstruction ahead of schedule.
Apart from learning about the image reconstruction approaches, it is mandatory to familiarise with at least one tool through which to implement the GRAPPA and SENSE methodologies. As a matter of fact Matlab was chosen as the tool in which to deploy the methodologies above due to its simplicity nature of coding. 2.0 MRI Image Reconstruction As indicated in the introductory section above, the main objectives of this study are to learn on how to apply the tools deployed for MRI image reconstruction in order to give conclusive remarks on the issue of image quality improvement.
This section therefore covers data acquisition, the technologies that are deployed in image reconstruction, the Matlab simulation exercise and the expected outcome. Images obtained from the simulation exercise are also exhibited in accordance to the technology applied for review and comparison. 2.1 Matlab Simulation tool Matlab is identified as a very important tool in this research due to the simulation capabilities that it poses towards MRI experiments. This is a cross platform tool which poses many advantages to learners in that it is programmed in a language that is easy to understand.
To add to this, there are several default commands that have been written and availed publicly for illustration purposes in case of beginners.
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