Since the advent of magnetic resonance (MR) imaging as a sound medical imaging technique there has been a need to classify the tissues in the images created. Typically the radiologist has been responsible for the evaluation of the images produced by the MRI machine. In any given MRI there exists many different types of tissues each with a certain concentration of spin densities. Each one of these tissues has a unique T1 and T2 decay times along with a with a unique spin density. The T2 values are indicative of the structure of the tissue, and if the T2 time of a particular tissue has been previously determined the type of tissue can be determined. This is useful for automatic identification of disease, tumors, or any tissue for that matter. This information is characteristic for a particular tissue across MRI platforms. When the imaging sequence is specified that keeps the time of repetition (TR) constant, the image is a T2 weighted image. The method used in this paper, the Direct Exponential Curve Resolution algorithm (DECRA), attempts to evaluate these unique T2 times and classify the images. Antalek and Windig (4) have shown that by using DECRA it is possible to classify images based on T2. The goal of this experiment is to determine the weaknesses and limitations of DECRA when applied to synthetically generated images and real images obtained from a MRI machine. This method was analyzed to see if there was a possibility of improvement for future application and improvement. To test DECRA it was applied to synthetic images and real images obtained from an imager. The synthetic images where given noise to try to simulate a real image and to see how high the signal to noise ratio had to be. DECRA was able to segment the noiseless synthetic images, but began to fail as the numbers of pixels were reduced. When it was applied to real image DECRA performed well only after manual separations of the test images. DECRA has shown that it can a viable method to segment data that is related by relaxation constants.
Guinn, Jordan, "Tissue classification based on relaxation environments" (1998). Thesis. Rochester Institute of Technology. Accessed from
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