<p> Magnetic Resonance Imaging (MRI) velocity quantification is addressed in part I of this thesis. In simple MR imaging, data is collected and tissue densities are displayed as images. Moving tissue creates signals which appear as artifacts in the images. In velocity imaging, more data is collected and phase differences are used to quantify the velocity of tissue components. The problem is described and a novel formulation of a regularized, nonlinear inverse problem is proposed. Both Tikhonov and Total Variation Regularization are discussed. Results of numerical simulations show that significant noise reduction is possible.</p> <p> The method is firstly verified with MATLAB. A number of experiments are carried out with different regularization parameters, different magnetic fields and different noise levels. The experiments show that the stronger the complex noise is, the stronger the magnetic field requires for estimating the velocity. The regularization parameter also plays an important role in the experiments. Given the noise level and with an appropriate value of regularization parameter, the estimated velocity converges to ideal velocity very quickly. A proof-of-concept implementation on the Cell BE processor is described, quantifying the performance potential of this platform.</p> <p> The second part of this thesis concerns the evaluation of an elementary function library. Since CBE SPU is designed for compute intensive applications, the well developed Math functions can help developer program and save time to take care other details. Dr. Anand's research group in McMaster developed 28 math functions for CBE SPU. The test tools for accuracy and performance were developed on CBE. The functions were tuned while testing. The functions are either competitive or an addition to the existing SDK1.1 SPU math functions.</p> / Thesis / Master of Applied Science (MASc)

No description available.