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Title: Level Set Methods for MRE Image Processing and Analysis
Keywords: Magnetic Resonance Elastography, Level Set Methods, Segmentation, Registration, Piecewise Constant Modeling
Issue Date: 20-Sep-2011
Citation: LI BINGNAN (2011-09-20). Level Set Methods for MRE Image Processing and Analysis. ScholarBank@NUS Repository.
Abstract: Manual palpation is a well-established routine in clinical medicine for health evaluation. It is a method of soft tissue discrimination according to their elastic properties. However, manual palpation is constrained by organ accessibility, tangible sensitivity and personal subjectivity. Magnetic resonance elastography (MRE) is an emerging technique for the quantification of soft tissue elasticity. It extends palpation to internal organs and tissues. The resultant shear modulus distributions or elastograms provide useful information complementary to structural magnetic resonance imaging (MRI). We conducted a series of experiments on static and dynamic MRE at the National University Hospital (NUH), Singapore. Dynamic MRE experiments were also conducted at the National Institute of Advanced Industrial Science & Technology (AIST), Japan and the National Institute of Radiological Sciences (NIRS), Japan. In this study, we concentrated on dynamic MRE, hereafter termed as MRE unless otherwise stated. Different from conventional structural images, MRE wave images are not directly interpretable. Sophisticated algorithms are required for MRE elasticity reconstruction. Local frequency estimation, algebraic inversion of differential equations and matched filters have been implemented and evaluated in our study. Some refractory issues, such as wave interference, phase wrapping and imaging noise, were investigated as well. We found that most algorithms for elasticity reconstruction were nonetheless susceptible to these refractory issues. In order to enhance MRE wave images, we developed new algorithms for phase unwrapping and directional spatiotemporal filtering. A numerical platform ? level set diffusion ? was proposed for unified noise suppression and image enhancement. Four controlling schemes, namely min/max curvature flow, Perona-Malik diffusion, coherence-enhancing diffusion and complex anisotropic diffusion, were developed and evaluated against traditional Gaussian and median filters. When the extracted wave fields are complex, complex anisotropic diffusion is particularly suitable for MRE image enhancement. There is a good tradeoff between noise suppression and elasticity consistency. In contrast, Gaussian smoothing distorts the values of elasticity, and median filtering is not good for structural similarity. We further investigated level set methods for MRE elastogram analysis, and have made contributions in two aspects. The first contribution is a new level set formulation unifying image gradient, region competition and prior information. It is helpful for robust elastogram segmentation. The other contribution is a hybrid level set model for piecewise constant elasticity modeling . It segments MRE elastograms and registers them to the corresponding magnitude images. Optimization is accomplished by alternating global and local region competitions. The resultant piecewise constant elasticity facilitates MRE analysis and interpretation. In summary, the work presented in this thesis advances the research on MRE image processing and analysis. To the best of our knowledge, this is the first systematical investigation of MRE image enhancement beyond Gaussian or median filtering. Level set diffusion is optimal for noise suppression and image enhancement in MRE. On the other hand, it is common to manually specify regions of interest in MRE images for elasticity evaluation. We proposed to automate this procedure by using level set methods for elasticity modeling and interpretation. Two new level set models have been developed, one for segmentation and the other one for piecewise constant modeling. These new methods have been evaluated on synthetic and/or real MRE datasets.
Appears in Collections:Ph.D Theses (Open)

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