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2 March 2006 Description of statistical theory of magnetic resonance imaging (MRI)
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The underlying phenomena in Magnetic Resonance imaging (MRI) physics, reconstruction, and analysis can be described by their statistics. This paper reports some new developments and focuses on two aspects: (1) new insights into statistical theory of MRI beyond its conclusions, and (2) three new applications of this theory spanned from image analysis to imaging physics. Why MR signals as well as k-space samples are statistically independent? When k-space samples are independent, why pixels in the reconstructed images are correlated? How does this correlation arise? Are spatially asymptotic independence and exponential correlation coefficient consistent? Why the homogeneity and scales can be characterized by stationarity and ergodicity? This paper provides answers to these questions. The first application of statistical theory of MRI is a stochastic model-based image analysis approach. The second application is a sensor array processing approach for detecting distinctive object regions. The third application is the Parallel Magnetic Resonance imaging (P-MRI) which shows by combining statistics of MRI and sensor array processing, P-MRI can be easily formulated. This paper shows that statistical theory of MRI not only provides basis for MR imaging, reconstruction, and analysis, but also offers means to integrate signal processing methods and image processing methods for solving various MRI related problems.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tianhu Lei "Description of statistical theory of magnetic resonance imaging (MRI)", Proc. SPIE 6142, Medical Imaging 2006: Physics of Medical Imaging, 61420Q (2 March 2006);

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