Abstract / truncated to 115 words (read the full abstract)

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties. Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information. In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local ... toggle 11 keywords

parameter estimation – efficient estimation algorithms – non-convex optimization – multicomponent t2 relaxometry – artifact reduction – t2 mapping – denoising – phase estimation – rf design – mr thermometry – in-vivo brain

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