Water and Fat Image Reconstruction in Magnetic Resonance Imaging

The reconstruction of water and fat images based on chemical shift-encoded MRI has been under intensive investigation ever since 1980's, due to its significance in biomedical and clinical researches. Various approaches have been proposed, but robust reconstruction with respect to high and ultra-high MRI remains challenging. Major challenges include: (1) Ill-posedness arising from the non-linear MR signal model. Multiple solutions may exist at pixels, whereas improper selection of solutions leads to severe estimation errors. (2) Large field inhomogeneity variation in MR field complicates reconstruction. Approaches validated on low field inhomogeneities may disfunction in the presence of large field inhomogeneity variation. (3) Critical demanding of biomedical and clinical applications on accuracy and robustness. Estimates must be at minimal error rate to be acceptable.

Since estimation of MR field map is crucial to water and fat image reconstruction, this dissertation mainly focuses on robust recovery of field map. The approach developed with this thesis research consists of a Markov random field (MRF) based energy model for casting the estimation of field map to extensively studied MRF energy optimization, and a novel Iterated Conditional Modes (ICM) algorithm providing high-performance MRF energy optimization. Compared to MRF energy model seen elsewhere, the proposed NLR-MRF model is characteristic of non-linear least residual data cost terms and background masking accounted for improvement of accuracy and efficiency. The major components of the novel ICM algorithm are the stability tracking (ST) and median initialization algorithms. The stability tracking algorithm dynamically keeps track of iterative stability at each pixel to avoid redundant computations. It is demonstrated that with an optimal configuration, the ST algorithm can substantially speed up the ICM iterative computations with accuracy compromise. Median based algorithms are developed to address the high sensitivity of ICM to initialization. By assuming the quasi-unimodality of MR field inhomogeneity, the median-based initialization algorithm identifies all unambiguous pixels in the whole MR field, estimates the median and uniformly or block-wisely sets the median as the initial guess. Experimental validation on synthetic and a large group of in vivo 7 Tesla mouse datasets demonstrate the robustness of the proposed approach.