Joint estimation of water/fat images and field inhomogeneity map

Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Magnetic Resonance in Medicine (Impact Factor: 3.4). 03/2008; 59(3):571-80. DOI: 10.1002/mrm.21522
Source: PubMed

ABSTRACT Water/fat separation in the presence of B 0 field inhomogeneity is a problem of considerable practical importance in MRI. This article describes two complementary methods for estimating the water/fat images and the field inhomogeneity map from Dixon-type acquisitions. One is based on variable projection (VARPRO) and the other on linear prediction (LP). The VARPRO method is very robust and can be used in low signal-to-noise ratio conditions because of its ability to achieve the maximum-likelihood solution. The LP method is computationally more efficient, and is shown to perform well under moderate levels of noise and field inhomogeneity. These methods have been extended to handle multicoil acquisitions by jointly solving the estimation problem for all the coils. Both methods are analyzed and compared and results from several experiments are included to demonstrate their performance.

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Available from: Brad P Sutton, Jan 07, 2014
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    • "There are two parts in the objective function formulated in VARPRO: One is the data cost that measures the goodness of fit at an individual pixel; the other is the smoothness cost that measures the similarity of field map values at neighboring pixels. The optimization problem is tackled by iterated conditional modes (ICM) algorithm in [9] and graph cut algorithm in [10], respectively. The regularization parameter, which is often empirically chosen to combine the two parts in the objective function, determines the degree of smoothing in the resultant field maps. "
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    ABSTRACT: Water-fat separation in magnetic resonance imaging (MRI) is of great clinical importance, and the key to uniform water-fat separation lies in field map estimation. This work deals with three-point field map estimation, in which water and fat are modelled as two single-peak spectral lines, and field inhomogeneities shift the spectrum by an unknown amount. Due to the simplified spectrum modelling, there exists inherent ambiguity in forming field maps from multiple locally feasible field map values at each pixel. To resolve such ambiguity, spatial smoothness of field maps has been incorporated as a constraint of an optimization problem. However, there are two issues: the optimization problem is computationally intractable and even when it is solved exactly, it does not always separate water and fat images. Hence, robust field map estimation remains challenging in many clinically important imaging scenarios. This paper proposes a novel field map estimation technique called JIGSAW. It extends a loopy belief propagation (BP) algorithm to obtain an approximate solution to the optimization problem. The solution produces locally smooth segments and avoids error propagation associated with greedy methods. The locally smooth segments are then assembled into a globally consistent field map by exploiting the periodicity of the feasible field map values. In vivo results demonstrate that JIGSAW outperforms existing techniques and produces correct water-fat separation in challenging imaging scenarios.
    IEEE Transactions on Medical Imaging 08/2011; 30(7-30):1417 - 1426. DOI:10.1109/TMI.2011.2122342 · 3.80 Impact Factor
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    • "We initialize this scheme by setting [ ] 0 α = n . This approach is similar to the VARPRO formulation, used by [15]. The main differences are 1) the inclusion of the decay term in α , which enables us to consider larger delays and 2) the use of more delay terms to improve the estimates. "
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    ABSTRACT: We introduce a novel algorithm to address the challenges in magnetic resonance (MR) spectroscopic imaging. In contrast to classical sequential data processing schemes, the proposed method combines the reconstruction and postprocessing steps into a unified algorithm. This integrated approach enables us to inject a range of prior information into the data processing scheme, thus constraining the reconstructions. We use high resolution, 3-D estimate of the magnetic field inhomogeneity map to generate an accurate forward model, while a high resolution estimate of the fat/water boundary is used to minimize spectral leakage artifacts. We parameterize the spectrum at each voxel as a sparse linear combination of spikes and polynomials to capture the metabolite and baseline components, respectively. The constrained model makes the problem better conditioned in regions with significant field inhomogeneity, thus enabling the recovery even in regions with high field map variations. To exploit the high resolution MR information, we formulate the problem as an anatomically constrained total variation optimization scheme on a grid with the same spacing as the magnetic resonance imaging data. We analyze the performance of the proposed scheme using phantom and human subjects. Quantitative and qualitative comparisons indicate a significant improvement in spectral quality and lower leakage artifacts.
    04/2010; 29(6):1297-309. DOI:10.1109/TMI.2010.2046673
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    • "Existing techniques for multi-point field map estimation include region growing [1], VARPO [2] and region unwrapping method [3] used in [4]. Region growing algorithms assume that most field map values are within the spectral FOV centered at 0, and large field map values can be extrapolated. "
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    ABSTRACT: Robust field map estimation is important to many MRI applications, such as reconstruction with correction of susceptibility artifacts, MR-based temperature mapping, and water-fat separation. To enable in-vivo field map estimation with minimal scan times, multi-echo imaging sequences, which acquire multiple images in a single repetition, are gaining great interest. However, it has been observed that field map estimation becomes less reliable with multi-echo imaging sequences, especially at high field strengths and around challenging anatomies where good shimming cannot be obtained. In this paper, the field map estimation is shown to be a high-dimensional combinatorial optimization problem, which cannot be addressed by local greedy algorithms. This paper describes an effective approach based on message passing algorithm to globally approximate a solution with maximum a posterior (MAP) probability.
    Proceedings of the 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, The Netherlands, 14-17 April, 2010; 01/2010
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