Effective Fat Quantification Using Multiple Region Growing Scheme at High-Field MRI

• Published in: IEEE access Vol. 7; pp. 2118 - 2125
• Main Authors: Eun, S. J., Jung, E. Y., Park, D. K., Kim, J. Y.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical imaging; Chemicals; Estimation; fat quantification; Fats; Field map estimation; Field of view; Image quality; Inhomogeneity; Iterative methods; least square fit; Magnetic resonance imaging; multiple region growing scheme; Nonhomogeneous media; Robustness; Separation; water fat separation; Water quality
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2883300

In high-field magnetic resonance imaging, water-fat separation in the presence of B0 field inhomogeneity is an important research. Various field map estimation techniques that use three-point multi-echo acquisitions have been developed for reliable water-fat separation. Among the numerous techniques, iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) has gained considerable popularity as an iterative method for acquiring high-quality water and fat images. Some of the goals for the IDEAL are to obtain an initial estimate of the field map and to ensure that the algorithm arrives at the correct solution that is close to the true value. However, due to the worsened B0 inhomogeneity at high field, IDEAL cannot adjust for meaningful field map estimation, particularly for a large field of view. Previously, to improve the robustness of this estimation, a region-growing technique was developed to take advantage of the 2D linear extrapolation procedure through the seed point set by the median value in the target object. There are some limitations with this approach, for example, the dependence on the initial seed point such as its number, intensity, and position. In this paper, we introduce a robust method called the multiple region-growing scheme that does not need to consider parameters related to accuracy. As a result of the proposed method, we obtained a robust field map estimation that can be applied in high fields with an average accuracy of 91.9% higher than the existing method.