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Message passing for in-vivo field map estimation in MRI

机译:消息传递,用于MRI中的体内场图估计

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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.
机译:健壮的场图估计对于许多MRI应用都很重要,例如通过磁化率伪影校正进行重建,基于MR的温度图以及水脂分离等。为了以最小的扫描时间进行体内场图估计,以一次重复获取多个图像的多回波成像序列引起了人们的极大兴趣。然而,已经观察到,对于多回波成像序列,场图估计变得较不可靠,尤其是在高场强以及难以获得良好匀场的具有挑战性的解剖结构周围。在本文中,场图估计显示为一个高维组合优化问题,无法通过局部贪婪算法解决。本文描述了一种基于消息传递算法的有效方法,该方法可以全局地近似具有最大后验(MAP)概率的解决方案。

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