利用磁共振电阻抗断层扫描(MREIT)重建各向同性电导的稳定性

Haiyang Wang, Yizhuang Song
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摘要

磁共振电阻抗断层成像(MREIT)是一种高分辨率成像模式,其目的是利用从磁共振成像扫描仪获得的磁通密度中可测量的 $z$-th 分量,在低频下重建物体的电导率分布。传统的 MREIT 重建算法使用两个线性独立电流密度的两个数据。然而,这种 MREIT 图像的时间分辨率相对较低。最近,有人提出了单电流 MREIT 来提高时间分辨率。尽管在过去二十年中提出了一系列重建算法,但对 MREIT 的理论研究仍然相当有限。本文提出了使用 MREIT 进行基于两个基准和单一数据的各向同性电导率重建的稳定性定理。利用椭圆偏微分方程的正则性理论,我们证明了 MREIT 逆问题中唯一的不稳定性来自于对测量数据 $B_z$(磁通密度的 $z$-th 分量)的二阶导数。为了从有噪声的 $B_z$ 数据中获得稳定的重建,我们注意到 $\nabla B_z$ 的边缘结构揭示了未知电导率的边缘特征,并为 $\nabla B_z$ 数据提供了一种边缘保留的去噪方法。我们使用改进的 Shepp-Logan 幻影模型来验证所提出的理论和去噪方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stability of the isotropic conductivity reconstruction using magnetic resonance electrical impedance tomography (MREIT)
Magnetic resonance electrical impedance tomography (MREIT) is a high-resolution imaging modality that aims to reconstruct the objects' conductivity distributions at low frequency using the measurable $z$-th component of the magnetic flux density obtained from an MRI scanner. Traditional reconstruction algorithms in MREIT use two data subject to two linearly independent current densities. However, the temporal resolution of such a MREIT image is relatively low. Recently, a single current MREIT has been proposed to improve the temporal resolution. Even though a series of reconstruction algorithms have been proposed in the last two decades, the theoretical studies of MREIT are still quite limited. This paper presents the stability theorems for two datum and a single data-based isotropic conductivity reconstruction using MREIT. Using the regularity theory of elliptic partial differential equations, we prove that the only instability in the inverse problem of MREIT comes from taking the second-order derivative of the measured data $B_z$, the $z$-th component of the magnetic flux density. To get a stable reconstruction from the noisy $B_z$ data, we note that the edge structure of $\nabla B_z$ reveals the edge features in the unknown conductivity and provides an edge-preserving denoising approach for the $\nabla B_z$ data. We use a modified Shepp-Logan phantom model to validate the proposed theory and the denoising approach.
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