Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, USA.
Magn Reson Med. 2017 Nov;78(5):2011-2021. doi: 10.1002/mrm.26590. Epub 2016 Dec 30.
To develop a novel regularized, model-based approach to phase-based conductivity mapping that uses structural information to improve the accuracy of conductivity maps.
The inverse of the three-dimensional Laplacian operator is used to model the relationship between measured phase maps and the object conductivity in a penalized weighted least-squares optimization problem. Spatial masks based on structural information are incorporated into the problem to preserve data near boundaries. The proposed Inverse Laplacian method was compared against a restricted Gaussian filter in simulation, phantom, and human experiments.
The Inverse Laplacian method resulted in lower reconstruction bias and error due to noise in simulations than the Gaussian filter. The Inverse Laplacian method also produced conductivity maps closer to the measured values in a phantom and with reduced noise in the human brain, as compared to the Gaussian filter.
The Inverse Laplacian method calculates conductivity maps with less noise and more accurate values near boundaries. Improving the accuracy of conductivity maps is integral for advancing the applications of conductivity mapping. Magn Reson Med 78:2011-2021, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
开发一种基于正则化、基于模型的相位电阻率映射新方法，该方法利用结构信息来提高电阻率图的准确性。
在带惩罚的加权最小二乘优化问题中，使用三维拉普拉斯算子的逆来模拟测量相位图与物体电导率之间的关系。基于结构信息的空间掩模被纳入到该问题中，以保持边界附近的数据。将提出的逆拉普拉斯方法与模拟、体模和人体实验中的受限高斯滤波器进行了比较。
在模拟中，逆拉普拉斯方法比高斯滤波器产生的重建偏差和噪声引起的误差更小。与高斯滤波器相比，逆拉普拉斯方法还在体模中产生了更接近测量值的电阻率图，并减少了人脑的噪声。
逆拉普拉斯方法计算的电阻率图具有更少的噪声和边界附近更准确的值。提高电阻率图的准确性对于推进电阻率映射的应用至关重要。磁共振医学杂志 78：2011-2021，2017。©2016 国际磁共振医学学会。
