Department of Computer Engineering, Engineering Faculty, University of Isfahan, Isfahan, Iran.
Biomed Eng Online. 2012 Jun 20;11:34. doi: 10.1186/1475-925X-11-34.
Electrical Impedance Tomography (EIT) is used as a fast clinical imaging technique for monitoring the health of the human organs such as lungs, heart, brain and breast. Each practical EIT reconstruction algorithm should be efficient enough in terms of convergence rate, and accuracy. The main objective of this study is to investigate the feasibility of precise empirical conductivity imaging using a sinc-convolution algorithm in D-bar framework.
At the first step, synthetic and experimental data were used to compute an intermediate object named scattering transform. Next, this object was used in a two-dimensional integral equation which was precisely and rapidly solved via sinc-convolution algorithm to find the square root of the conductivity for each pixel of image. For the purpose of comparison, multigrid and NOSER algorithms were implemented under a similar setting. Quality of reconstructions of synthetic models was tested against GREIT approved quality measures. To validate the simulation results, reconstructions of a phantom chest and a human lung were used.
Evaluation of synthetic reconstructions shows that the quality of sinc-convolution reconstructions is considerably better than that of each of its competitors in terms of amplitude response, position error, ringing, resolution and shape-deformation. In addition, the results confirm near-exponential and linear convergence rates for sinc-convolution and multigrid, respectively. Moreover, the least degree of relative errors and the most degree of truth were found in sinc-convolution reconstructions from experimental phantom data. Reconstructions of clinical lung data show that the related physiological effect is well recovered by sinc-convolution algorithm.
Parametric evaluation demonstrates the efficiency of sinc-convolution to reconstruct accurate conductivity images from experimental data. Excellent results in phantom and clinical reconstructions using sinc-convolution support parametric assessment results and suggest the sinc-convolution to be used for precise clinical EIT applications.
电阻抗断层成像（EIT）被用作一种快速的临床成像技术，用于监测肺部、心脏、大脑和乳房等人体器官的健康状况。每个实用的 EIT 重建算法都应该在收敛速度和准确性方面具有足够的效率。本研究的主要目的是研究在 D-bar 框架中使用 sinc 卷积算法进行精确经验电导率成像的可行性。
首先，使用合成和实验数据计算称为散射变换的中间目标。接下来，将该对象用于二维积分方程，该方程通过 sinc 卷积算法精确快速地求解，以找到图像中每个像素的电导率的平方根。为了进行比较，在类似的设置下实现了多网格和 NOSER 算法。使用 GREIT 认可的质量指标测试了对合成模型的重建质量。为了验证仿真结果，使用了一个胸部和一个人体肺部的重建。
对合成重建的评估表明，在幅度响应、位置误差、振铃、分辨率和形状变形方面，sinc 卷积重建的质量明显优于每个竞争对手。此外，结果分别证实了 sinc 卷积和多网格的近指数和线性收敛速度。此外，在实验体模数据的 sinc 卷积重建中发现了最小的相对误差程度和最大的真实程度。临床肺部数据的重建表明，sinc 卷积算法能够很好地恢复相关的生理效应。
参数评估证明了 sinc 卷积从实验数据中重建准确电导率图像的效率。使用 sinc 卷积在体模和临床重建中获得的优异结果支持参数评估结果，并表明 sinc 卷积可用于精确的临床 EIT 应用。
