Brandstätter Bernhard, Hollaus Karl, Hutten Helmut, Mayer Michael, Merwa Robert, Scharfetter Hermann
Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Kopernikusgasse 24, A-8010 Graz, Austria.
Physiol Meas. 2003 May;24(2):437-48. doi: 10.1088/0967-3334/24/2/355.
A major drawback of electrical impedance tomography is the poor quality of the conductivity images, i.e., the low spatial resolution as well as large errors in the reconstructed conductivity values. The main reason is the necessity for regularization of the ill-conditioned inverse problem which results in excessive spatial low-pass filtering. A novel regularization method (SMORR (spectral modelling regularized reconstructor)) is proposed, which is based on the inclusion of spectral a priori information in the form of appropriate tissue models (e.g. Cole models). This approach reduces the ill-posedness of the inverse problem, when multifrequency data are available. An additional advantage is the direct reconstruction of the (physiological) tissue parameters of interest instead of the conductivities. SMORR was compared with posterior fitting of a Cole model to the conductivity spectra obtained with a classical iterative reconstruction scheme at various frequencies. SMORR performed significantly better than the reference method concerning robustness against noise in the data.
电阻抗断层成像的一个主要缺点是电导率图像质量较差,即空间分辨率低以及重建电导率值存在较大误差。主要原因是对不适定逆问题进行正则化的必要性,这会导致过度的空间低通滤波。提出了一种新颖的正则化方法(SMORR(光谱建模正则化重建器)),该方法基于以适当的组织模型(例如科尔模型)形式纳入光谱先验信息。当有多频数据可用时,这种方法可降低逆问题的不适定性。另一个优点是直接重建感兴趣的(生理)组织参数而非电导率。将SMORR与在不同频率下使用经典迭代重建方案获得的电导率谱的科尔模型后验拟合进行了比较。在数据抗噪声鲁棒性方面,SMORR的表现明显优于参考方法。