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ELECTRONIC SUPPLEMENTARY MATERIAL
电子补充材料

Evaluation of an electrical impedance tomography-based global inhomogeneity index for pulmonary ventilation distribution
基于电阻抗成像的全球不均匀指数对肺通气分布的评估

Zhanqi Zhao 1 , 2 1 , 2 ^(1,2){ }^{1,2}, Knut Möller 2 2 ^(2){ }^{2}, Daniel Steinmann 1 1 ^(1){ }^{1}, Inéz Frerichs 3 3 ^(3){ }^{3}, Josef Guttmann 1 1 ^(1){ }^{1}
赵占奇 1 , 2 1 , 2 ^(1,2){ }^{1,2} , 克努特·穆勒 2 2 ^(2){ }^{2} , 丹尼尔·施泰因曼 1 1 ^(1){ }^{1} , 伊内兹·弗雷里希斯 3 3 ^(3){ }^{3} , 约瑟夫·古特曼 1 1 ^(1){ }^{1}
1 1 ^(1){ }^{1} Section for Experimental Anesthesiology, Department of Anesthesiology and Critical Care Medicine, University Medical Center, Freiburg, Germany
弗赖堡大学医学中心麻醉学与重症监护医学系实验麻醉学科
2 2 ^(2){ }^{2} Department of Biomedical Engineering, Furtwangen University, Villingen-Schwenningen, Germany
德国菲尔特旺根大学生物医学工程系,威林根-施温宁根
3 3 ^(3){ }^{3} Department of Anesthesiology and Intensive Care Medicine, University Medical Center of Schleswig-Holstein Campus Kiel, Germany
舒勒斯维希-霍尔斯坦大学医学中心基尔校区麻醉学与重症医学系,德国

Address of the corresponding author:
通讯作者的地址:

Zhao, Zhanqi MSc. 赵, 战棋 硕士
Anaesthesiologische Universitätsklinik Freiburg
弗赖堡麻醉学大学医院

Sektion für Experimentelle Anaesthesiologie
实验麻醉科

Hugstetter Straße 55, D-79106 Freiburg, Germany
弗赖堡,德国,D-79106,哈格斯特特街 55 号

Tel: +49-761-2702309 电话:+49-761-2702309
Fax: +49-761-2702328 传真: +49-761-2702328
zhanqi.zhao@uniklinik-freiburg.de

The identification of the lung area based on EIT:
基于电阻抗成像(EIT)确定肺部区域:

The Lung Area Estimation (LAE) method
肺区估测(LAE)方法

Hahn and Frerichs et al. have proposed a functional EIT (fEIT) based identification of the lung boundaries in EIT images. They assume that a pixel belongs to the lung area if the variation of the pixel values in the EIT images during a certain period is larger than a threshold of 20% of the maximum variation [S1-S5]. This approach, called fEIT method in the following, may fail to exclude the heart from the lung. Further in some lung diseases, such as lung cancer and pneumothorax, the ventilation in parts of the lung region may be impaired to such an extent that it is not visible at all in a fEIT image.
Hahn 和 Frerichs 等人提出了一种基于功能电阻抗成像(fEIT)的肺部边界识别方法。他们假设如果 EIT 图像中某一像素在一定时间内的像素值变化超过最大变化的 20%的阈值,则该像素属于肺部区域[S1-S5]。这种方法在下文中称为 fEIT 方法,可能无法将心脏排除在肺部之外。此外,在某些肺部疾病中,如肺癌和气胸,肺部区域的某些部分的通气可能受到损害,以至于在 fEIT 图像中完全不可见。
We recently proposed a novel method for lung area estimation (LAE) [S6]. The LAE method can be described in 3 steps:
我们最近提出了一种新的肺部面积估计(LAE)方法[S6]。LAE 方法可以分为三个步骤:
  1. Identify the lung area with the fEIT method.
    使用 fEIT 方法识别肺部区域。
  2. Mirror the lung area identified in the first step from left to right and from right to left and combine them by a logical OR-operation. The overlaid lung areas of both sides are projected back to the other side.
    将第一步中识别的肺部区域从左到右和从右到左进行镜像,并通过逻辑或运算将它们结合起来。两侧重叠的肺部区域投影回另一侧。
  3. Subtract the cardiac related area from the lung area derived in the second step. Since the heart rate is normally different from respiratory frequency in adults, these two kinds of areas may be well separated in the frequency domain. The impedance variation of every pixel vs. time was analyzed in the frequency domain by Fast Fourier transform (FFT). The total power in the frequency range below (respiratory value) and above 0.8 of the heart frequency (heart value) was determined. The ratio between these two values of power was then compared to an individual threshold θ θ theta\theta (Eq. S1). Pixel ratios smaller than the threshold were subtracted from the lung area.
    从第二步得出的肺部面积中减去与心脏相关的面积。由于成人的心率通常与呼吸频率不同,这两种面积在频域中可能分开很好。通过快速傅里叶变换(FFT)分析每个像素的阻抗随时间的变化。确定心脏频率(心脏值)以上 0.8 和以下(呼吸值)频率范围内的总功率。然后将这两个功率值之间的比率与个体阈值 θ θ theta\theta (方程 S1)进行比较。小于阈值的像素比例从肺部面积中减去。
f = 0 0.8 h f E k ( f ) / f = 0.8 h f s f / 2 E k ( f ) < θ f = 0 0.8 h f E k ( f ) / f = 0.8 h f s f / 2 E k ( f ) < θ sum_(f=0)^(0.8 hf)E_(k)(f)//sum_(f=0.8 hf)^(sf//2)E_(k)(f) < theta\sum_{f=0}^{0.8 h f} E_{k}(f) / \sum_{f=0.8 h f}^{s f / 2} E_{k}(f)<\theta
where E k ( f ) E k ( f ) E_(k)(f)E_{k}(f) represents the energy distribution function of pixel k k kk in frequency domain. h f h f hfh f corresponds to the heart frequency in Hz , s f s f sfs f denotes the sampling frequency in Hz . θ Hz . θ Hz.theta\mathrm{Hz} . \theta is the threshold determined individually. Let
其中 E k ( f ) E k ( f ) E_(k)(f)E_{k}(f) 表示频域中像素 k k kk 的能量分布函数。 h f h f hfh f 对应于 Hz 中的心率, s f s f sfs f 表示 Hz . θ Hz . θ Hz.theta\mathrm{Hz} . \theta 中的采样频率,个体确定的阈值则为。让
R E , k = f = 0 0.8 h f E k ( f ) / f = 0.8 h f s f / 2 E k ( f ) R E , k = f = 0 0.8 h f E k ( f ) / f = 0.8 h f s f / 2 E k ( f ) R_(E,k)=sum_(f=0)^(0.8 hf)E_(k)(f)//sum_(f=0.8 hf)^(sf//2)E_(k)(f)R_{E, k}=\sum_{f=0}^{0.8 h f} E_{k}(f) / \sum_{f=0.8 h f}^{s f / 2} E_{k}(f)
and R E , 1 : n R E , 1 : n R_(E,1:n)R_{E, 1: n} is a vector of all the R E R E R_(E)R_{E} sorted in ascending order of its value. Fig. S1 shows R E , 1 : n R E , 1 : n R_(E,1:n)R_{E, 1: n} of one patient. θ θ theta\theta is equal to the first R E R E R_(E)R_{E} value in R E , 1 : n R E , 1 : n R_(E,1:n)R_{E, 1: n} satisfied R E , i + 1 R E , i < R E , i + 1 R E , i < R_(E,i+1)-R_(E,i) <R_{E, i+1}-R_{E, i}< 0.0014 , i [ 1 , n ) 0.0014 , i [ 1 , n ) 0.0014,i in[1,n)0.0014, i \in[1, \mathrm{n}).
R E , 1 : n R E , 1 : n R_(E,1:n)R_{E, 1: n} 是一个所有 R E R E R_(E)R_{E} 按其值升序排序的向量。图 S1 显示了一名患者的 R E , 1 : n R E , 1 : n R_(E,1:n)R_{E, 1: n} θ θ theta\theta 等于满足 R E , i + 1 R E , i < R E , i + 1 R E , i < R_(E,i+1)-R_(E,i) <R_{E, i+1}-R_{E, i}< 0.0014 , i [ 1 , n ) 0.0014 , i [ 1 , n ) 0.0014,i in[1,n)0.0014, i \in[1, \mathrm{n}) R E , 1 : n R E , 1 : n R_(E,1:n)R_{E, 1: n} 中的第一个 R E R E R_(E)R_{E} 值。
As a result, a quasi-symmetric left and right lobe results that includes all detectable lung but not cardiac related area.
因此,生成一个准对称的左右叶,包括所有可检测的肺部,但不包括与心脏相关的区域。
There are two types of fEIT scans [S7, S8]. The first type calculates the standard deviation of relative impedance change in each image pixel for a certain time period [S9]. The second type calculates the linear regression coefficient in the following regression formula:
有两种类型的 fEIT 扫描[S7, S8]。第一种类型计算每个图像像素在一定时间内相对阻抗变化的标准差[S9]。第二种类型计算以下回归公式中的线性回归系数:
Local Δ Z = α Global Δ Z + β + ε  Local  Δ Z = α  Global  Δ Z + β + ε " Local "Delta Z=alpha*" Global "Delta Z+beta+epsi\text { Local } \Delta Z=\alpha \cdot \text { Global } \Delta Z+\beta+\varepsilon
where Local Δ Z Δ Z Delta Z\Delta Z and Global Δ Z Δ Z Delta Z\Delta Z are local (each pixel) and global (sum of all pixels) relative impedance change for a certain time period, respectively; α , β α , β alpha,beta\alpha, \beta are regression coefficients and ε ε epsi\varepsilon is the fitting error [S10]. The standard deviation values in the first type or the regression coefficient α α alpha\alpha in the second type are plotted in the corresponding pixel locations to build a fEIT image. It is suggested that the second type of fEIT should be used when the environment during data acquisition is very noisy [S7, S8, S11]. But the “noise” that the second type fEIT suppressed may help to assess the pulmonary perfusion [S12], which is essential to identify the non-ventilated lung area (e.g. atelectasis). In particular, the noise level in the present patient data was low and these two types of fEIT images were similar (Fig. S2). Considering the fact that the first type of fEIT may have potential benefits for the collapsed lungs, it was used for LAE method and for fEIT method in stead of the second type.
局部 Δ Z Δ Z Delta Z\Delta Z 和全局 Δ Z Δ Z Delta Z\Delta Z 分别是某一时间段内局部(每个像素)和全局(所有像素之和)相对阻抗变化; α , β α , β alpha,beta\alpha, \beta 是回归系数, ε ε epsi\varepsilon 是拟合误差 [S10]。第一类的标准差值或第二类的回归系数 α α alpha\alpha 被绘制在相应的像素位置,以构建 fEIT 图像。建议在数据采集期间环境噪声很大时使用第二类 fEIT [S7, S8, S11]。然而,第二类 fEIT 所抑制的“噪声”可能有助于评估肺灌注 [S12],这对于识别非通气的肺部区域(例如肺不张)至关重要。特别是,当前患者数据中的噪声水平较低,这两种类型的 fEIT 图像相似(图 S2)。考虑到第一类 fEIT 可能对塌陷的肺部有潜在好处,因此在 LAE 方法和 fEIT 方法中使用了第一类,而不是第二类。

Comparison of LAE method and fEIT method
LAE 方法与 fEIT 方法的比较

Totally 50 patients were studied and divided into control group and test group. For details of patient’s information and protocols, please refer to the “Materials and methods” section of the original article. EIT measurement was performed and the EIT images were analyzed.
共有 50 名患者参与研究,并分为对照组和实验组。有关患者信息和方案的详细信息,请参考原始文章的“材料和方法”部分。进行了电阻抗成像(EIT)测量,并分析了 EIT 图像。
Fig. S3 shows the lung area determined for a patient in control group with fEIT method and LAE method, respectively. Cardiac related area was excluded in the result of LAE method. Fig. S4 shows the lung area identified with these two methods for a patient in the test group. Part of the lung was collapsed and thus was not detected by the fEIT method.
图 S3 显示了通过 fEIT 方法和 LAE 方法分别确定的对照组患者的肺部区域。LAE 方法的结果中排除了心脏相关区域。图 S4 显示了测试组患者通过这两种方法识别的肺部区域。部分肺区域塌陷,因此未被 fEIT 方法检测到。
It is assumed that the fraction of the lung area in the thorax cross-section for different people should be more or less in the same range, in spite of the state of the lung. With fEIT method, the size of lung area identified in the control group S C -fEIT = 361 ± 35.1 S C -fEIT  = 361 ± 35.1 S_(C"-fEIT ")=361+-35.1\mathrm{S}_{\mathrm{C} \text {-fEIT }}=361 \pm 35.1 (mean ± ± +-\pm standard deviation) and in test group S T -fEIT = 299 ± 60.8 S T -fEIT  = 299 ± 60.8 S_(T"-fEIT ")=299+-60.8\mathrm{S}_{\mathrm{T} \text {-fEIT }}=299 \pm 60.8. They differed significantly ( P = 0.004 ) ( P = 0.004 ) (P=0.004)(P=0.004). On the contrary, the sizes estimated with LAE method in control group S C-LAE = 353 ± 27.2 S C-LAE  = 353 ± 27.2 S_("C-LAE ")=353+-27.2\mathrm{S}_{\text {C-LAE }}=353 \pm 27.2 and in test group S T- S T-  S_("T- ")\mathrm{S}_{\text {T- }} LAE = 353 ± 61.1 LAE = 353 ± 61.1 LAE=353+-61.1\mathrm{LAE}=353 \pm 61.1. They did not differ significantly ( P = 0.41 ) ( P = 0.41 ) (P=0.41)(P=0.41). Figure S5 shows the comparison of the size of the identified lung area in quartiles.
据假设,不同人的胸部横截面中肺部面积的比例应该或多或少在相同范围内,尽管肺部的状态不同。使用 fEIT 方法,在对照组中识别出的肺部面积为 S C -fEIT = 361 ± 35.1 S C -fEIT  = 361 ± 35.1 S_(C"-fEIT ")=361+-35.1\mathrm{S}_{\mathrm{C} \text {-fEIT }}=361 \pm 35.1 (均值 ± ± +-\pm 标准差),在实验组中为 S T -fEIT = 299 ± 60.8 S T -fEIT  = 299 ± 60.8 S_(T"-fEIT ")=299+-60.8\mathrm{S}_{\mathrm{T} \text {-fEIT }}=299 \pm 60.8 。它们有显著差异 ( P = 0.004 ) ( P = 0.004 ) (P=0.004)(P=0.004) 。相反,使用 LAE 方法在对照组中估计的面积为 S C-LAE = 353 ± 27.2 S C-LAE  = 353 ± 27.2 S_("C-LAE ")=353+-27.2\mathrm{S}_{\text {C-LAE }}=353 \pm 27.2 ,在实验组中为 S T- S T-  S_("T- ")\mathrm{S}_{\text {T- }} LAE = 353 ± 61.1 LAE = 353 ± 61.1 LAE=353+-61.1\mathrm{LAE}=353 \pm 61.1 。它们没有显著差异 ( P = 0.41 ) ( P = 0.41 ) (P=0.41)(P=0.41) 。图 S5 显示了在四分位数中识别的肺部面积大小的比较。
The special ventilator settings in the control group may have influenced the results because zero PEEP together with the high F I O 2 F I O 2 F_(I)O2\mathrm{F}_{\mathrm{I}} \mathrm{O} 2 during induction of anesthesia is expected to lead to a derecruitment. In the worst case, large parts of dependent lung regions in patients from the control group were collapsed. Due to the mirroring, the lung area determined by the LAE method in the dependent lung regions will be greater or equal to the atelectic lung area determined with the f E I T f E I T fEITf E I T method, i.e. S dependent,C-LAE S dependent,C-fEIT. If S dependent,C-LAE  S dependent,C-fEIT. If  S_("dependent,C-LAE ") >= S_("dependent,C-fEIT. If ")\mathrm{S}_{\text {dependent,C-LAE }} \geq \mathrm{S}_{\text {dependent,C-fEIT. If }} both LAE and fEIT methods are compared with cardiac related areas being subtracted, the mirroring effect alone adds on the average 7 % 7 % 7%7 \% more pixels to the results of the LAE method. Since the original fEIT method includes also cardiac related area, small reduction of the whole lung area size S C -LaE S C -LaE  S_(C"-LaE ")\mathrm{S}_{\mathrm{C} \text {-LaE }} compared to S C -feIt S C -feIt  S_(C"-feIt ")\mathrm{S}_{\mathrm{C} \text {-feIt }} (Fig. S5) may be due to the subtraction of the cardiac related area by the LAE method. The enlargement of S T-LAE S T-LAE  S_("T-LAE ")\mathrm{S}_{\text {T-LAE }} compared to S T-feIt S T-feIt  S_("T-feIt ")\mathrm{S}_{\text {T-feIt }} may be mainly due to the invisibility of the damaged part in one lung by the fEIT method and again the mirror effect of the LAE method, which leads to the increase of P P PP from 0.004 (between control and test group, with fEIT method) to 0.41 (with LAE method). However, it has also been observed that although the LAE method was used, the estimated size of the lung in the test group is still a little bit smaller than in the control group (Fig. S5). The lungs may be damaged to such an extent that neither of both lungs is completely visible in the EIT image. That leads to a decrease of GI and makes the air distribution seem more homogenous. Even so the GI is still high compared to the control group (Fig. 2 in the original article). Further anatomical confirmation with computed tomography (CT) or single photon emission CT (SPECT) is needed.
控制组中特殊的通气设定可能影响了结果,因为在麻醉诱导期间零 PEEP 与高 F I O 2 F I O 2 F_(I)O2\mathrm{F}_{\mathrm{I}} \mathrm{O} 2 一起预期会导致肺泡吸收。在最坏的情况下,控制组患者的大部分依赖性肺区将会塌陷。由于镜像效应,由 LAE 方法在依赖性肺区确定的肺部区域将大于或等于用 f E I T f E I T fEITf E I T 方法确定的无气体肺区面积,即 S dependent,C-LAE S dependent,C-fEIT. If S dependent,C-LAE  S dependent,C-fEIT. If  S_("dependent,C-LAE ") >= S_("dependent,C-fEIT. If ")\mathrm{S}_{\text {dependent,C-LAE }} \geq \mathrm{S}_{\text {dependent,C-fEIT. If }} LAE 和 fEIT 方法都与心脏相关区域进行比较,单纯的镜像效应平均会在 LAE 方法的结果中增加 7 % 7 % 7%7 \% 个像素。由于原始 fEIT 方法也包括心脏相关区域,相比于 S C -feIt S C -feIt  S_(C"-feIt ")\mathrm{S}_{\mathrm{C} \text {-feIt }} (图 S5),整体肺部面积的微小减少 S C -LaE S C -LaE  S_(C"-LaE ")\mathrm{S}_{\mathrm{C} \text {-LaE }} 可能是由于 LAE 方法对心脏相关区域的减去。与 S T-feIt S T-feIt  S_("T-feIt ")\mathrm{S}_{\text {T-feIt }} 相比, S T-LAE S T-LAE  S_("T-LAE ")\mathrm{S}_{\text {T-LAE }} 的扩张可能主要是由于 fEIT 方法看不到一侧肺部受损部分,以及 LAE 方法的镜像效应,这导致从 0.004(控制组和实验组之间,使用 fEIT 方法)增加到 P P PP 的结果。41(使用 LAE 方法)。然而,尽管使用了 LAE 方法,但测试组的肺部估计大小仍然比对照组小一点(图 S5)。肺部可能受到损伤,以至于在 EIT 图像中两侧肺部都无法完全显示。这导致 GI 的降低,使得气体分布看起来更为均匀。尽管如此,与对照组相比,GI 仍然较高(原文图 2)。仍需要使用计算机断层扫描(CT)或单光子发射计算机断层扫描(SPECT)进行进一步的解剖确认。

Comparison of LAE method and V T V T VT\boldsymbol{V T} method
LAE 方法与 V T V T VT\boldsymbol{V T} 方法的比较

Hahn et al. have recently proposed an improved approach (VT method) to image ventilation in fEIT [S8]. In principle it could be also used to determine the lung area by adding a threshold
Hahn 等人最近提出了一种改进的方法(VT 方法)来在 fEIT 中成像通气 [S8]。原则上,它也可以通过添加阈值来用于确定肺部区域。

value. Such a lung area determination method will be called V T V T VTV T method in the following. A direct comparison of the LAE method and the V T V T VTV T method is not possible since the calculation of the V T V T VTV T method is based on raw data, which is not available to us. But according to the description of the authors, the VT method is superior to the first type of fEIT by suppressing the non-ventilatory impedance changes (noise and impedance changes, e.g., from heart action and movement artifacts). Unfortunately, both VT method and fEIT method may fail to discover the nonventilated part of the lung (e.g. atelectasis). On the other hand, the LAE method tries to identify the non-ventilated lung area by mirroring the ventilated lung area.
值。因此,以下将这种肺区确定方法称为 V T V T VTV T 方法。由于 V T V T VTV T 方法的计算基于我们无法获得的原始数据,因此无法直接比较 LAE 方法和 V T V T VTV T 方法。但是根据作者的描述,VT 方法通过抑制非通气阻抗变化(噪声和阻抗变化,如心脏动作和运动伪影)优于第一种类型的 fEIT。不幸的是,VT 方法和 fEIT 方法可能都无法发现肺部的非通气部分(例如,肺不张)。另一方面,LAE 方法试图通过镜像通气肺区来识别非通气肺区。

Actually, both VT method and fEIT method can be used as the first step of the LAE method. However, as mentioned before, excluding the cardiac related area at the beginning (the second type of f E I T f E I T fEITf E I T and V T V T VTV T method) may fail to assess lung aeration therefore fail to percept the nonventilated lung area. Besides, the phase shift because of the inhomogeneous air distribution in the lung may influence the VT method but not the fEIT method. Nevertheless, using the fEIT method may include different source of noise other than cardiac related actions. Considering the noise level in the present patient data was low, the LAE method was calculated based on the f E I T f E I T fEITf E I T method.
实际上,VT 方法和 fEIT 方法都可以用作 LAE 方法的第一步。然而,如前所述,在开始时排除心脏相关区域(第二种 f E I T f E I T fEITf E I T V T V T VTV T 方法)可能无法评估肺部通气,因此无法感知未通气的肺区域。此外,由于肺部空气分布不均匀引起的相位偏移可能会影响 VT 方法,但不会影响 fEIT 方法。然而,使用 fEIT 方法可能会包含心脏相关活动以外的其他噪声源。考虑到目前患者数据的噪声水平较低,LAE 方法是基于 f E I T f E I T fEITf E I T 方法计算的。

Limitations of LAE method
LAE 方法的局限性

Heart may not be the only factor that influences the symmetry of the left and right lungs (e.g. the mediastinum). By mirroring, some pixels which do not belong to the lung area are misleadingly added to the lung area. Subtraction of the cardiac area may not be enough but nevertheless the error is expected to be minimized.
心脏可能不是影响左右肺对称性的唯一因素(例如,纵隔)。通过镜像,一些不属于肺部区域的像素被误导性地添加到肺部区域。心脏区域的减法可能不足,但预计错误将得到最小化。

The LAE method may not perceive the lung regions when both lungs collapse at the same corresponding areas. Multi-frequency EIT may perform better in such extreme case since large impedance differences may exist in different tissues [S13]. The quality and interpretation of multi-frequency EIT measurements is still difficult and further investigations are needed.
LAE 方法可能无法感知肺部区域,当两个肺在相同位置塌陷时。多频率电阻抗成像(EIT)在这种极端情况下可能表现更好,因为不同组织可能存在较大阻抗差异 [S13]。多频率 EIT 测量的质量和解读仍然困难,需要进一步的研究。

REFERENCES 参考文献

S1. Frerichs I, Hinz J, Herrmann P, Weisser G, Hahn G, Dudykevych T, Quintel M, Hellige G (2002) Detection of local lung air content by electrical impedance tomography compared with electron beam CT. J Appl Physiol 93: 660-666
S1. Frerichs I, Hinz J, Herrmann P, Weisser G, Hahn G, Dudykevych T, Quintel M, Hellige G (2002) 通过电阻抗成像与电子束 CT 比较的局部肺气体含量检测. J Appl Physiol 93: 660-666

S2. Frerichs I, Hahn G, Schroder T, Hellige G (1998) Electrical impedance tomography in monitoring experimental lung injury. Intensive Care Med 24: 829-836
S2. Frerichs I, Hahn G, Schroder T, Hellige G (1998) 电阻抗成像在监测实验性肺损伤中的应用。重症监护医学 24: 829-836

S3. Frerichs I, Hahn G, Golisch W, Kurpitz M, Burchardi H, Hellige G (1998) Monitoring perioperative changes in distribution of pulmonary ventilation by functional electrical impedance tomography. Acta Anaesthesiol Scand 42: 721-726
S3. Frerichs I, Hahn G, Golisch W, Kurpitz M, Burchardi H, Hellige G (1998) 通过功能电阻抗成像监测围手术期肺通气分布的变化。Acta Anaesthesiol Scand 42: 721-726

S4. Frerichs I, Hahn G, Hellige G (1999) Thoracic electrical impedance tomographic measurements during volume controlled ventilation-effects of tidal volume and positive end-expiratory pressure. IEEE Trans Med Imaging 18: 764-773
S4. Frerichs I, Hahn G, Hellige G (1999) 在体积控制通气期间的胸部电阻抗成像测量—潮气量和呼气末正压的影响。IEEE Trans Med Imaging 18: 764-773

S5. Hahn G, Frerichs I, Kleyer M, Hellige G, (1996) Local mechanics of the lung tissue determined by functional EIT. Physiological measurement 17 Suppl 4A: A159-166
S5. Hahn G, Frerichs I, Kleyer M, Hellige G, (1996) 通过功能性 EIT 确定的肺组织局部力学。生理测量 17 增刊 4A: A159-166

S6. Zhao Z, Möller K, Steinmann D, Guttmann J (2009) Determination of lung area in EIT images. In: Proceedings of the 3rd conference on iCBBE, Beijing, China. IEEE (in press)
S6. Zhao Z, Möller K, Steinmann D, Guttmann J (2009) 在 EIT 图像中确定肺部区域。载于:第三届 iCBBE 会议论文集,北京,中国。IEEE(待发表)

S7. Pulletz S, van Genderingen HR, Schmitz G, Zick G, Schadler D, Scholz J, Weiler N, Frerichs I (2006) Comparison of different methods to define regions of interest for evaluation of regional lung ventilation by EIT. Physiol Meas 27: S115-127
S7. Pulletz S, van Genderingen HR, Schmitz G, Zick G, Schadler D, Scholz J, Weiler N, Frerichs I (2006) 通过电阻抗成像(EIT)评估区域肺通气的不同区域定义方法比较。生理测量 27: S115-127

S8. Hahn G, Dittmar J, Just A, Hellige G (2008) Improvements in the image quality of ventilatory tomograms by electrical impedance tomography. Physiol Meas 29: S51-61
S8. Hahn G, Dittmar J, Just A, Hellige G (2008) 通过电阻抗成像改善通气断层图像质量。生理测量 29: S51-61

S9. Hahn G, Sipinkova I, Baisch F, Hellige G (1995) Changes in the thoracic impedance distribution under different ventilatory conditions. Physiol Meas 16: A161-173
S9. Hahn G, Sipinkova I, Baisch F, Hellige G (1995) 在不同通气条件下胸腔阻抗分布的变化。生理测量 16: A161-173

S10. Kühnel G, Hahn G, Frerichs I, Schröder T, Hellige G (1997) Neue Verfahren zur Verbesserung der Abbildungsqualität bei funktionellen EIT-Tomogrammen der Lunge. Biomed Technol Berl 42 Suppl: 470-471
S10. Kühnel G, Hahn G, Frerichs I, Schröder T, Hellige G (1997) 新方法改善肺功能性 EIT 断层扫描的成像质量。生物医学技术柏林 42 增刊: 470-471

S11. Pulletz S, Elke G, Zick G, Schadler D, Scholz J, Weiler N, Frerichs I (2008) Performance of electrical impedance tomography in detecting regional tidal volumes during one-lung ventilation. Acta Anaesthesiol Scand 52:1131-1139
S11. Pulletz S, Elke G, Zick G, Schadler D, Scholz J, Weiler N, Frerichs I (2008) 一肺通气期间电阻抗成像检测区域潮气量的表现。麻醉学报 52:1131-1139

S12. Schibler A, Calzia E (2008) Electrical impedance tomography: a future item on the “Christmas Wish List” of the intensivist? Intensive Care Med 34: 400-401
S12. Schibler A, Calzia E (2008) 电阻抗成像:重症监护医生未来的“圣诞愿望清单”之一?重症监护医学 34: 400-401

S13. Oh TI, Koo H, Lee KH, Kim SM, Lee J, Kim SW, Seo JK, Woo EJ (2008) Validation of a multi-frequency electrical impedance tomography (mfEIT) system KHU Mark1: impedance spectroscopy and time-difference imaging. Physiol Meas 29: 295-307
S13. Oh TI, Koo H, Lee KH, Kim SM, Lee J, Kim SW, Seo JK, Woo EJ (2008) 多频电阻抗成像(mfEIT)系统 KHU Mark1 的验证:阻抗光谱与时间差成像。生理测量 29: 295-307

FIGURE LEGENDS 图例

Figure S 1 : R E S 1 : R E S1:R_(E)\mathrm{S} 1: R_{E}, Ratio of energy distributions between two frequency ranges. R E R E R_(E)R_{E} were sorted in ascending order. The higher the R E R E R_(E)R_{E} was, the higher the respiratory related activity presented. N is the number of pixels. Dashed line denotes the threshold θ θ theta\theta in Eq. S1. Figure adapted from Ref. S6 Figure S2: Two different types of fEIT images. Left, the first type: the standard deviations of relative impedance change in each image pixel for a certain time period were used; right, the second type: the regression coefficient α α alpha\alpha in Eq. S3 were used.
S 1 : R E S 1 : R E S1:R_(E)\mathrm{S} 1: R_{E} ,两个频率范围之间能量分布比率。 R E R E R_(E)R_{E} 按升序排列。 R E R E R_(E)R_{E} 越高,呼吸相关活动越高。N 是像素数量。虚线表示阈值 θ θ theta\theta 在方程 S1 中。图改编自参考文献 S6 图 S2:两种不同类型的 fEIT 图像。左侧,第一种类型:在特定时间段内,每个图像像素相对阻抗变化的标准偏差被使用;右侧,第二种类型:在方程 S3 中使用的回归系数 α α alpha\alpha
Figure S3: Comparison of the lung area identified with different methods for a patient in control group. A: fEIT image of the patient; B: fEIT method and C: LAE method.
图 S3:对照组患者使用不同方法识别的肺部区域比较。A:患者的 fEIT 图像;B:fEIT 方法;C:LAE 方法。
Figure S4: Comparison of the lung area identified with different methods for a patient in test group. A: fEIT image of the patient; B : fEIT method and C: LAE method.
图 S4:测试组患者使用不同方法识别的肺区域比较。A:患者的 fEIT 图像;B:fEIT 方法;C:LAE 方法。
Figure S5: The comparison of the size of the lung area identified solely with the fEIT method and the LAE method. C-fEIT, C-LAE: sizes of the identified lung area in control group (healthy) with fEIT method and with LAE method, respectively; T-fEIT, T-LAE: sizes of the identified lung area in test group (during TLV) with fEIT method and with LAE method, respectively. The boxes marked the quartiles while the whiskers extend from the box out to the most extreme data value within 1.5 × 1.5 × 1.5 xx1.5 \times the interquartile range of the sample. *, significantly different from C-fEIT ( P = 0.004 P = 0.004 P=0.004P=0.004 ). No significant difference was found between C-LAE and T-LAE ( P = 0.41 ) ( P = 0.41 ) (P=0.41)(P=0.41)
图 S5:仅使用 fEIT 方法和 LAE 方法识别的肺区域大小的比较。C-fEIT、C-LAE:对照组(健康)中通过 fEIT 方法和通过 LAE 方法识别的肺区域大小;T-fEIT、T-LAE:测试组(在 TLV 期间)中通过 fEIT 方法和通过 LAE 方法识别的肺区域大小。框中标记四分位数,而触须则从框向外延伸到样本的四分位数范围内最极端的数据值。*,与 C-fEIT 有显著差异( P = 0.004 P = 0.004 P=0.004P=0.004 )。在 C-LAE 和 T-LAE 之间没有发现显著差异 ( P = 0.41 ) ( P = 0.41 ) (P=0.41)(P=0.41)

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