## 4 Experiment
### Results  
This paper aims to evaluate and compare the effectiveness of the second-order TV algorithm with the existing classic algorithms in terms of their numerical performance. To achieve this, the EIT simulation experiments were conducted using the pyEIT environment in Python 3.10 [43]. The experiment was performed on a Windows 10 64-bit operating system, with an Intel Core i7-10510U CPU 1.80 GHz-2.30 GHz processor and 16 GB of memory. The test data consisted of chest contour point coordinates obtained from EIDORS's chest simulation, utilizing simulation data from 16 full-electrode adjacent excitation modes [44]. Figure 3 displays the reconstructed images obtained using five regularization methods: Singular-Value Decomposition (SVD) [45], TV [46], Accelerated TV [47], total variation Augmented Lagrangian Alternating Direction Algorithm (TVAL3) [48], and our SOTV  
method. Upon comparisons, it became evident that the TGV regularization method produced an image with a minimal ladder effect to the naked eye, unlike SVD and TV. The SVD regularization method yielded the lowest image quality, with oversmoothed edges. In comparison to SVD, the TV regularization method significantly improved the reconstructed image quality and better preserved the edge features. The TVAL3 method outperformed the former two methods, particularly in terms of artifact removal. However, all these methods still exhibited a step effect. Nevertheless, the SOTV regularization method effectively reduced the step effect observed in the TV regularization method and avoided the excessive edge smoothing effects in the SVD and TVAL3 regularization methods. The analysis described above was based solely on visual judgments and does not establish strong credibility. Therefore, it was necessary to use the two imaging index evaluation methods mentioned in Table 1 to evaluate the specific imaging quality of each method. The two optimal indicators corresponding to the four methods are highlighted in bold black font. The PSNR value of the image reconstructed by the four methods indicated that the SOTV algorithm had the highest PSNR value, demonstrating that it was significantly better than the other three algorithms in terms of suppressing the step effect. Similarly, the calculated LPIPS values also supported this point, as the LPIPS value of the TGV algorithm was the smallest among the four algorithms for the three different models, indicating higher image similarity. However, the LPIPS values of the SVD and TV algorithms were relatively high, suggesting low similarity between the original image and the reconstructed image. Figure 3: Reconstruction results produced by the 5 different algorithms. The experiment setup is shown in Figure 4. To replicate the impedance disturbance of the lungs, we constructed a circular water tank physical model in Figure 5a. The model consisted of sixteen electrodes placed at separate holes on the same level of the tank. These electrodes were made of brass pillars with excellent conductivity. To ensure proper sealing and prevent any leakage of experimental liquid that could affect the test results, double-sided silicone rings were used at the holes. The water tank was filled with a solution composed of water and sodium chloride, with a mass ratio of 1000:3 [49], to mimic the environment of biological tissue solution in Figure 5b. Additionally, a 4 cm acrylic cylinder was immersed in the sodium chloride solution within the water tank to simulate the disturbance of intracerebral hemorrhage (Figure 5c). The experimental process involved fully stirring the prepared sodium chloride solution to ensure an even distribution. The solution was then left to settle for a specific period of time before being transferred into an acrylic cylinder. Once the sodium chloride solution was stable, the excitation current was set to 1 mA, and data were collected at a frequency of 100 kHz. Following the processing and conversion of the acquired data into the pyEIT format, the following Figure 6 was produced.