Pulmonary electrical impedance tomography based on deep recurrent neural networks

Hanyun Wen; Jianping Li; Jun Zhang; Suqin Zhang; Wei Jiang; Xingxing Zhou; Zhenzhong Song

REVIEW 4 major objections 5 minor 9 references

Pulmonary electrical impedance tomography based on deep recurrent neural networks

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read This paper establishes that stacking LSTM and BiLSTM layers improves lung EIT image reconstruction, raising correlation coefficients up to 27.5% over a single LSTM.

desk verdict A modest simulation study of stacked LSTM/BiLSTM for EIT whose headline gains are unsupported by the experiment as reported. read the letter →

arxiv 2504.14521 v1 pith:VAX2KYMZ submitted 2025-04-20 physics.bio-ph

Zhenzhong Song , Jianping Li , Jun Zhang , Hanyun Wen , Suqin Zhang , Wei Jiang , Xingxing Zhou This is my paper · ORCID

classification physics.bio-ph

keywords ElectricalimpedancetomographyLSTMBideeprecurrentneuralnetworklungimagingimagereconstructionconductivitydistributioncorrelationcoefficient

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that stacking long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) layers noticeably improves the quality of electrical impedance tomography (EIT) images of the lungs. Using simulated boundary voltages from 576-element finite element chest and circular models, it trains four stacked networks and finds that they match or beat their single-layer baselines in most test cases, with the largest gains on difficult reconstructions. The strongest reported improvements are 27.5% for LSTM-LSTM over LSTM and 25.4% for LSTM-BiLSTM over LSTM on circular models, with 11.7% and 13.4% gains for BiLSTM-based stacks over BiLSTM. The authors conclude that recurrent depth suppresses artifacts and contour errors and that the two LSTM-fronted stacks are generally superior. The result matters because EIT is a radiation-free, real-time bedside lung monitor whose clinical usefulness is limited by reconstructed image quality.

What carries the argument

The engine of the argument is the deep recurrent stack. A single LSTM cell propagates an internal state $c_t$ under forget, input, and output gates; a BiLSTM layer runs two such cells in opposite directions over the same input. Stacking means feeding the hidden-state sequence of the first recurrent layer into a second recurrent layer, and the paper builds the four combinations in both orders. Each network maps 40 boundary voltages collected by an 8-electrode adjacent-excitation dynamic EIT system into 576 finite element conductivities, and the correlation coefficient between the reconstructed and true conductivity distributions is the score that carries the comparison.

What would settle it

Run the same stacks and baselines on boundary voltages from a physical thorax phantom with known conductivity inclusions, or from patients whose conductivity maps are obtainable from CT; if the stacked networks' correlation coefficients do not beat single-layer baselines on this noisy real-electrode data, the central claim fails. A minimal version is to add measurement noise and random contact impedances to the simulated voltages and re-measure the reported 27.5% and 25.4% gains.

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Extended reading notes

Core claim

The central claim is that stacking LSTM and BiLSTM layers—LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, BiLSTM-BiLSTM—reconstructs simulated lung conductivity distributions from boundary voltages more accurately than either network used alone. The simulations support this by correlation coefficients: on five circular models, LSTM-LSTM reaches 0.9159 and LSTM-BiLSTM 0.9111, while on five lung models the maxima are 0.9691 and 0.9714. The largest relative gains appear where the base algorithm is weakest: circular model CIR3 improves from 0.6650 with LSTM to 0.8477 with LSTM-LSTM (a 27.5% gain), and lung model L5 improves from 0.7365 to 0.9552 (about 29.7%). The paper interprets the gains as artifact reduction and contour correction and adds the caveat that stacking increases training time, so extra depth should be added deliberately.

Load-bearing premise

The load-bearing premise is that clean synthetic boundary voltages from a 576-element finite element model stand in for real clinical lung EIT; if electrode noise, contact impedance, or patient anatomy shift the voltage-to-conductivity map, the stacked networks' correlation gains may not reproduce outside simulation.

Editorial extensions

If this is right

Single-layer LSTM or BiLSTM reconstruction software can be upgraded by stacking an extra recurrent layer without any change to the 8-electrode measurement protocol, so the reported quality gain is available at the algorithm level.
The largest relative gains occur where the single-layer baseline is weakest, so stacking primarily repairs artifact-heavy, hard reconstructions rather than polishing already good images.
Because the four stacked variants cluster closely in maximum lung-model correlation (0.9691 to 0.9736), choice among them can be driven by training time or implementation convenience rather than by a decisive accuracy gap.
The training-time caveat implies a design rule: add recurrent depth only while the extra correlation repays the compute cost, which is exactly the trade-off the paper endorses.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The comparisons use clean simulated voltages with no electrode contact impedance or measurement noise, so the reported percentage gains should be read as upper bounds until the same stacks are tested on noisy physical data; that test is the natural next experiment.
Since BiLSTM layers read the voltage sequence in both directions, stacked BiLSTM variants may show larger advantages on time-resolved ventilation sequences than on the static conductivity snapshots evaluated here, a hypothesis a temporal correlation metric could test.
The 27.5% gain on CIR3 comes from a case where baseline LSTM performs worst, which suggests stacking may be most valuable as a corrective stage attached to a weak base rather than as a universal improvement; ablating each added layer on easy and hard cases would test this.
Transferring from 60,000 synthetic finite element datasets to bedside monitoring would likely require simulated noise augmentation or fine-tuning on measured voltages, because the training distribution contains none of the artifacts of real electrodes.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The useful thing to know about this paper is that it reports a purely simulated comparison of stacked LSTM/BiLSTM networks for EIT image reconstruction, and the headline numbers (27.5%, 25.4%, 11.7%, 13.4%) come from a handful of test models. The second thing: the central claim that stacking 'effectively enhances' image quality is not supported by the experiment as reported, because the stacked networks are never compared against baselines with matched capacity or optimization budget.

What is new is modest: prior papers (refs 25–26) already apply LSTM and BiLSTM to electrical tomography, so the contribution here is four specific stacking combinations, tested on finite-element models with 576 elements. The paper describes the network components clearly and reports CC values for each test model in Tables 2–3, which is more transparent than many reconstruction papers. The idea of stacking is natural and worth a controlled test.

The controlling problem is experimental rigor. There are no training details—no hidden sizes, epochs, learning rates, initialization, or random seeds. Each CC is a single number with no repeated runs, error bars, or statistical testing. Table 4 even contains negative ratios (LSTM-LSTB on L3, BiL-BiLB on L4), which the text acknowledges in passing but then overrides with 'generally superior.' Because the stacked networks have extra layers and therefore more parameters and longer training, the observed gains could reflect model size or optimization effort rather than the architectural operation of stacking. A proper control would train a single-layer LSTM with comparable parameter count, or tune hyperparameters separately for each architecture. Without that, the 27.5% maximum increase is not attributable to stacking. There is also no external benchmark against a conventional EIT reconstruction algorithm, and the data is entirely synthetic and noiseless, so the abstract's suggestion that this is a 'valuable approach' for clinical lung monitoring is unsupported.

Who is this paper for? A reader curious about how recurrent nets might behave in EIT, but not as evidence for any method's superiority. I would not cite it for the claimed effect. The paper needs major additional work: matched-capacity baselines, repeated runs, error bars, proper statistics, and ideally noisy or experimental data. If I were the editor, I'd desk reject it with an invitation to resubmit after that work; it is too thin as is. That said, the question—whether stacking recurrent layers helps EIT—is legitimate and the paper gives a starting point.

Referee Report

4 major / 5 minor

Summary. The paper proposes four stacked recurrent architectures (LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, BiLSTM-BiLSTM) for lung electrical impedance tomography (EIT) image reconstruction. The authors train and evaluate these models on simulated circular and lung cross-section finite-element models, report correlation coefficients (CC) between reconstructed and ground-truth conductivity distributions, and compute relative increases over single-layer LSTM and BiLSTM baselines. The central claim is that stacking LSTM/BiLSTM layers improves reconstruction quality, with maximum CC increases of 27.5%, 25.4%, 11.7%, and 13.4% over the corresponding baselines. The paper concludes that stacked neural networks are a promising approach for lung EIT image enhancement.

Significance. If the central claim is validated, the paper would offer a simple and potentially useful empirical contribution to deep-learning-based EIT reconstruction, which is an active area. The use of the correlation coefficient as a standard image-quality metric and the tabulation of per-model results are positive features. However, the experiments as reported lack the controls needed to attribute observed improvements to stacking rather than to confounds such as model capacity, optimization effort, or run-to-run variability. The paper also evaluates only noise-free synthetic data, so the clinical relevance suggested by the title and conclusion is not yet established. The work does not provide code, trained models, or machine-checked proofs, so the reproducibility of the numerical results cannot be independently assessed.

major comments (4)

[Section 5.2, Table 4] The text in Section 5.2 states that the BiLSTM-LSTM and BiLSTM-BiLSTM algorithms exhibit increase ratios of 109.7% and 113.4%, but Table 4 reports values of 0.117 and 0.134 (i.e., 11.7% and 13.4%). This is an order-of-magnitude inconsistency that makes the reported maximum increases unreliable. Additionally, Table 4 contains negative ratios (BiL-BiLB L4 = -0.047, LSTM-LSTB L3 = -0.025, LSTM-LSTL L4 = -0.004), directly contradicting the conclusion in Section 5.2 that the stacked algorithms exceed the baseline 'with the exception of models L3 and L4' and undermining the blanket claim in Section 6 that 'stacking neural networks effectively enhances the quality of the reconstructed images.' These exceptions must be acknowledged and analyzed, and the conclusions must be qualified accordingly.
[Sections 4.2 and 5.1] The experimental protocol is critically under-specified. Section 4.2 describes the datasets but gives no training hyperparameters: hidden layer sizes, number of LSTM/BiLSTM layers, learning rate, number of epochs, optimizer, regularization, initialization, batch size, or random seeds. Tables 2 and 3 report a single correlation coefficient per model and algorithm, with no error bars, no repeated trials, and no statistical significance testing. Given the small differences between some configurations (e.g., BiL vs. BiLL on CIR1: 0.8818 vs. 0.9023; LSTM vs. LSTL on L4: 0.8643 vs. 0.8611), the reported gains may be indistinguishable from run-to-run variation. Repeated independent runs and reporting of mean, standard deviation, or confidence intervals are necessary before any claim of 'significant enhancement' is supportable.
[Section 3.4] The comparison is not capacity-controlled. Stacking layers as described (LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, BiLSTM-BiLSTM) necessarily increases the number of trainable parameters relative to the single-layer LSTM and BiLSTM baselines. The manuscript never evaluates a single-layer LSTM or BiLSTM with a matched parameter count (e.g., a wider hidden state) or a matched training budget. Therefore the observed CC increases could be due to increased model capacity or additional optimization effort rather than to the architectural operation of stacking. To support the central claim, the authors must include capacity-matched baselines or otherwise control for model size and training cost.
[Section 4.2 and Section 6] The training and evaluation are performed entirely on simulated, noise-free boundary voltages generated from 576-element finite-element models. The manuscript provides no validation with added measurement noise, electrode contact impedance, or anatomical/patient variability, despite the title's 'pulmonary' scope and the conclusion's promise to apply the algorithms to clinical lung monitoring and combine with CT images. If the paper's claim is limited to simulated EIT reconstruction, the wording should be revised accordingly. Otherwise, a noise-robustness study or experimental validation (phantom or clinical data) is required to justify transferability to real-world EIT measurements.

minor comments (5)

[Section 3.2] Equation numbering is inconsistent: after Eq. (1), the paper introduces Eqs. (3.1), (3.2), (3.3) for the candidate state, internal state, and external state, but the later quality metric is numbered Eq. (8). Please renumber all equations sequentially.
[Section 5.2] The text reports 'maximum increases of 27.5% and 25.4%' for LSTM-LSTM and LSTM-BiLSTM over LSTM, but Table 4 also shows negative ratios for L3 and L4. Please add a sentence explicitly identifying the models where the stacked algorithm performs worse, and discuss possible reasons or whether these are within expected variation.
[Section 5.2] Table 4's caption says 'increase ratio,' but the text interprets the values as percentages (e.g., 0.275 as 27.5%). Clarify the units and ensure all statements in the text consistently use either ratios or percentages. The phrase '109.7% and 113.4%' in Section 5.2 appears to be a typographical error for '11.7% and 13.4%'.
[Throughout] Several typos and formatting issues remain: 'BILSTM' and 'BILSTM-BILSTM' in Section 6; 'Overall, the quality of EIT images is observed' is an incomplete sentence in Section 6; and the reference list contains an in-text citation [29] for static/dynamic imaging but no corresponding entry (only 28 references are listed).
[Figures] None of the figures (Fig. 1 through Fig. 11) has a caption in the submitted manuscript text. All figures should be captioned to explain their contents, especially the reconstruction results in Figures 10 and 11, which are central to the evaluation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported comparisons are empirical evaluations against the authors' own baselines, with no input-equivalent derivation or load-bearing self-citation.

full rationale

The paper's central claim is that stacking LSTM and BiLSTM layers improves EIT reconstruction quality, measured by correlation coefficients on simulated phantoms. This is an empirical comparison: the stacked networks are trained to map boundary voltages to conductivity distributions, and their outputs are evaluated against ground-truth conductivities using a standard CC formula (Eq. 8). The CC is not used as a training target or as an architectural input; it is a post-hoc evaluation metric. The improvement reported (e.g., 27.5% for LSTM-LSTM over LSTM) is a direct arithmetic comparison of Table 2 and Table 3 values, not a quantity fitted from or defined in terms of the compared architectures. Although the paper cites the authors' own earlier work (Song et al 2024) as an example of BP neural networks in EIT, that citation is background context and is not load-bearing for the stacked-network claim. The absence of external benchmarks, capacity-matched baselines, or repeated-run statistics weakens the strength of the causal attribution to stacking, but that is a correctness and experimental-design concern, not circularity. No derivation step reduces, by the paper's own equations or by self-citation, to its inputs. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper contributes no new physics or entities. It relies on standard LSTM/BiLSTM math, standard FEA forward modeling, and a strong unvalidated assumption that simulated, noise-free dynamic EIT data represents clinical conditions.

free parameters (2)

Network hyperparameters (layer sizes, hidden units, learning rate, epochs)
Not reported in the manuscript; the central CC comparison depends on them, and without disclosure the baseline vs stacked comparison cannot be assessed.
Number of finite elements (576) = 576
Modeling choice for FEA discretization; results may depend on mesh resolution, but the paper does not test sensitivity.

assumptions (4)

standard math The LSTM and BiLSTM recurrence equations (Eqs. 2-4) correctly model the temporal dynamics of EIT boundary voltage sequences.
Standard definitions adopted without proof; needed for the network implementations.
domain assumption The finite element forward model with 576 elements accurately approximates the true conductivity-to-voltage relationship.
Used to generate all training and test data (Section 4.1); accuracy depends on mesh resolution and electrode model.
domain assumption The simulated circular and lung cross-section models with varied object size, position, and conductivity represent the diversity of real pulmonary EIT measurements.
Training data are generated from these models only (Section 4.2); no clinical or noisy data are used.
domain assumption The correlation coefficient between reconstructed and true conductivity is an adequate measure of image quality.
Used as the sole quality metric (Eq. 8); no other metrics such as resolution or artifact level are reported.

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Cite this review

Pith. "Pith review of Pulmonary electrical impedance tomography based on deep recurrent neural networks." pith.science (2026). https://pith.science/paper/VAX2KYMZ

@misc{pith2026250414521,
  author       = {Pith},
  title        = {Pith review of: Pulmonary electrical impedance tomography based on deep recurrent neural networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VAX2KYMZ}},
  note         = {Machine review of arXiv:2504.14521}
}

read the original abstract

Electrical impedance tomography (EIT) is a non-invasive functional imaging technology. In order to enhance the quality of lung EIT images, novel algorithms, namely LSTM-LSTM, LSTM-BiLSTM, BiLSTM-LSTM, and BiLSTM-BiLSTM, leveraging LSTM or BiLSTM networks, were developed. Simulation results demonstrate that the optimized deep recurrent neural network significantly enhanced the quality of the reconstructed images. Specifically, the correlation coefficients of the LSTM-LSTM and the LSTM-BiLSTM algorithms exhibited maximum increases of 27.5% and 25.4% over the LSTM algorithm, respectively. Moreover, in comparison to the BiLSTM algorithm, the correlation coefficients of the BiLSTM-LSTM and BiLSTM-BiLSTM algorithms increased by 11.7% and 13.4%, respectively. Overall, the quality of EIT images showed notable enhancement. This research offers a valuable approach for enhancing EIT image quality and presents a novel application of LSTM networks in EIT technology.

Figures

Figures reproduced from arXiv: 2504.14521 by the authors.

**Figure 2.** Principle of electrical impedance tomography The method of reconstructing images is categorized into static imaging and dynamic imaging [29]. Static imaging involves reconstructing EIT images of objects by applying boundary voltages at specific moments, resulting in direct image reconstruction using the boundary voltage at time T2. On the other hand, dynamic imaging reconstructs EIT images by utilizing the differenc… view at source ↗

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Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages

[1]

45 054004

Onsager C C et al 2024 Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography Physiol Meas. 45 054004

work page 2024
[2]

Rubin J and Berra L 2022 Electrical impedance tomography in the adult intensive care unit: clinical applications and future directions Curr Opin Crit Care 28 292-301

work page 2022
[3]

An introduction to the technique Adv

Maciejewski D et al 2021 Electrical impedance tomography as a tool for monitoring mechanical ventilation. An introduction to the technique Adv. Med. Sci-poland 66 388-395 Journal XX (XXXX) XXXXXX Author et al 7

work page 2021
[4]

Thomas A, Kim J, Tallman T and Bakis C 2019 Damage detection in self-sensing composite tubes via electrical impedance tomography. Compos. Part B 177 107276

work page 2019
[5]

Kuusela P and Seppänen A 2024 A Coupled Double-Layer Electrical Impedance Tomography-Based Sensing Skin for Pressure and Leak Detection Sensors 24 4134

work page 2024
[6]

Gupta R et al 2021 A Review of Sensing Technologies for Non- Destructive Evaluation of Structural Composite Materials J. Compos. Sci. 5 319

work page 2021
[7]

Bai S, Guo Y, Li W, Wang L and Shi X 2024 Optimization of Electrode Configuration for 3-D Brain EIT IEEE open j. instrum. Meas. 3 4500108

work page 2024
[8]

252 117385

Li W et al 2025 A high-sensitivity four-layer capacitively coupled electrical impedance tomography sensor for brain stroke monitoring Meas. 252 117385

work page 2025

Show all 9 references

[9]

Shi Y, Lou Y, Wang M, Yang K, Gao Z and Fu F 2024 Densely Connected Convolutional Neural Network-Based Invalid Data Compensation for Brain Electrical Impedance Tomography IEEE Trans. Comput. Imaging 10 143-153 [10]Ouypornkochagorn T et al 2024 Frequency-Difference Electrical I...

2024

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

45 054004

Onsager C C et al 2024 Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography Physiol Meas. 45 054004

work page 2024

[2] [2]

Rubin J and Berra L 2022 Electrical impedance tomography in the adult intensive care unit: clinical applications and future directions Curr Opin Crit Care 28 292-301

work page 2022

[3] [3]

An introduction to the technique Adv

Maciejewski D et al 2021 Electrical impedance tomography as a tool for monitoring mechanical ventilation. An introduction to the technique Adv. Med. Sci-poland 66 388-395 Journal XX (XXXX) XXXXXX Author et al 7

work page 2021

[4] [4]

Thomas A, Kim J, Tallman T and Bakis C 2019 Damage detection in self-sensing composite tubes via electrical impedance tomography. Compos. Part B 177 107276

work page 2019

[5] [5]

Kuusela P and Seppänen A 2024 A Coupled Double-Layer Electrical Impedance Tomography-Based Sensing Skin for Pressure and Leak Detection Sensors 24 4134

work page 2024

[6] [6]

Gupta R et al 2021 A Review of Sensing Technologies for Non- Destructive Evaluation of Structural Composite Materials J. Compos. Sci. 5 319

work page 2021

[7] [7]

Bai S, Guo Y, Li W, Wang L and Shi X 2024 Optimization of Electrode Configuration for 3-D Brain EIT IEEE open j. instrum. Meas. 3 4500108

work page 2024

[8] [8]

252 117385

Li W et al 2025 A high-sensitivity four-layer capacitively coupled electrical impedance tomography sensor for brain stroke monitoring Meas. 252 117385

work page 2025