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REVIEW 3 major objections 5 minor 49 references

Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A two-encoder CNN called Y-Net reconstructs photoacoustic images better than single-input U-Net.

desk verdict Y-Net is a plausible dual-encoder architecture with code and a clean synthetic validation, but the in-vivo comparison is compromised by an undisclosed change to Encoder II's input, so the current paper needs major revision before its claims can be trusted. read the letter →

arxiv 1908.00975 v1 pith:TBWEKHCK submitted 2019-08-02 eess.IV cs.CVcs.LG

Hengrong Lan , Daohuai Jiang , Changchun Yang , Fei Gao This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords photoacousticcomputedtomographylimited-viewreconstructionconvolutionalneuralnetworkdual-encoderarchitecturedelay-and-sumbeamforminghybriddeeplearninginvivoimagingsimulation-basedtraining
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

The paper tries to establish that a convolutional network with two encoders—one reading raw photoacoustic signals, one reading a delay-and-sum beamformed image—reconstructs photoacoustic images better than networks using either input alone. The two inputs are meant to complement each other: the beamformed image supplies overall texture while the raw data restores fine details lost to artifacts. If true, the architecture offers a fast, non-iterative route to higher-quality photoacoustic images in real-time settings, and the same dual-input idea could transfer to other imaging modalities. The supporting experiments compare against U-Net and two ablated variants on simulated vessel data and on chicken-breast and human-palm measurements.

What carries the argument

The load-bearing architecture is the Y-shaped network: Encoder I compresses raw time-series photoacoustic signals into a small feature map, Encoder II compresses a delay-and-sum beamformed image, and a single decoder concatenates features from both arms at every level through skip connections, outputting a 128-by-128 initial-pressure image. The two arms are trained with a composite loss: a reconstruction mean-squared-error on the output plus an auxiliary loss that asks Encoder II alone to reproduce the ground truth, so the beamformed branch is explicitly responsible for texture while the raw-signal branch fills in missing detail.

What would settle it

Run the same training protocol on a matched set of real ex-vivo measurements with high-quality references, feeding both Y-Net and U-Net the identical delay-and-sum image with no ad hoc input replacement; if Y-Net's margin disappears or reverses, the reported advantage is an artifact of simulation transfer rather than the dual-input architecture.

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Extended reading notes

Core claim

The central claim is that Y-Net, a U-Net-like encoder-decoder CNN with a second encoder branch for raw photoacoustic signals, outperforms single-input deep-learning reconstruction (U-Net) and conventional algorithms for photoacoustic computed tomography. On the synthetic test set it reports the best scores of all compared methods (SSIM 0.9119, PSNR 25.5434 dB, SNR 9.9291), and in chicken-breast and human-palm experiments it states that Y-Net retains clearer structure with fewer false vessel associations. All networks are trained on 4700 simulated phantoms built from segmented retinal-vessel patterns under a fixed linear-array geometry and scored on 400 held-out phantoms; the paper attributes the gain to the hybrid input, with the beamformed branch providing texture and the signal branch adding details the beamforming step discarded.

Load-bearing premise

The method assumes that vessel-like phantoms simulated under one fixed acoustic setup capture the structure of real tissue signals closely enough that a network trained only on those simulations improves real reconstructions; when this fails, the in-vivo test substitutes a better beamformed input, which masks the mismatch.

Editorial extensions

If this is right

  • Any fast conventional reconstruction, not only delay-and-sum, can be plugged into Encoder II, so improvements in beamforming should directly improve Y-Net's output.
  • At roughly 0.03 seconds per image, the network is fast enough for real-time photoacoustic computed tomography workflows, unlike iterative model-based reconstruction.
  • The synthetic test results place Y-Net above both of its ablated variants and above U-Net, which suggests both encoder branches contribute to the improvement.
  • If the hybrid input strategy is the reason for the gain, other imaging modalities that have both raw detector data and a fast approximate reconstruction can borrow the same dual-encoder design.

Reading between the lines

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

  • The in-vivo protocol's switch to a 'better texture reconstructed result' for Encoder II means the real-tissue comparison tests a domain-adapted variant, not the exact simulation-trained architecture; quantifying that adaptation would be needed to compare fairly with U-Net.
  • A direct extension would be to train with randomized acoustic parameters and source geometries; if Y-Net's edge over U-Net widens with diversity, the mechanism is the dual-input fusion, while if it shrinks, the edge may come from better-matched priors rather than the architecture.
  • The same two-input pattern—raw sensor time series plus fast back-projection—could transfer to other limited-view tomographies, such as ultrasound or microwave imaging, where analogous artifacts and detail loss occur.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes Y-Net, a convolutional neural network with two encoders (one taking raw photoacoustic signals, the other taking a delay-and-sum beamformed image) and a shared decoder, intended as a non-iterative 'hybrid' reconstruction method for photoacoustic tomography. The network is trained on k-Wave simulations of segmented retinal vessels from the DRIVE dataset and evaluated on a synthetic test set, an in-vitro chicken-breast phantom, and an in-vivo human-palm experiment. The authors report that Y-Net achieves the highest SSIM, PSNR, and SNR on the synthetic test set among the compared learned and conventional methods, and qualitatively state superior performance in the experimental cases. The paper also provides an ablation study (removing either encoder path) and a point-target resolution comparison.

Significance. If the claims hold, the dual-encoder architecture is a reasonable and practical contribution to learned photoacoustic reconstruction, and the paper explicitly targets a real gap between direct raw-data-to-image methods and image-post-processing methods. Strengths include the use of a public vessel dataset for simulation, the inclusion of ablation studies, a point-target resolution test, and a public source-code link. The central quantitative claim on synthetic data is supported by the reported metrics, though the absolute advantage over U-Net is small. The in-vivo comparison, however, is undermined by an undocumented change to the network input in Section IV.D, which prevents attribution of the observed improvement to the architecture and makes the in-vivo claim non-reproducible as written.

major comments (3)
  1. [Section IV.D and Fig. 8] The in-vivo experiment is not reproducible because the input to Encoder II is changed without specification: the text states 'we alter the input of Encoder II, which is revised as a better texture reconstructed result instead of DAS,' but no algorithm, parameters, or example of this input are given, and no ablation is shown with this input. Since the network was trained on DAS inputs (Section IV.A), substituting a different input at inference creates an unquantified distribution shift. The claimed advantage of Y-Net over U-Net in Fig. 8 cannot be attributed to the architecture unless the alternative input is fully described and either the network is retrained with it or a controlled ablation is provided. This issue is load-bearing for the abstract's claim of 'validated with experiments ... in vivo, which still performs better than other existing methods.'
  2. [Table I and Section V.A] The quantitative comparison on the synthetic test set reports only point estimates (SSIM 0.9119 vs 0.9002; PSNR 25.5434 vs 25.0032 dB; SNR 9.9291 vs 9.3233) without error bars, standard deviations, or significance tests across the 400 test samples. Given the small margins, the reader cannot assess whether Y-Net's advantage over U-Net is statistically meaningful or within run-to-run variability. The paper should report per-sample distributions, confidence intervals, or paired statistical tests.
  3. [Section IV.A and IV.B] The evaluation metrics are computed on a test set generated by the same k-Wave simulation pipeline used for training, so the synthetic results reflect interpolation within the training distribution rather than generalization to unseen acquisition conditions. The in-vitro and in-vivo results are qualitative and, as noted above, the in-vivo comparison is compromised. The paper should explicitly acknowledge this limitation and, if possible, include a robustness test (e.g., different acoustic speeds, noise levels, or transducer geometries) to support the claimed generalization.
minor comments (5)
  1. [Section III.D] The hyper-parameter λ in the total loss (Eq. 19) is set to 0.5 without any sensitivity analysis; a brief statement on how the results vary with λ would help reproducibility.
  2. [Equations (2) and (3)] Several equations appear to have rendering or typographical issues, for example Eq. (2) is missing part of the integrand and Eq. (3) has a misplaced comma; these should be carefully revised.
  3. [Fig. 5 and Fig. 8] The qualitative figures would benefit from consistent color bars, scale bars, and clear labels for the ROIs, as the reader cannot currently judge the absolute intensity scales.
  4. [Section V.A, point-target experiment] The point-target resolution comparison is presented qualitatively with a single profile plot; reporting a quantitative metric such as full-width at half-maximum for the reconstructed points would strengthen this comparison.
  5. [References] Reference [31] is cited as 'unpublished' but appears to be an EMBC 2019 paper; the final citation should be updated to its published version if available.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical deep-learning reconstruction study whose reported results are not forced by construction or by self-citation.

full rationale

Y-Net is presented as a supervised CNN mapping from (raw PA signals, DAS beamformed image) to a reconstructed initial pressure, trained on k-Wave simulations with an MSE loss plus an auxiliary supervision on Encoder II. There is no claimed first-principles derivation chain whose conclusion reduces to its inputs: the forward model (Eqs. 1-4) is standard background, and the network parameterization (Eqs. 9-16) is a direct architecture description rather than a derivation of results from assumptions. The only self-citation that could be noted is reference [31], which merely announces preliminary EMBC results and is not load-bearing for the present claims. The quantitative test-set metrics are computed on 400 simulations generated by the same k-Wave pipeline used for training; this is a distributional-overlap limitation of the evaluation, not a circular reduction, because the network output is not by construction equal to any fitted parameter or input. The in-vivo protocol replaces Encoder II's DAS input with an unspecified 'better texture reconstructed result' and applies the same substitution to U-Net; this is a reproducibility and comparison-fairness flaw, but it does not make the claimed Y-Net advantage equivalent to the paper's inputs by definition. No step satisfies the evidentiary standard of quoting an equation or cited theorem that forces the paper's conclusion from its own assumptions, so the appropriate finding is no circularity.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the assumptions that the simulation faithfully represents experimental conditions and that the network generalizes from synthetic vessel data to real tissue; these are unproven and only partially validated qualitatively. The hyperparameters are chosen by hand, and the unspecified 'better texture' input for in-vivo experiments is a post-hoc choice. No invented physical entities are introduced.

free parameters (5)
  • lambda (L_total loss weight) = 0.5
    Hand-chosen hyperparameter in Eq. (19); no ablation study is provided.
  • initial learning rate = 0.005
    Adam optimizer setting in Section III.D; chosen by hand.
  • batch size = 64
    Training setting in Section III.D; chosen by hand.
  • number of epochs = 1000
    Training setting in Section III.D; early stopping is not described.
  • Encoder II in-vivo input = unspecified 'better texture reconstructed result'
    Section IV.D changes the beamformed input for in-vivo experiments without identifying the algorithm, making the real-data method a hand-tuned configuration.
assumptions (4)
  • domain assumption k-Wave simulation accurately models the photoacoustic forward problem for the experimental linear-array geometry and tissue types used.
    Section IV.A uses k-Wave to generate all training data; if the simulator's acoustic and optical assumptions do not match the chicken breast and human palm experiments, the learned mapping is invalid.
  • domain assumption Vessel patterns segmented from retinal fundus images (DRIVE) are a representative prior for blood vessel structures in a chicken breast phantom and a human palm.
    Section IV.A creates training targets from DRIVE vessels; the in-vitro phantom contains pencil leads, not vessels, and the in-vivo palm vessels may differ in scale and orientation.
  • domain assumption A convolutional network with two encoders and a decoder, trained with MSE loss and the stated hyperparameters, can learn the inverse mapping from (raw signals, DAS image) to ground-truth initial pressure.
    Section III.D assumes this representational capacity and optimizability; no theorem or formal guarantee is given.
  • standard math The photoacoustic wave equation and its Green's function solution (Eqs. 1-3) are accepted physical models.
    Equations are cited to standard references [4,32,33]; the paper does not re-derive them.

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Cite this review

Pith. "Pith review of Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo." pith.science (2026). https://pith.science/paper/TBWEKHCK

@misc{pith2026190800975,
  author       = {Pith},
  title        = {Pith review of: Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TBWEKHCK}},
  note         = {Machine review of arXiv:1908.00975}
}
read the original abstract

Photoacoustic imaging (PAI) is an emerging non-invasive imaging modality combining the advantages of deep ultrasound penetration and high optical contrast. Image reconstruction is an essential topic in PAI, which is unfortunately an ill-posed problem due to the complex and unknown optical/acoustic parameters in tissue. Conventional algorithms used in PAI (e.g., delay-and-sum) provide a fast solution while many artifacts remain, especially for linear array probe with limited-view issue. Convolutional neural network (CNN) has shown state-of-the-art results in computer vision, and more and more work based on CNN has been studied in medical image processing recently. In this paper, we present a non-iterative scheme filling the gap between existing direct-processing and post-processing methods, and propose a new framework Y-Net: a CNN architecture to reconstruct the PA image by optimizing both raw data and beamformed images once. The network connected two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. The results of the test set showed good performance compared with conventional reconstruction algorithms and other deep learning methods. Our method is also validated with experiments both in-vitro and in vivo, which still performs better than other existing methods. The proposed Y-Net architecture also has high potential in medical image reconstruction for other imaging modalities beyond PAI.

Figures

Figures reproduced from arXiv: 1908.00975 by the authors.

Figure 1
Figure 1. Comparison of information loss in the traditional DAS reconstruction method. (a) The ground-truth; (b) The delay-and-sum reconstructed result of (a); (c) The difference between (a) and (b). Model-based approach can reconstruct the imperfect data well compared with above non-iterative algorithms, which devotes to rebuild PA image f from signal b by optimizing the objective function: 2 2 1 arg min ( ), f 2 Af b f −+ λ… view at source ↗

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.