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REVIEW 2 major objections 5 minor 37 references

Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner

T0 review · 2 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A deep learning network trained purely on Monte Carlo simulations estimates scatter in long-axial-field-of-view PET more accurately than the standard single scatter simulation, with better robustness to body size and dose, and transfers…

desk verdict Useful LAFOV extension of DLSE, but the phantom superiority claim is undermined by a slice-level train/test split that leaks anatomy; needs a simulation-level split before the numbers can be trusted. read the letter →

arxiv 2501.01341 v4 pith:CKZO24O6 submitted 2025-01-02 physics.med-ph

Baptiste Laurent , Alexandre Bousse , Thibaut Merlin , Axel Rominger , Kuangyu Shi , Dimitris Visvikis This is my paper · ORCID
classification physics.med-ph
keywords positronemissiontomographyscattercorrectionestimationdeeplearningU-Netlong-axialfield-of-viewPETsinogramdomainMonteCarlosimulation
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

Long-axial-field-of-view PET scanners catch more oblique and multiple-scattered photons, and the standard single scatter simulation (SSS) correction struggles with them. This paper argues that a convolutional U-Net trained only on simulated FDG acquisitions can estimate the full scatter sinogram directly from measured emission and attenuation sinograms, capturing multiple scatters and oblique planes that SSS handles poorly. On simulated phantom data the network beat SSS in sinogram and image accuracy, stayed more accurate across body sizes and injected doses, and recovered lesion contrast better. On 14 clinical datasets the network matched or improved on SSS correction, including PSMA scans the network had never seen. If the simulation-based training is faithful to real scanner physics, DLSE offers a fast, accurate scatter correction for LAFOV PET without needing clinical ground truth for training.

What carries the argument

The load-bearing object is the U-Net function $f_\theta(y,b)$ that takes as input the random-free emission sinogram $y$ and the attenuation correction factor sinogram $b$ and outputs an estimate $\hat{s}$ of the expected scatter sinogram $\bar{s}$. It is trained by minimizing the mean squared error $\frac{1}{I}\|f_\theta(y,b)-s\|_2^2$ against Monte Carlo simulated scatter sinograms $s$, on 208,062 slice pairs from 18 simulated FDG acquisitions spanning three body morphologies and six dose levels. The architectural choice of processing each 520 by 50 sinogram slice independently with 2D convolutions, via a five-level U-Net, is what lets oblique-plane scatter contributions from the full-angle acceptance mode enter directly. The key simplifying assumption is that a single Monte Carlo instance $s$ approximates the expected scatter $\bar{s}=\mathbb{E}[s]$.

What would settle it

Acquire a physical phantom with known activity concentrations on the same model of LAFOV scanner, estimate scatter with DLSE and with SSS, and compare reconstructed concentration maps against the known values, for example using a line source in a water cylinder with a beam blocker to measure scatter directly; the central claim collapses if SSS matches or beats DLSE in mean error or contrast recovery on real, non-simulated data.

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Extended reading notes

Core claim

The paper's central claim is that a deep learning scatter estimator (DLSE), a U-Net mapping the measured emission sinogram and attenuation correction factor sinogram to a scatter sinogram, can serve as an accurate scatter correction for a 106-cm axial-field-of-view PET scanner. Because the network processes raw sinogram slices independently, it incorporates multiple-scatter and oblique-plane contributions directly rather than scaling a single-scatter model. Trained on Monte Carlo simulations of three anatomies and six dose levels of FDG distributions, it produced scatter sinograms closer to the Monte Carlo ground truth than SSS across phantom sizes and doses, with sinogram NRMSE ranging from 0.153 to 0.163 for DLSE versus 0.215 to 0.243 for SSS, and reconstructed images with lower error in lungs and brain and closer lesion contrasts. On FDG patient data DLSE gave lesion contrasts equal to or better than SSS, and on PSMA data it agreed closely with SSS despite never being trained on PSMA distributions. The method's prediction time of roughly 381 seconds per whole 3D sinogram, about 33 ms per slice, is presented as practical, with input downsampling projected to cut this below 50 seconds.

Load-bearing premise

The load-bearing premise is that the Monte Carlo model of the scanner is faithful to true scatter and that a single simulated scatter instance is close enough to the expected scatter to serve as training truth; if the simulation is biased, the reported accuracy advantage over SSS may not transfer to real scanners.

Editorial extensions

If this is right

  • In LAFOV PET, DLSE can replace or supplement SSS as the scatter estimate inside iterative reconstruction, reducing image error in low-count regions such as lungs while improving lesion contrast recovery.
  • Scatter correction no longer requires per-patient tail-fitting or clinical training data, because training uses only Monte Carlo simulations; deployment would need only emission and attenuation sinograms.
  • DLSE's insensitivity to body size and injected dose means one trained model can serve a wide range of patient morphologies and acquisition protocols on the same scanner geometry.
  • A model trained only on FDG appears to transfer to a different tracer, PSMA, so retraining may not be needed for new radiopharmaceuticals on the same scanner.
  • With input downsampling, scatter estimation time can drop below 50 seconds, making the method clinically practical alongside reconstruction.

Reading between the lines

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

  • If the simulator's scatter physics are accurate, DLSE's advantage should be largest where SSS's tail-scaling is weakest: large patients, high multiple-scatter fractions, and oblique planes; a direct comparison on physical phantoms with known activity would test this.
  • Because the network is slice-wise, it could be applied to time-of-flight PET by feeding each time-bin sinogram separately, a natural extension the paper mentions implicitly; the same architecture may then transfer to other scanners with matching sinogram geometry.
  • The single-instance MC training target means the accuracy ceiling is set by Monte Carlo variance and bias; averaging several MC instances or adding measured scatter data as targets might improve the already reported margins.
  • The PSMA generalization hints that the network learns the physical relation between attenuation, emission, and scatter rather than tracer-specific uptake; if so, cross-scanner transfer may be achievable with only sinogram-dimension adaptation.
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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

2 major / 5 minor

Summary. The paper proposes and evaluates DLSE, a U-Net-based scatter estimation method for long-axial field-of-view PET, trained on GATE Monte-Carlo simulations of XCAT phantoms to estimate scatter sinograms from emission and ACF sinograms. The method is compared against the clinical standard SSS on simulated phantom data (sinogram and image domain) and on 14 clinical datasets (7 [18F]-FDG, 7 [18F]-PSMA). The authors report that DLSE outperforms SSS on simulated data in terms of NRMSE, robustness to patient size and dose, and lesion contrast recovery, and that clinical results are promising, with improved lesion contrasts on FDG and consistent behavior on PSMA data despite no PSMA training data.

Significance. If the findings are valid, DLSE would offer a fast, accurate scatter estimate for LAFOV PET, directly addressing a known limitation of SSS in systems with wide acceptance angles and multiple scatter contributions. The study is timely and uses a realistic scanner model (Siemens Vision Quadra) with multiple anthropomorphic phantoms and dose levels, and it tests cross-radiopharmaceutical generalization. The clinical evaluation is a useful feasibility check. However, the central simulated-data claim rests on a data-split procedure that may introduce slice-level leakage, which would materially inflate the reported DLSE advantage over SSS. The clinical data cannot independently validate the method because they are compared only against SSS, not against a ground truth.

major comments (2)
  1. [Section 2.2.3 and Section 2.3.1] The train/validation/test split is performed at the sinogram-slice level, not at the simulation level. Section 2.2.3 states that each of the 18 GATE simulations yields 11,559 sinogram slices, giving P = 208,062 realizations, allocated 2/3, 1/6, 1/6 to training, validation, and testing. Because the three morphologies and six dose levels are exactly the ones reported in the evaluation (Figs. 3 and 5), the test slices are drawn from the same 18 simulations as the training slices. Test slices from a given simulation share the same phantom anatomy, activity distribution, and correlated Monte-Carlo noise with training slices from that simulation. The sentence in Section 2.3.1 that 'The phantoms used for evaluation were not included in the training' is therefore misleading: only the particular 1/6 of slices were held out, not the simulations themselves. Under this split, the NRMSE comparisons in Figs. 3 and 5 do not measure generalization to unseen phantoms or dose levels, and the comparison to SSS, which has no training component, is not a fair out-of-sample evaluation. Please redo the evaluation with a simulation-level split (e.g., leave out entire morphology/dose combinations for testing) or with newly generated independent GATE simulations for testing, and report the resulting metrics.
  2. [Section 2.2.2, Eq. (4)] The training target in Eq. (4) is a single Monte-Carlo instance s of the scatter sinogram, with the explicit assumption (Section 2.2.2) that s ≈ s̄. The evaluation in Section 3.1 also uses this same single-instance s as the ground truth for NRMSE. Because s contains Poisson/Monte-Carlo noise, the reported NRMSE values include this noise component, and the network may partially learn to predict the specific noise realization of the training slices. Even if the slice-level split were corrected, the absolute accuracy numbers would still depend on the noise level of the single MC instance. Please quantify the MC noise (for example, by generating several MC instances for at least one phantom/dose combination and computing the variance of s) and discuss how the use of a single noisy target affects the DLSE-vs-SSS comparison and the interpretation of the reported NRMSE.
minor comments (5)
  1. [Section 2.3.1] Please clarify whether the lesion phantom simulations (six spherical lesions, three contrasts) were included in the training data or held out entirely, and if held out, describe how the split was performed.
  2. [Section 3.2 and Abstract] The clinical results are comparisons only against SSS-corrected and uncorrected images; there is no independent ground truth. The abstract's phrase 'improving lesion contrasts' should be tempered to 'improving lesion contrasts relative to SSS-corrected images' to avoid overstating the evidence.
  3. [Eq. (6)] The NRMSE definition uses the range of the estimate (ˆsmax − ˆsmin) in the denominator; standard NRMSE typically uses the range of the ground truth. Please justify this choice or switch to the GT range for easier comparability with other studies.
  4. [General] There are minor typographical issues: 'respecfully' should be 'respectively' in the text after Eq. (6), 'leaded' should be 'led' in Section 2.3.2, and the abstract inconsistently uses '7' and 'seven' for the number of clinical datasets.
  5. [Section 2.2.3] The manuscript states that scattering from outside the axial FOV was included by considering activity along the entire body, but it does not specify how the activity outside the FOV was modeled in the MC simulation or whether the same activity distribution was used for training and evaluation; please clarify.

Circularity Check

2 steps flagged · score 6.0 of 10

The phantom-data DLSE superiority claims rest on a slice-level train/test split from the same 18 GATE simulations, so the network is 'predicting' near-duplicates of its training data; the single-MC-instance training target further makes the reported NRMSE largely a measure of fit to the training target.

  1. fitted input called prediction [Section 2.2.3 (Data Generation) and Section 2.3.1 / 3.1.1 (Simulated Data Evaluation, Figs. 3 and 5)]
    "Each activity, scatter and ACF sinogram consists of 11,559 sinograms slices (with dimensions of 520×50), which represents a total of P = 208,062 realisations of (y, b, s), allocated as follows: 2/3 for training, 1/6 for validation and 1/6 for testing. ... The phantoms used for evaluation were not included in the training."

    The 208,062 'realisations' are sinogram slices, not independent simulations: all 18 GATE simulations (3 morphologies × 6 doses) contribute slices to the training and test sets. Test slices therefore come from the same phantom anatomies, activity distributions, and MC runs as training slices, with highly correlated scatter distributions. The central phantom claims (Figs. 3 and 5 NRMSE) compare DLSE, whose test inputs are near-duplicates of its training distribution, against SSS, whose error is independent of the split. This is not an unbiased out-of-sample comparison; the 'prediction' is largely an in-distribution fit to the same simulations, and the statement that evaluation phantoms were not in training does not establish independence.

  2. fitted input called prediction [Section 2.2.2 (Training, Eq. 4) and Section 2.3.1 (Evaluation, Eq. 6)]
    "In principle, the model should be trained with the expected scatter sinograms ¯s = E[s] obtained by averaging several MC instances of s. However, due to the high computational time, we only used a single instance of s and we assumed s ≈ ¯s. In the following s will be referred to as the ground truth (GT)."

    The network is trained to minimise L(fθ(y,b), s) for a single MC scatter instance, and the evaluation NRMSE is computed against the same type of single-instance MC GT s (Eq. 6). The reported accuracy therefore measures how well the network reproduces the particular noisy MC scatter realization it was fitted to, rather than the expected scatter ¯s required by the forward model in Eq. (2). This inflates apparent DLSE accuracy and makes the phantom comparison with SSS—which estimates expected scatter—partially a comparison to the network's own training target.

full rationale

The paper's method is not definitionally circular: DLSE is a U-Net trained on MC scatter sinograms and evaluated against SSS on simulated data and against SSS on 14 clinical datasets. However, the load-bearing phantom-data claim of superiority is undermined by a slice-level train/test split from the same 18 GATE simulations, so the held-out test slices share anatomy, activity, and MC-run structure with training slices. The single-MC-instance choice for both training target and evaluation ground truth further means the NRMSE partly rewards reproducing the training target's noise. The clinical comparisons provide no independent ground truth—only agreement or disagreement with SSS—which is a validation limitation rather than a circularity, and the self-citation to the authors' previous DLSE paper [22] is used as method provenance and motivation but is not load-bearing because the current evaluation is a new experiment. Overall, the central simulated-data 'prediction' reduces substantially to an in-distribution fit, so a score of 6 is appropriate; the clinical tests and the non-trivial network architecture prevent a higher score.

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

The central claim rests on the fidelity of the GATE Monte Carlo model as a stand-in for real scanner physics, on treating a single MC instance as the true scatter, and on the assumption that a U-Net trained on simulated FDG distributions transfers to real FDG and PSMA patients. The paper does not release code, weights, or data, so the fitted network cannot be independently inspected. No new physical entities are introduced.

free parameters (4)
  • CNN weights theta of the DLSE U-Net = Not released (trained model not available)
    The central mapping f_theta(y,b) approx s is learned from 18 MC simulations. Without the weights, the fitted function cannot be audited or ported.
  • Training hyperparameters (learning rate, epochs, batch size) = 1e-5, 10 epochs, batch size 8
    Chosen by hand; standard values, but they determine the fitted network and the reported performance.
  • Scatter sinogram smoothing width = 2-pixel FWHM Gaussian applied before reconstruction
    Applied to DLSE and MC GT scatter sinograms before injection into the forward model; influences image-domain NRMSE and contrast.
  • Post-reconstruction Gaussian filter = 4 mm FWHM
    Applied to all reconstructed images; affects NRMSE values in Figures 5 and 6.
assumptions (5)
  • domain assumption Emission sinograms are random-free (r_bar = 0).
    Stated in Section 2.1. The forward model and DLSE training ignore randoms; clinical data had randoms subtracted. Residual randoms could bias scatter estimates.
  • domain assumption A single Monte Carlo instance approximates the expected scatter (s_bar is approximately s).
    Section 2.2.2 acknowledges training uses one MC realization instead of E[s]. This makes the training target and the phantom ground truth noisy, potentially biasing accuracy estimates.
  • domain assumption GATE simulations faithfully model the Siemens Vision Quadra.
    Section 2.2.3 cites prior validation [33,34]. The simulated scanner geometry, energy window, and physics are taken as ground truth for training and evaluation.
  • domain assumption A U-Net trained on simulated FDG distributions generalizes to clinical FDG and PSMA scans.
    The paper tests this empirically on 14 patients, but no theoretical guarantee exists; arms-down positioning and different dose statistics are outside the training distribution.
  • domain assumption The evaluation is limited to full-angle acceptance (MRD 322) and non-TOF sinograms.
    Section 4 argues MRD 85 is a subset and TOF is a future extension, but neither is demonstrated. The central claims are scoped to this acquisition mode.

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Cite this review

Pith. "Pith review of Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner." pith.science (2026). https://pith.science/paper/CKZO24O6

@misc{pith2026250101341,
  author       = {Pith},
  title        = {Pith review of: Evaluation of Deep Learning-based Scatter Correction on a Long-axial Field-of-view PET scanner},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CKZO24O6}},
  note         = {Machine review of arXiv:2501.01341}
}
read the original abstract

Objective: Long-axial field-of-view (LAFOV) positron emission tomography (PET) systems allow higher sensitivity, with an increased number of detected lines of response induced by a larger angle of acceptance. However, this extended angle increases the number of multiple scatters and the scatter contribution within oblique planes. As scattering affects both quality and quantification of the reconstructed image, it is crucial to correct this effect with more accurate methods than the state-of-the-art single scatter simulation (SSS) that can reach its limits with such an extended field-of-view (FOV). In this work, which is an extension of our previous assessment of deep learning-based scatter estimation (DLSE) carried out on a conventional PET system, we aim to evaluate the DLSE method performance on LAFOV total-body PET. Approach: The proposed DLSE method based on a convolutional neural network (CNN) U-Net architecture uses emission and attenuation sinograms to estimate scatter sinogram. The network was trained from Monte-Carlo (MC) simulations of XCAT phantoms [18F]-FDG PET acquisitions using a Siemens Biograph Vision Quadra scanner model, with multiple morphologies and dose distributions. We firstly evaluated the method performance on simulated data in both sinogram and image domain by comparing it to the MC ground truth and SSS scatter sinograms. We then tested the method on seven [18F]-FDG and seven [18F]-PSMA clinical datasets, and compare it to SSS estimations. Results: DLSE showed superior accuracy on phantom data, greater robustness to patient size and dose variations compared to SSS, and better lesion contrast recovery. It also yielded promising clinical results, improving lesion contrasts in [18F]-FDG datasets and performing consistently with [18F]-PSMA datasets despite no training with [18F]-PSMA.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.