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REVIEW 4 major objections 5 minor 42 references

A High-Granularity Proton CT Enhanced by Track Discrimination

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

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Pith's one-line read A multi-stage track filter lets proton CT reach sub-1% relative stopping power accuracy at 0.16 mGy dose in simulation.

desk verdict Coherent simulation study of a high-granularity pCT with track discrimination; the low-dose numbers are plausible in silico but rest on an unvalidated light-yield model and in-sample tuning. read the letter →

arxiv 2504.20698 v2 pith:SL6YLUDX submitted 2025-04-29 physics.med-ph physics.ins-det

Huang-Chao Shi , Dan-Qi Wang , Mei-Qi Liu , Yi-Dong Yang , Bao-Hua Qi , Yong Liu , Yang-Hui Qin , Chang-Qing Feng
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Yi Liu Hong-Bo Zhu
This is my paper · ORCID
classification physics.med-phphysics.ins-det
keywords protoncomputedtomographyrangetelescopetrackdiscriminationrelativestoppingpowerBortfeldfunctionconvolutionalneuralnetworkultra-low-doseimagingMonteCarlosimulation
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 claims that a proton CT scanner can reach clinically useful accuracy at roughly twenty times lower radiation dose than standard protocols by discarding corrupted proton tracks before image reconstruction. The proposed system replaces the usual precision energy measurement with a high-granularity range telescope whose energy-deposition profiles double as track-quality classifiers, using either a fit to the Bortfeld Bragg curve or a convolutional neural network. In Monte Carlo simulation this multi-filter chain lowers per-track water-equivalent path length (WEPL) uncertainty below 3 mm, which in turn lets a 4×$10^{8}$-proton scan (3.2 mGy) reach about 0.5 mm spatial resolution with sub-1% relative stopping power (RSP) accuracy, and a 2×$10^{7}$-proton scan (0.16 mGy) keep sub-1% RSP accuracy at about 1.1 mm resolution. If these numbers survive prototype tests, the design would enable low-dose pediatric imaging and real-time image guidance for proton therapy.

What carries the argument

The load-bearing mechanism is a three-stage filter chain that runs from raw detector hits to the final RSP map. The first stage cuts on the reconstructed proton scattering angle (θs, around 10°), removing tracks deflected by large-angle elastic scattering; the second stage is the track discriminator in the range telescope, where each track's energy-deposition profile is fitted with the Bortfeld function (an analytical Bragg-curve approximation with parameters fixed by simulation, leaving only the proton range R0 free) or scored by a CNN fed with two orthogonal nPE projection images; the third stage is a pixel-wise 2σ filter on WEPL values grouped in 0.5 mm pixels, applied under the assumption of uniform WEPL within a pixel. The Bortfeld function and the CNN act as classifiers of 'good' versus 'perturbed' tracks, and it is this classification that lets the pixel-wise filter operate with as few as 11 protons per pixel in the low-dose protocol.

What would settle it

Run the proof-of-concept prototype, send 100 or 200 MeV protons through a 100 mm water phantom, apply the same scattering-angle and energy-deposition filters, and compare the measured track-level WEPL spread; if it exceeds about 3 mm, or if the reconstructed RSP error at 2×$10^{7}$ protons exceeds 1%, the central claim fails. A quicker check is to compare the light output along the scintillator bar against the single-bar model, since the CNN's practical 'range tag' labels depend on that model being accurate.

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Extended reading notes

Core claim

The authors' central claim is that a pCT calorimeter should be treated as a range telescope whose measured energy-deposition profile is itself a discriminator of track quality, rather than as a spectrometer that must measure each proton's residual energy precisely. They demonstrate that two discrimination algorithms—a physics-based fit using the Bortfeld function and a CNN classifying raw scintillation-light projections in two orthogonal views—both separate 'unperturbed' tracks from tracks that underwent nuclear interactions or large-angle scattering. With this discrimination, WEPL standard deviation for individual tracks drops below 3 mm, and the subsequent pixel-wise 2σ filter in WEPL-map construction needs far fewer protons per pixel. The consequence is a standard imaging protocol at 3.2 mGy achieving about 0.5 mm resolution with RSP error below 0.5% for non-air inserts, and an ultra-low-dose protocol at 0.16 mGy keeping RSP error below 1% at about 1.1 mm resolution after a count-driven sinogram correction.

Load-bearing premise

The whole performance story rests on the assumption that the simulation, which leaves out electronic noise and light leaking between neighboring detector channels and generates scintillation light from a standalone single-bar calibration, faithfully predicts how the physical range telescope and its discrimination algorithms will behave.

Editorial extensions

If this is right

  • At the standard 4×10^8-proton protocol, simulated RSP accuracy stays below 0.5% for polypropylene, Teflon, and bone-equivalent inserts with about 0.5 mm spatial resolution.
  • At 2×10^7 protons (0.16 mGy), simulated RSP accuracy remains below 1% for tissue-like inserts and spatial resolution degrades only to about 1.1 mm.
  • Because only about 40 of the original 56 protons per pixel survive filtering, the discrimination chain directly cuts the proton statistics needed for pixel-wise filtering, which is what enables the low-dose protocol.
  • A 10 MHz detection rate makes a 2-second scan possible, opening the door to real-time image guidance during radiotherapy.
  • WEPL standard deviation grows only about 2% when the ADC digitization is reduced from 12 bits to 4 bits, so the architecture tolerates cheaper, faster readout electronics.

Reading between the lines

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

  • If the prototype confirms the simulation, the same filter chain could be retrofitted to existing range-telescope pCT designs that currently rely on precision energy measurement, since the discrimination uses the energy-deposition profile itself.
  • The count-driven sinogram interpolation is a basic fix; a statistical reconstruction that jointly models WEPL values and proton counts per pixel could recover additional resolution at low dose.
  • The CNN's range-tag labels depend on the training WEPL window (0–150 mm), so clinical use would likely require retraining per anatomical site or a wider calibration phantom range.
  • The Bortfeld discriminator, with fixed physics parameters and no training data, could serve as a sanity check for the CNN on real detector data, since the two methods are expected to agree on distal-tail events.
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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

4 major / 5 minor

Summary. The paper presents a simulation study of a proton CT system combining a CMOS pixel tracking system with a 100-layer segmented scintillator range telescope. The central idea is to use track discrimination—via Bortfeld-function fitting or a CNN classifier on light-yield images—to reject protons that undergo nuclear interactions or large-angle scattering, thereby reducing WEPL uncertainty and, in turn, the number of protons needed for clinically acceptable RSP imaging. The authors report that in Geant4 simulation both discrimination methods reduce per-track WEPL uncertainty to below 3 mm, that the standard protocol (4×10^8 protons, 3.2 mGy) achieves roughly 0.5 mm spatial resolution with sub-1% RSP accuracy, and that an ultra-low-dose protocol (2×10^7 protons, 0.16 mGy) achieves sub-1% RSP accuracy (excluding air) with roughly 1.1 mm resolution. The paper also includes systematic studies of detector digitization, scintillator thickness and width, and proton energy, and it identifies a prototype and nonideal simulation as future work.

Significance. If the reported simulation results transfer to a physical system, the proposed multi-filter architecture would be a meaningful step toward low-dose proton CT for treatment planning and repeated positioning. The paper is careful in several respects: it explicitly distinguishes the experimentally infeasible process-tagged CNN benchmark from the range-tagged CNN that could be used in practice; it acknowledges that noise and crosstalk are excluded from the present simulation; it provides a clear three-stage filtering workflow; and it includes a range of detector configuration studies that give the results internal coherence. The main significance is therefore conditional: the central claim of ultra-low-dose feasibility rests on the untested fidelity of the light-yield model and on in-sample optimization of thresholds and parameters. The paper is not internally inconsistent, but the gap between simulation and hardware is the key load-bearing issue.

major comments (4)
  1. [Sec. II B and Sec. IV B] The central low-dose claim (Table 2) depends on the range-tagged CNN, which is trained and evaluated on nPE maps generated by sampling from a standalone single-bar light-yield model with 10% coupling smearing, while noise and crosstalk are intentionally excluded from the full-system Geant4 simulation. The range labels are derived from reconstructed ranges in this same idealized simulation, so both the input features and the labels can encode the same simulation-specific artifacts. Because the paper defers nonideal simulation, crosstalk/noise mitigation, and prototype validation to future work, the transfer of the reported sub-3 mm WEPL STD and sub-1% RSP accuracy to a physical detector is not yet supported. I recommend either adding a nonideal simulation with realistic noise, crosstalk, and position-dependent light collection and retraining/testing the CNN, or substantially qualifying the clinical-feasibility statements so that they are explicitly limited to the idealized simulation.
  2. [Sec. III A and Sec. II C] The θs and Edep thresholds are selected by balancing reconstructed WEPL STD and selection rate on the same simulated data that is then used to report the filtered STD values, and the Bortfeld parameters are fitted to MC unperturbed tracks and fixed during the reported evaluation. This makes the 3 mm track-level WEPL STD and the subsequent low-dose protocol in-sample estimates rather than held-out or hardware-transferable estimates. A validation protocol should be reported: for example, thresholds and Bortfeld parameters could be chosen on one simulated dataset and evaluated on an independent phantom or on data with different noise conditions, and the sensitivity of the low-dose conclusion to threshold variation should be quantified.
  3. [Sec. III C and Table 2] The ultra-low-dose protocol at 2×10^7 protons is demonstrated only with the range-tagged CNN, and the paper itself notes that this method exhibits sample-dependent variations and truncation anomalies at 0 mm and 140 mm thickness. The abstract's broad statement of 'sub-1% RSP accuracy' at low dose should be qualified by specifying that it refers to the range-tagged CNN, to the specific phantom materials, and to the exclusion of the air insert, whose relative error is large (Table 2: +64±141%) because of its near-zero RSP. Without this qualification, the headline claim overstates the generality of the low-dose result.
  4. [Abstract and Sec. IV C] The abstract and conclusion suggest that the 10 MHz proton detection rate enables real-time image guidance and a 2-second scan at low dose, but the reconstruction framework is currently limited to single-proton events and the paper states that multi-proton reconstruction is under development. The detector electronics may support 10 MHz rates, but the imaging throughput claim is not yet demonstrated at the reconstruction level. Please separate the demonstrated detector-rate capability from the realized imaging time and soften the real-time claim accordingly.
minor comments (5)
  1. [Sec. III A and Eq. (2)] Eq. (2) with σ_R=3 mm, R=260 mm, and p=1.77 gives σ_E/E ≈ 0.65%, not 0.6% as stated in the text; please update the number.
  2. [Sec. II C 1 and Sec. III A] The text says 'Applying this filter to only 8% of proton events reduces the energy loss STD from 16.7 MeV to 3.3 MeV' but later reports a 97% selection rate for the θs filter; please clarify whether 8% is the rejection fraction for the θs<10° example and how the optimized threshold differs.
  3. [Sec. III B and Table 1] The text states that all materials show spatial resolution of about 0.5 mm except PP, but Table 1 lists PP with range-tagged CNN as 0.27 mm; please clarify whether the quoted resolution is per-method or an average and explain the PP instability more precisely.
  4. [Introduction and Sec. IV A] There are some language errors, e.g., 'Such goals are empirical achieved' and 'varying by less that 2%'; a careful proofread would improve readability.
  5. [Sec. II C 2] The CNN training description lists hyperparameters (10 epochs, batch size 128, Adam at 1e-3) but does not state the final training set size, the class balance between good and bad tracks, or whether early stopping was used; please add these details for reproducibility.

Circularity Check

2 steps flagged · score 6.0 of 10

Low-dose claim rests partly on range-tag labels defined by reconstructed ranges, and thresholds are tuned on the same WEPL-STD metric.

  1. self definitional [Sec. II.C.2 (CNN track discrimination), Sec. III.A (Fig. 5b), Sec. III.C (low-dose RSP imaging, Table 2)]
    "Range tags : Reconstructed ranges for protons passing a same phantom thickness are fitted with a Gaussian distribution. Tracks within µ± 3σ are labeled as 'good'."

    The 'good' class for the range-tagged CNN is defined by fitting a Gaussian to reconstructed residual ranges per phantom thickness and keeping tracks within µ±3σ. Since WEPL is obtained from range through a linear calibration (Fig. 4c), this label is a cut on the same reconstructed WEPL residual whose standard deviation is later reported as the filter's achievement (Fig. 5b, Table 2). A classifier trained to predict that label will preferentially select tracks whose reconstructed range is close to the mean, so a reduction in WEPL STD is partly guaranteed by the target definition rather than by an independent physical measurement.

  2. fitted input called prediction [Sec. III.A (Performance of Track Reconstruction), Fig. 5b]
    "The θs and Edep thresholds are determined by balancing the reconstructed WEPL STD and selection rate - two critical metrics for imaging noise reduction. Optimized thresholds yield selection rates of 97% for the θs filter and 73% for the Edep filter."

    The thresholds are optimized on the same simulation and against the same reconstructed-WEPL-STD metric that is then used to quantify filter performance (Fig. 5b). The reported sub-3 mm WEPL STD values are therefore in-sample optima of the tuning objective, not held-out or hardware-transfer estimates. The paper explicitly defers nonideal simulation, crosstalk/noise mitigation, and prototype testing to future work (Sec. IV.B), so the headline uncertainty numbers are conditional on the idealized simulation in which the thresholds were selected.

full rationale

This is a simulation-design paper rather than a first-principles derivation, and much of the chain is internally coherent: Geant4 provides detector outputs and ground-truth interaction flags, the Bortfeld fit uses physics-based parameters fixed from unperturbed MC tracks, the CNN is trained on a water-phantom sample and evaluated on a different RSP phantom, and the WEPL calibration is a separate linear mapping. The process-tagged CNN and the Bortfeld discriminator have independent physical content. However, the experimentally oriented low-dose claim in Sec. III.C and Table 2 relies on the range-tagged CNN, whose 'good' label is defined directly on the reconstructed range/WEPL distribution; the resulting WEPL-STD reduction is therefore partly a property of the label definition rather than an external prediction. The threshold choices are also tuned on the same simulation against the same metric, making the quoted STD values in-sample optima. The paper itself flags that realistic nPE distributions and prototype testing remain future work (Sec. IV.B), confirming that the headline low-dose performance is not yet validated outside the idealized simulation. These issues are partial rather than total: the process-tagged benchmark and Bortfeld path provide independent support, so the circularity score is moderate, not extreme.

Assumptions & free parameters 9 free parameters · 6 assumptions · 0 invented entities

The reported performance rests on the Geant4 physics lists, the standalone single-bar light model, the Bortfeld parameterization, and a set of thresholds tuned on the same simulation. These are reasonable inputs for a detector design study, but they are not independently validated outside the paper.

free parameters (9)
  • Bortfeld D100 = 14 MeV
    Peak energy deposition in the Edep profile model, fixed from MC study on unperturbed tracks in Sec II C 2.
  • Bortfeld p = 1.77
    Range-energy exponent in Eq (1), fixed from MC; used in WEPL-range conversion via Eq (2).
  • Bortfeld sigma = 1 mm
    Composite Gaussian width in Eq (1), fixed from MC.
  • Bortfeld k = 0.001
    Fluence reduction coefficient in Eq (1), fixed from MC.
  • theta_s threshold = 10 degrees
    Scattering-angle filter threshold optimized to balance WEPL STD and selection rate; yields 97 percent selection in Sec III A.
  • Edep filter threshold (phi cut) = tuned for 73 percent selection rate
    Goodness-of-fit metric phi cut for Bortfeld method and CNN probability threshold, optimized on the simulation; exact numeric cut not stated in Sec III A.
  • Energy leakage weighting threshold = 80 percent of first layer Edep
    Empirically determined; deposits below this receive increased uncertainty weighting in the Bortfeld fit in Sec II C 2.
  • Pixel-wise filter = 2 sigma
    WEPL measurements deviating more than 2 sigma from pixel mean are discarded in Sec II C 4.
  • Low-dose count threshold = 5 protons per pixel
    Pixels below 5 counts are corrected by linear interpolation of adjacent valid pixels in low-dose reconstruction in Sec III C.
assumptions (6)
  • domain assumption Geant4 with QGSP_BIC, G4EmStandardPhysics_option4, and G4OpticalPhysics accurately simulates proton energy loss, multiple Coulomb scattering, nuclear interactions, and scintillation light transport for 200 MeV protons.
    The entire simulated performance depends on this physics-list choice in Sec II B.
  • domain assumption The standalone single-bar optical model (8000 photons/MeV, 40 percent quantum efficiency, 10 percent smearing, position-dependent light attenuation) represents the SiPM response of all 1600 scintillator bars.
    Used to generate nPE maps in the full-system simulation and to calibrate the nPE-to-Edep conversion in Sec II B and II C 2.
  • standard math The Bortfeld function is a valid analytical approximation of the deposited-energy profile for unperturbed proton tracks in the range telescope.
    Eq (1) from Ref [26]; parameters fixed by MC, then used as the discrimination model in Sec II C 2.
  • standard math The MLP matrix formalism with a Gaussian approximation of MCS gives sufficient path estimate accuracy for WEPL mapping.
    From Ref [25]; used for position estimation in track reconstruction in Sec II C 1.
  • domain assumption WEPL calibration using water cuboid thicknesses is unbiased, and the WEPL-range relation is linear over 0-140 mm.
    Phantom 2 geometry and Gaussian fits establish the linear calibration curve in Sec II C 3 and Fig 4.
  • domain assumption The phantom boundaries are known exactly in the simulation, and the MLP inputs use predefined phantom boundaries.
    Sec II C 1 states that inputs to the scattering geometry algorithm include predefined phantom boundaries.

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Cite this review

Pith. "Pith review of A High-Granularity Proton CT Enhanced by Track Discrimination." pith.science (2026). https://pith.science/paper/SL6YLUDX

@misc{pith2026250420698,
  author       = {Pith},
  title        = {Pith review of: A High-Granularity Proton CT Enhanced by Track Discrimination},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SL6YLUDX}},
  note         = {Machine review of arXiv:2504.20698}
}
abstract

Proton Computed Tomography (pCT) provides a promising solution to enhance the accuracy of Relative Stopping Power (RSP) required for proton therapy planning. This research introduces a novel high-granularity pCT architecture that incorporates a silicon pixel tracking system and a calorimetric range telescope, which uniquely integrates range telescope functionality with track discrimination capabilities. The Bortfeld function fitting and Convolutional Neural Network (CNN) classifier algorithms are developed and applied for discrimination. In simulation studies, both approaches demonstrate the capability to reduce uncertainty in Water Equivalent Path Length (WEPL) determination for individual proton tracks to below 3~mm. The standard imaging protocol (3.2~mGy, $4\times10^{8}$ protons) achieves sub-millimeter spatial resolution ($\sim$0.5 mm) with sub-1\% RSP accuracy. With proton count requirements reduced by track discrimination, an ultra-low-dose protocol (0.16~mGy, $2\times10^{7}$~protons) is proposed with achieved sub-1\% RSP accuracy and $\sim$1.1~mm spatial resolution in simulation. This low-dose performance significantly expands clinical applicability, particularly for pediatric imaging or frequent imaging scenarios. Furthermore, the target 10 MHz proton detection rate suggests potential for real-time image guidance during radiotherapy. By circumventing the need for ultra-precise energy measurements, this design minimizes hardware complexity and provides a scalable foundation for future pCT systems.

Figures

Figures reproduced from arXiv: 2504.20698 by the authors.

Figure 1
Figure 1. The top figure illustrates an overview of pCT system. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The top figures show energy loss from ground truth data [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Bortfeld fit examples and performance for 200 MeV pro [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Calibration process and results. Top figures are Gaussian fits [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: (a) compares the reconstructed WEPL distributions under Edep filters after applying the θs filter. The WEPL pro￾file is segmented into three regions: the peak, proximal tail and distal tail. While the θs filter marginally reduces back￾ground in the tails, all Edep filt…
Figure 6
Figure 6. Figure 6: pCT imaging from 4 × 108 protons with 180 projec￾tions and 0.5 × 0.5 × 0.5 mm3 pixel resolution. Insert coordinates: PP (+10 mm, +10 mm), Teflon (+10 mm, -10 mm), air (-10 mm, +10 mm), and bone (-10 mm, -10 mm). (a) A slice of reconstructed RSP image on the plane where…
Figure 7
Figure 7. Figure 7: (a) exhibit linear artifacts caused by noisy pixels in the sinogram where insufficient proton counts compromise the effectiveness of pixel-wise filtering. To address this issue, a noise correction protocol is implemented in which pixels with <5 proton counts are correc…
Figure 8
Figure 8. Figure 8: Overview of proof-of-concept prototype structure. Each [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]

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    A matching algorithm similar to that presented in Ref

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Pith tools

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