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REVIEW 3 major objections 5 minor 44 references

Prompt gamma timing can recover proton stopping power from beam tests alone

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2026-08-03 19:59 UTC pith:2JNLH4TX

load-bearing objection First experimental demonstration of SER-PGT-based stopping power and range retrieval from real proton beam data; the central feasibility claim holds, but the missing sensitivity analysis on simulation-based calibration leaves the reported accuracy partly ungrounded. the 3 major comments →

arxiv 2511.21344 v1 pith:2JNLH4TX submitted 2025-11-26 physics.med-ph

Stopping power monitoring during proton therapy by means of prompt gamma timing: first experimental results with a homogeneous phantom

Julius Werner , Francesco Pennazio , Piergiorgio Cerello , Elisa Fiorina , Simona Giordanengo , Felix Mas Milian , Alessio Mereghetti , Franco Mostardi
show 6 more authors
Marco Pullia Sahar Ranjbar Roberto Sacchi Anna Vignati Magdalena Rafecas Veronica Ferrero
This is my paper
classification physics.med-ph
keywords proton therapyprompt gamma timingstopping powerrange verificationspatiotemporal emission reconstructionmaximum likelihood expectation maximizationhomogeneous phantomin-vivo monitoring
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper reports the first experimental demonstration that a proton therapy beam's stopping power and range can be reconstructed from prompt-gamma timing measurements, using a homogeneous PMMA phantom and a sub-clinical 226.9 MeV beam. The authors built a multi-detector setup—a fast silicon diode for proton timing and two lanthanum-bromide photon detectors moved through 14 positions—and applied spatiotemporal emission reconstruction to recover the 2D gamma-emission distribution. They then fit this distribution with a proton-motion model to derive the stopping power profile. Against standard reference values, the estimated stopping power had an average error of 8% (plus or minus 3%), and at 100 MeV it deviated by only 2% from water. They also showed the method can spot a 4 cm range shift with a 3 mm standard deviation. If these results hold, the technique could become a non-invasive, in-vivo check of both range and stopping power during proton therapy, reducing the uncertainties that currently force treatment plans to add safety margins.

Core claim

The central claim is that SER-PGT—spatiotemporal emission reconstruction from prompt-gamma timing—can extract both the beam range and the stopping power of the target material directly from measured gamma time-of-flight distributions, without needing prior tissue-composition data. Using a 226.9 MeV proton beam on a homogeneous PMMA phantom, the authors reconstructed the gamma emission in space and time, then fit a simple analytical motion model of the proton slowdown to those (t,z) points. From the model parameters they obtained a stopping power profile with an average error of 8% ± 3% relative to the PSTAR reference, and a water-relative stopping power ratio at 100 MeV of 2% ± 2%. In a sepa

What carries the argument

The key object is the spatiotemporal emission map produced by SER-PGT: a 2D histogram of prompt-gamma emission points in depth versus time, reconstructed from time-of-flight measurements of gamma photons detected at several angles. The reconstruction uses a maximum-likelihood expectation-maximization algorithm with a system matrix precomputed by Monte Carlo simulation (treating ideal identical detectors) and a background term for random coincidences. From the reconstructed (t,z) pairs, the authors fit the analytical proton-motion model S = -dE/dz = (1/p)√α (R0 - z)^(1/p - 1), where α and p are free parameters and R0 = α E0^p, to extract the stopping power. A risk-sensitive optimizer tunes th

Load-bearing premise

The entire stopping-power estimate relies on the Monte-Carlo-generated system matrix and time-alignment from simulated rising edges faithfully reproducing the real detectors and photon transport; if that simulated response does not match the physical setup, the reconstructed emission distribution and the final stopping power inherit an unquantified systematic error that the reported 8% and 2% figures do not include.

What would settle it

Measure the stopping power of a homogeneous water or PMMA phantom using both SER-PGT and a direct method such as a Bragg-peak ionization chamber across a range of beam energies (e.g., 100, 150, 200, 226 MeV). If the SER-PGT estimates systematically deviate from the direct stopping-power measurements by more than the reported 8% MRE plus the statistical spread, the discrepancy would indicate that the motion model or the simulation-based system matrix is introducing bias; if the deviations are within errors, it would confirm the claim.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the reconstruction is unbiased, SER-PGT offers a way to measure stopping power in vivo during a proton treatment fraction, which is not possible with current imaging methods.
  • A range verification that catches multi-centimeter shifts with sub-centimeter precision could help reduce the large safety margins currently applied in proton therapy planning.
  • The same 2D distribution could provide information about the proton energy loss along the path, potentially supporting LET-guided treatment plan optimization.
  • Because the instrumentation is simpler than in-beam PET or gamma cameras, the technique may be easier to integrate into existing clinical treatment rooms.
  • The success with only two photon detectors moved through seven configurations suggests that a modest number of detectors may be sufficient for routine monitoring.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The reported errors are averaged over depth and subsets; a deeper look at the spatial distribution of the error (e.g., near the Bragg peak or at the phantom entrance) might reveal where the motion model or reconstruction is weakest, and could indicate whether a more refined model would push the accuracy below the 8% level.
  • The dependence on a Monte Carlo system matrix means that transferring the technique to another beam line or detector geometry would likely require a re-simulation; a parameterized or analytically computed system matrix could make the method more portable and less sensitive to simulation mismatches.
  • A natural next experiment is to test a range of beam energies (say 100–250 MeV) and heterogeneous phantoms to see whether the motion-model assumption of homogeneity is the main bottleneck; if it fails in heterogeneities, the technique may still be useful for the homogeneous tissue regions of the body, such as the brain or prostate.
  • The method could be cross-validated against direct measurements of stopping power, such as a Bragg-peak scan with a water column, to independently confirm the 8% accuracy claim and to quantify the additional bias from the simulation-based calibration.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper reports the first experimental application of SER-PGT (Spatiotemporal Emission Reconstruction from Prompt-Gamma Timing) to proton therapy monitoring. A 226.9 MeV proton beam impinged on a homogeneous PMMA phantom and a PMMA phantom with a 4-cm air gap. Prompt gamma rays were detected with two LaBr3:Ce detectors moved to 14 positions, and protons were timed with an LGAD detector. The authors reconstructed the spatiotemporal emission distribution using an MLEM algorithm with a FLUKA-based system matrix, then fitted a two-parameter power-law motion model to the reconstructed (t,z) trajectory to derive the stopping power. They report an 8%±3% mean relative error (MRE) in stopping power compared to NIST PSTAR, a 2%±2% deviation in SPR at 100 MeV, and a range shift of 3.8±0.3 cm for the 4-cm air gap. The central claim is the experimental feasibility of recovering both range and stopping power from prompt-gamma timing alone.

Significance. If the result holds, this is a significant step toward in-vivo stopping-power verification in proton therapy, potentially enabling on-the-fly treatment adaptation without additional imaging. The paper combines a novel reconstruction methodology with a careful experimental campaign, including leave-one-out cross-validation, and is the first to demonstrate SER-PGT on real data. The reported statistical precision is encouraging. However, the claimed accuracy is only statistical; the systematic uncertainties arising from the simulation-based calibration and system matrix are not quantified, and the dependence on a specific power-law model is not validated. These gaps must be addressed before the magnitude of the advance can be fully assessed.

major comments (3)
  1. [Sec. 2.1.4 / 2.2.3 / 3.3] The absolute time-delay calibration aligns experimental and simulated PGT rising edges using a FLUKA simulation, and the system matrix is computed with FLUKA assuming ideal identical detectors. The reported uncertainties (±3% MRE, ±2% SPR) are solely the dispersion across data subsets; no sensitivity analysis is provided for the simulation inputs (e.g., detector time resolution, energy threshold, photon transport, or the stopping-power database embedded in FLUKA). Because the calibration directly ties the measurement to the simulation, any systematic mismatch—particularly in the stopping-power model used by FLUKA—will propagate into the reconstructed (t,z) and hence the derived stopping power. Without a perturbation study or an independent calibration cross-check, the 8% MRE cannot be interpreted as the true accuracy of the method.
  2. [Sec. 2.2.5] The stopping power is extracted by fitting the reconstructed (t,z) trajectory with a two-parameter power-law model (R0 = α E0^p, S ∝ (R0−z)^(1/p−1)). The paper does not report how well this model alone reproduces NIST PSTAR stopping power for PMMA. If the power-law fit to NIST already has an MRE of order 8%, then the reported 8% MRE is largely a model error rather than a demonstration of the measurement's capability. The authors should fit the same model to NIST PSTAR data (or to a known stopping-power profile from FLUKA) and show the residual, or adopt a more flexible model and demonstrate that the reconstruction is not limited by the parametric form.
  3. [Sec. 3.3 / Fig. 6] The MRE is averaged over all depth bins, which can be dominated by regions of low stopping power (e.g., near the distal falloff) where relative errors are naturally large. The clinically relevant accuracy is the error in the plateau and Bragg-peak region. The paper should report a depth-resolved error profile or at least the MRE over a clinically meaningful depth window (e.g., from the entrance to 80% of the range). This would clarify whether the 8% average reflects a uniform bias or a localized failure.
minor comments (5)
  1. [Sec. 3.1] The sentence 'the reference detector collected 4.0·10^5 events for each position ... while it detected 4.7·10^7 events during the irradiation of the phantom with the air gap' appears to contain an order-of-magnitude typo; 4.7·10^7 is inconsistent with the stated splitting into ten subsets of comparable size.
  2. [Sec. 2.2.5] The notation is inconsistent: the text defines R0 = α E0^p, but the stopping-power formula uses 1/(p√α). Please clarify the definition of α or correct the formula.
  3. [Fig. 6(b)] The histograms lack axis labels and a legend; specify what is plotted (e.g., counts vs. SPR and MRE) and, for MRE, the units (percent).
  4. [Sec. 4] The claim that SER-PGT is 'the only technique published so far that potentially allows on-the-fly measurement of the stopping power' is stronger than necessary and could be rephrased; other approaches (e.g., dual-energy CT, proton CT) provide stopping-power estimates, even if not on-the-fly.
  5. [Sec. 2.2.3] The system matrix is based on 'ideal identical detectors' with a single efficiency correction. It would be helpful to state explicitly which non-idealities (e.g., time-walk, energy response, detector position uncertainty) are neglected and how they might affect the reconstruction.

Circularity Check

0 steps flagged

No significant circularity: stopping-power and range estimates are obtained by model-based inversion of measured PGT data with external NIST PSTAR comparison.

full rationale

The derivation chain is not circular. The reported stopping-power profile is obtained by reconstructing a spatiotemporal emission distribution from measured PGT histograms using an MLEM algorithm, then fitting the extracted (t,z) pairs to a two-parameter analytical motion model and computing S = -dE/dz from that model. The system matrix from FLUKA is an imaging-physics calibration (detection probabilities for photons emitted at given positions/times), not a substitution of the stopping-power answer; it does not encode the spatial proton-velocity profile. The time-delay alignment against simulated PGT rising edges fixes only constant time offsets, and it does not force the shape of the reconstructed emission distribution. The comparison against NIST PSTAR is external to the fit: the MRE is minimized on training subsets, and the reported 8% ± 3% MRE and 2% ± 2% SPR are evaluated on held-out subsets via leave-one-out, so they are not in-sample fits renamed as predictions. The self-citations to Werner et al. [25], Ferrero et al. [26], and Pennazio et al. [27] provide the reconstruction and motion-model framework, but the present experimental data constitute independent evidence; no uniqueness theorem or unverified imported ansatz is used to foreclose alternatives. The stated limitations (simulation-based system matrix, homogeneous-phantom assumption, background-model characterization, and the need for further MC studies) concern unquantified systematic uncertainty and model validity, which are correctness risks rather than circularity under the criteria used here.

Axiom & Free-Parameter Ledger

6 free parameters · 4 axioms · 0 invented entities

The central result depends on two fitted motion-model parameters (α, p), two reconstruction hyperparameters (k, T), a per-detector efficiency correction, and an estimated background. The physical assumptions are the homogeneity of the target, the validity of the analytical motion model, the accuracy of the MC-computed system matrix, and the correspondence between prompt-gamma emission and proton motion. No new physical entities are introduced.

free parameters (6)
  • α (motion model parameter) = not reported
    Fitted when the reconstructed (t,z) profile is matched to the analytical motion model (Sec. 2.2.5); sets the scale of the stopping-power curve.
  • p (motion model exponent) = not reported
    Second parameter of the motion model; determines the energy-loss shape; fitted to data (Sec. 2.2.5).
  • k (MLEM iterations) = 39–46 (chosen by risk-sensitive optimization)
    Hyperparameter of the reconstruction; optimized on the training set (leave-one-out) to minimize stopping-power MRE (Sec. 3.3).
  • T (relative threshold) = 0.325–0.500
    Threshold applied to the reconstructed emission distribution; optimized together with k (Sec. 3.3).
  • c_d (per-detector efficiency correction) = ~10% per-detector difference
    Correction factor derived from symmetric-detector measurements to reconcile count-rate differences (Sec. 2.2.3).
  • b_dn (random-coincidence background estimate) = estimated via SNIP and scaled by 1/10
    Used in the MLEM update; derived from the homogeneous-phantom PGT distribution and scaled to subset size (Sec. 2.2.2, 3.1).
axioms (4)
  • domain assumption The material is homogeneous and the analytical motion model is valid for this case.
    Stopping-power retrieval explicitly assumes a homogeneous target and the analytical proton-motion model (Sec. 2.2.5). The paper notes extension to heterogeneous targets is future work.
  • domain assumption The FLUKA system matrix accurately describes the detection probabilities of an ideal detector.
    The system matrix h*_dnjp is computed with Monte Carlo simulations for identical ideal detectors; efficiency corrections c_d are applied afterward (Sec. 2.2.3). Any mismatch between the simulation and real detector response introduces bias in the reconstruction.
  • domain assumption Prompt-gamma emission is a valid proxy for primary-proton motion.
    The (t,z) profile from the reconstructed emission is treated as the primary particle trajectory z(t) (Sec. 2.2.5). This is the physical basis of the technique.
  • domain assumption Time-delay calibration via alignment of simulated and measured PGT rising edges is unbiased.
    Relative delays between photon detectors and LGAD strips are obtained by aligning rising edges of FLUKA-simulated and experimental PGT distributions (Sec. 2.1.4). This assumes the simulation captures the earliest-emission timing correctly.

pith-pipeline@v1.3.0-alltime-deepseek · 13741 in / 15483 out tokens · 136647 ms · 2026-08-03T19:59:20.940837+00:00 · methodology

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read the original abstract

Proton therapy's full potential is limited by uncertainties that prevent optimal dose distribution. Monitoring techniques can reduce these uncertainties and enable adaptive treatment planning. Spatiotemporal Emission Reconstruction from Prompt-Gamma Timing (SER-PGT) is a promising method that provides insights into both particle range and stopping power, whose calculation would normally require knowledge about patient tissue properties that cannot be directly measured. We present the first experimental results using a 226.9 MeV synchrotron-proton beam impinging on a homogeneous phantom at a sub-clinical intensity (2 - 4 x 10^7 pps). SER-PGT uses data from a multi-detector setup: a thin and segmented Low Gain Avalanche Diode for proton detection and Lanthanum Bromide-based crystals for photon detection. The estimated stopping power profile showed an 8% +- 3% average error compared to NIST PSTAR values, and 2% +- 2% deviation relative to water at 100 MeV. Range assessment in a phantom with a 4 cm air-gap successfully identified the range shift with a 3 mm standard deviation. These results demonstrate the feasibility of using SER-PGT to recover both range and stopping power information through particle kinematics and PGT measurements.

Figures

Figures reproduced from arXiv: 2511.21344 by Alessio Mereghetti, Anna Vignati, Elisa Fiorina, Felix Mas Milian, Francesco Pennazio, Franco Mostardi, Julius Werner, Magdalena Rafecas, Marco Pullia, Piergiorgio Cerello, Roberto Sacchi, Sahar Ranjbar, Simona Giordanengo, Veronica Ferrero.

Figure 1
Figure 1. Figure 1: (a): Schematic of the phantom without (yellow) or with (blue) the airgap, the reference detector [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Waveform snapshots after baseline subtraction and signal inversion, showing the photon signal in [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example of PGT spectra of the first subset with and without the air cavity for detector positions [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reconstructed spatiotemporal distributions using the first subset of PGT-data. Left: homogeneous [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Range differences for homogeneous subsets (blue) and 4-cm air gap subsets (red), both centred at [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a): Stopping power versus depth. The reference values are given in red, while the blue diamonds [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

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