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REVIEW 3 major objections 5 minor 30 references

Exploring Offline Pileup Correction to Improve the Accuracy of Microdosimetric Characterization in Clinical Ion Beams

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Offline stochastic resampling corrects pulse pileup in microdosimetric spectra at clinical ion-beam rates, reducing mean lineal energy differences to below one percent.

desk verdict A solid feasibility study of offline pileup correction for diamond microdosimeters; the method works in the four demonstrated cases but rests on a partially validated reference endpoint and a fitted pileup parameter. read the letter →

arxiv 2412.11593 v3 pith:2TWIZZEY submitted 2024-12-16 physics.med-ph physics.ins-det

Matthias Knopf , Sandra Barna , Daniel Radmanovac , Thomas Bergauer , Albert Hirtl , Giulio Magrin This is my paper · ORCID
classification physics.med-phphysics.ins-det
keywords microdosimetrymicrodosimetricspectrumpulsepileupcorrectionstochasticresamplingsolid-statemicrodosimeterclinicalionbeamslinealenergy
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 argues that pulse pileup, which distorts microdosimetric spectra recorded with solid-state detectors in clinical ion beams, can be corrected offline with a stochastic resampling algorithm rather than prevented in hardware. The algorithm simulates the pileup process from a low-flux reference spectrum, determines a pileup vector, and subtracts it from the measured high-flux spectrum iteratively. In four measurements with carbon ions at clinical rates, the correction reduced the average relative differences in the mean lineal energies $\bar{y}_F$ and $\bar{y}_D$ from $(3.57 \pm 1.92)\%$ and $(5.80 \pm 2.73)\%$ to $(0.28 \pm 0.08)\%$ and $(0.97 \pm 0.66)\%$. If this holds, microdosimetric characterization can be performed under true clinical beam conditions using conventional analog readout, supporting more accurate radiobiological modeling and quality assurance.

What carries the argument

The load-bearing object is the stochastic resampling algorithm, which treats the pulse train as a Poisson process with a pileup magnitude $\alpha\tau$, where $\tau$ is the pileup resolution time fixed by the shaping amplifier's unit pulse shape and $\alpha$ is an effective particle rate fitted per setup. It samples pulse heights from the measured spectrum, superposes scaled unit pulse shapes separated by random time intervals, and builds a simulated pileup spectrum; subtracting the bin-wise difference between the input and simulated spectrum produces a corrected spectrum, and the process is repeated until convergence. The unit pulse shape was measured from oscilloscope waveforms, giving a Gaussian pulse with shaping time $T = 1.7\,\mu\text{s}$ and $\tau = 7.5\,\mu\text{s}$.

What would settle it

Measure each of the four setups again at a true kHz-rate pileup-free reference and compare the corrected high-flux spectra to it; if the relative differences in $\bar{y}_F$ and $\bar{y}_D$ exceed the claimed $(0.28 \pm 0.08)\%$ and $(0.97 \pm 0.66)\%$, or if the low-flux spectra differ from the kHz reference by more than the correction's residual, the endpoint assumption is false and the correction is biased.

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Extended reading notes

Core claim

The central claim is that a resampling algorithm, originally developed for neutron spectra from tissue-equivalent proportional counters, can realistically recreate and then correct the pulse pileup seen in solid-state microdosimeters exposed to clinical ion beams. The paper demonstrates this by showing that pileup simulated from low-flux spectra reproduces the double-sum peaks and tail distortions of high-flux measured spectra, and that the iterative correction brings high-flux spectra into agreement with low-flux reference spectra. Quantitatively, the average relative difference between corrected and low-flux spectra falls to $(0.28 \pm 0.08)\%$ for the frequency-mean lineal energy $\bar{y}_F$ and $(0.97 \pm 0.66)\%$ for the dose-mean lineal energy $\bar{y}_D$, starting from uncorrected differences of $(3.57 \pm 1.92)\%$ and $(5.80 \pm 2.73)\%$.

Load-bearing premise

The correction relies on the assumption that spectra recorded at one-tenth of the clinical particle rate are effectively free of pileup and can serve as the target for all four setups, even though the paper validates this only for one carbon-ion case and calls the low-rate spectra 'not entirely pileup-free'.

Editorial extensions

If this is right

  • Once $\alpha\tau$ and the unit pulse shape are established for a readout setup, high-flux spectra can be corrected in a single step, allowing accurate microdosimetry at clinical dose rates with standard analog electronics.
  • The correction removes double-sum peaks and spectral broadening, so the frequency-mean and dose-mean lineal energies of corrected spectra match low-flux measurements to within about one percent.
  • For repeated quality-assurance measurements under fixed conditions, correction parameters can be pre-determined, enabling short, high-statistics acquisitions that would otherwise be unusable due to pileup.
  • The same pileup magnitude corrects spectra at different depths along the depth-dose curve for a fixed particle energy and readout chain, covering both plateau and Bragg-peak measurements.

Reading between the lines

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

  • A testable extension would apply the resampling correction to silicon or SiC microdosimeters; because the method only needs a unit pulse shape and a fitted $\alpha\tau$, it should transfer whenever the shaping response is stable.
  • The need to fit $\alpha$ per setup implies the method would benefit from an independent measurement of arrival-time statistics; comparing fitted $\alpha\tau$ values with beam-monitor timing data could expose how bunch structure biases the Poisson assumption.
  • Replacing the low-flux reference with a truly pileup-free kHz reference would let the same algorithm estimate residual pileup in the low-flux endpoint, potentially explaining the remaining $\sim1\%$ disagreement in $\bar{y}_D$.
  • The residual after correction sets a floor on achievable accuracy; tracking how that residual varies with spectral shape could guide whether a single-step or iterative correction should be trusted for a given radiation quality.
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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 adapts a stochastic resampling algorithm from Langen et al. to simulate and correct pulse pileup in microdosimetric pulse-height spectra acquired with a diamond microdosimeter in clinical carbon-ion beams at MedAustron. The method models pulse arrivals as a Poisson process, uses a measured semi-Gaussian pulse shape and a fitted pileup magnitude ατ, and corrects high-flux spectra by subtracting a simulated pileup component either in a single step or iteratively. The authors compare high-flux (4.3×10^7 s^-1) and low-flux (4.3×10^6 s^-1) measurements for four configurations (120 MeV/u plateau and Bragg peak; 120 MeV/u and 238.6 MeV/u plateau from a second campaign) and report that forward simulation reproduces the high-flux spectra from the low-flux spectra, and that iterative and single-step corrections reduce the relative differences in mean lineal energies to sub-percent levels.

Significance. If the result holds, the paper offers a practical offline alternative to online pileup rejection for solid-state microdosimeters at clinical ion-beam rates, addressing a genuine need where conventional pileup rejection is insufficient. The strongest evidence is the forward simulation: starting from measured low-flux spectra, the algorithm with a measured pulse shape and a single fitted pileup magnitude reproduces the measured high-flux spectra to within a few percent in all four configurations, which is a nontrivial test of the pileup model. The paper is also transparent about the main limitation, namely that ατ cannot be predicted from particle rates via Eq. (2) and must be established empirically for each setup. The contribution is incremental, being an adaptation of an existing algorithm, but the experimental dataset and the fidelity of the forward simulation make it a useful feasibility study for the microdosimetry community. The main weaknesses are the incomplete validation of the low-flux endpoint for three of the four setups and the partially circular evaluation of the iterative correction.

major comments (3)
  1. [Section 3, low-flux endpoint] The low-flux spectra at 4.3×10^6 s^-1 are adopted as the pileup-free endpoint for all four setups, yet the only validation against very-low-flux (kHz) data is reported for the 238.6 MeV/u plateau case (P(Δk_i)^2≈0.02, Fig. 3A). The 120 MeV/u plateau and Bragg-peak spectra (Fig. 4) were measured with a different amplifier gain, different lower cutoffs (y0=48 and 99.6 keV/µm versus 31.8 keV/µm), and different effective rates, so the statement that the low-flux spectra are 'not entirely pileup-free' leaves open the possibility that residual pileup at 4.3×10^6 s^-1 is non-negligible for those setups. Because the fitted ατ (0.1875 for Fig. 4) is determined by simulating pileup from these low-flux spectra, any residual pileup biases both the simulation and the evaluation, and the reported reduction to (0.28±0.08)% in yF and (0.97±0.66)% in yD would measure agreement with a contaminated reference. Please provide a kHz-rate validation for the 120 MeV/u setups, or a quantitative upper bound on the residual pileup probability using Eq. (1) with the established ατ and τ.
  2. [Section 4 and Table 1] The evaluation is partially circular for the iterative correction: the algorithm is iterated until convergence with the measured low-flux spectrum, and Table 1 then reports the difference between the corrected spectrum and that same low-flux reference. The non-circular evidence is the forward simulation, which uses the same fitted ατ to add pileup to the low-flux spectrum and reproduces the measured high-flux spectrum to within 0.02–0.72% in yF and 0.72–2.60% in yD; this should be presented as the primary validation of the pileup model. The single-step correction, which uses ατ predetermined from the forward simulation rather than from matching the corrected spectrum to the low-flux target, is a fairer test of the correction accuracy and should be reported alongside the iterative results as the main quantitative claim.
  3. [Section 4, Eq. (2)] The paper acknowledges that the rate-based estimate of ατ from Eq. (2) (0.126 for 120 MeV/u and 0.254 for 238.6 MeV/u) does not match the values needed for correction (0.1875/0.3 and 0.6, respectively). Since ατ is therefore a fitted parameter per setup, the method as demonstrated requires a low-flux reference spectrum for calibration, which is in tension with the abstract's claim that the method is useful 'in situations where a direct pileup-free measurement is currently not practicable.' Please clarify how ατ would be established in practice for a new clinical setup without such a reference, or explicitly reframe the contribution as a proof-of-principle requiring a one-time calibration.
minor comments (5)
  1. [Section 2, Materials and Methods] In the paragraph on internal measurements, the text states that 'the low flux setting is thus approximately 4.3·10^7 s^-1', but Figure 3 and Section 3 indicate the low-flux rate is 4.3·10^6 s^-1; please correct this typo.
  2. [Section 3, Figure 3A] The notation P(Δk_i)^2 is used to quantify the difference between normalized low-flux and very-low-flux spectra, but the definition of Δk_i is not given; please define it explicitly (e.g., bin-wise count difference).
  3. [Table 1] Table 1 reports relative differences to several decimal places without any statement of counting-statistics or systematic uncertainties; please add at least a rough uncertainty estimate for the mean values or explain why they are omitted.
  4. [Equation (3)] Equation (3) describes the semi-Gaussian pulse shape as V_out(t) = (V_max/n!) (t/T)^n exp(-t/T), but with this normalization the maximum of the pulse is V_max n^n e^{-n}/n!, not V_max; please clarify whether V_max denotes the peak amplitude or a scaling constant.
  5. [Section 4] The sentence 'For a different amplifier gain and particle energy, new ατ needed to be established' would benefit from a specification of which of the four spectra share the same amplifier gain and which require recalibration, since Figures 4 and 5 appear to come from two different measurement campaigns.

Circularity Check

2 steps flagged · score 6.0 of 10

The validation loop is fitted, not predicted: ατ is tuned per setup so the forward model maps low-flux onto high-flux, and the same low-flux spectrum then serves as the correction target and success metric; the sub-percent residuals measure self-consistency of that fit.

  1. fitted input called prediction [Section 4 (Discussion), Table 1]
    "To compare the spectra, the frequency average lineal energy ¯yF and the dose average lineal energy ¯yD values are taken as indicators for a global change in the data. This change is quantified as the relative difference in these values. The comparison is shown in table 1. The measured high flux spectra are taken as the reference for the pileup simulation, and the measured low flux spectra are taken as a reference for the pileup corrected spectra."

    The forward pileup simulation is controlled by the pileup magnitude ατ, and the paper states that α must be empirically determined per setup, with the analytical estimate from Eq. (2) explicitly failing. The values used (0.1875, 0.3, 0.6) are thus fitted so that the forward operator P_θ applied to the low-flux spectrum reproduces the measured high-flux spectrum; Table 1 shows simulated-pileup ¯yF differences of only 0.02–0.72% against high flux. The same fitted θ is then used in the iterative correction, which is evaluated against the same low-flux spectrum. Whenever P_θ(L) ≈ H by construction, L is a fixed point of the deconvolution, so the corrected spectrum must converge to L.

  2. other [Section 3 (Results), paragraph on very low flux validation]
    "Although not entirely pileup-free, the low flux spectra are considered the optimal endpoint for the pileup correction, as the relative difference between the low and extremely low flux spectra is negligible."

    The only direct evidence that the low-flux endpoint is close to pileup-free comes from a kHz-rate very-low-flux comparison at 238.6 MeV/u (P(Δk_i)^2 ≈ 0.02), and the paper notes that the very low flux setting was not used for the measurements in this paper. For the 120 MeV/u plateau and Bragg-peak setups, which use different gain, different energy cutoff, and different effective rate, no equivalent verification is shown. Because the low-flux spectra are simultaneously the input to the forward pileup simulation and the reference for judging correction success, any residual pileup in this endpoint biases the fitted ατ and the reported sub-percent residuals in the same direction.

full rationale

The central validation loop is circular in a specific, quotable way. The pileup magnitude ατ is the dominant free parameter controlling the simulated pileup (together with the measured unit pulse shape). The paper explicitly says α must be empirically determined for each setup and that the rate-based estimate from Eq. (2) does not give usable values: 'Using ατ = 0.126, however, neither adequately simulates pileup in the low flux spectra nor effectively corrects pileup in the high flux spectra.' The fit target is the measured high-flux spectrum, since Table 1 reports that forward simulation from the low-flux spectrum reproduces the high-flux ¯yF to within 0.02–0.72%. The same low-flux spectrum is then used as the correction target and as the evaluation reference. When the forward condition P_θ(L) ≈ H holds, L is a fixed point of the iterative deconvolution, so the corrected spectrum converging to L measures the internal consistency of the fitted operator, not an independent validation that the true pileup-free spectrum has been recovered. The paper's own admission that the low-flux spectra are 'not entirely pileup-free,' and that the very-low-flux kHz check was only made for 238.6 MeV/u, further weakens the claim that the endpoint is ground truth for the 120 MeV/u measurements. Some partial independence exists: one ατ was applied to both plateau and Bragg-peak spectra at the same energy, and different energies required different ατ, so the work is not entirely a tautology. Nevertheless, the headline numerical claims of accuracy are dominated by a per-setup fit, making the demonstration substantially circular rather than a first-principles prediction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central demonstration relies on a Poisson pileup model, an assumed identical pulse shape for all events, and the availability of a near-pileup-free low-flux reference. The pileup magnitude ατ is the key fitted parameter: it cannot be predicted from beam parameters, and the same low-flux spectra used to fit it are used to judge correction success. No new physical entities are introduced.

free parameters (3)
  • Pileup magnitude ατ for single-step correction = 0.1875 (120 MeV/u plateau/peak); 0.3 (120 MeV/u previous campaign); 0.6 (238.6 MeV/u previous campaign)
    Fitted per measurement campaign to match simulated pileup to measured high-flux spectra; Eq. (2) rate-based estimates (0.126 and 0.254) are explicitly rejected in the Discussion.
  • ατ for iterative correction = 0.0375 (used for all iterative corrections)
    A small fixed value chosen to add simulated pileup gradually over iterations; the convergence criterion references the low-flux spectrum, so the effective correction is still tied to the fitted reference.
  • Lower spectral cutoff y0 = 48 keV/µm (120 MeV/u plateau); 99.6 keV/µm (120 MeV/u Bragg peak); 31.8 keV/µm (238.6 MeV/u plateau)
    Hand-chosen per spectrum to exclude electronic noise and delta-ray tails before calculating mean values and before sampling pileup events; affects the reported means and pileup sampling.
assumptions (5)
  • domain assumption Particle inter-arrival times follow a Poisson process with a constant effective rate α
    Invoked in Eq. (1) to derive pileup probabilities P(m); the paper acknowledges real synchrotron beams show non-Poissonian bunching, so this is an approximation.
  • domain assumption The spectroscopic readout chain behaves as a non-paralyzable system
    Used in Section 2 to define pileup probability; arrivals within τ do not extend the pileup resolution time.
  • domain assumption All signals have an identical normalized semi-Gaussian pulse shape, scaled only by pulse height
    Used for pulse superposition in the resampling algorithm; shape parameters T=1.7 µs and τ=7.5 µs are fitted from oscilloscope waveforms.
  • domain assumption Low-flux spectra at 4.3×10^6 s^-1 are an adequate pileup-free reference
    Used as the target for correction and for fitting ατ; the paper shows a negligible difference to kHz-rate data only for one 238.6 MeV/u setup and calls the low-flux spectra "not entirely pileup-free".
  • domain assumption Pileup distortion is approximately additive and removable by subtracting the simulated pileup vector
    Core to the Langen correction step (Methods Step (iii)); no derivation or convergence proof is provided.

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Cite this review

Pith. "Pith review of Exploring Offline Pileup Correction to Improve the Accuracy of Microdosimetric Characterization in Clinical Ion Beams." pith.science (2026). https://pith.science/paper/2TWIZZEY

@misc{pith2026241211593,
  author       = {Pith},
  title        = {Pith review of: Exploring Offline Pileup Correction to Improve the Accuracy of Microdosimetric Characterization in Clinical Ion Beams},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2TWIZZEY}},
  note         = {Machine review of arXiv:2412.11593}
}
read the original abstract

Microdosimetry investigates the energy deposition of ionizing radiation at microscopic scales, beyond the assessment capabilities of macroscopic dosimetry. This contributes to an understanding of the biological response in radiobiology, radiation protection and radiotherapy. Microdosimetric pulse height spectra are usually measured using an ionization detector in a pulsed readout mode. This incorporates a charge-sensitive amplifier followed by a shaping network. At high particle rates, the pileup of multiple pulses leads to distortions in the recorded spectra. Especially for gas-based detectors, this is a significant issue, that can be reduced by using solid-state detectors with smaller cross-sectional areas and faster readout speeds. At particle rates typical for ion therapy, however, such devices will also experience pileup. Mitigation techniques often focus on avoiding pileup altogether, while post-processing approaches are rarely investigated. This work explores pileup effects in microdosimetric measurements and presents a stochastic resampling algorithm, allowing for offline simulation and correction of spectra. Initially it was developed for measuring neutron spectra with tissue equivalent proportional counters and is adapted for the use with solid-state microdosimeters in a clinical radiotherapy setting. The algorithm was tested on data acquired with solid-state microdosimeters at the MedAustron ion therapy facility. The successful simulation and reduction of pileup counts is achieved by establishing of a limited number of parameters for a given setup. The presented results illustrate the potential of offline correction methods in situations where a direct pileup-free measurement is currently not practicable.

Figures

Figures reproduced from arXiv: 2412.11593 by the authors.

Figure 1
Figure 1. Illustration of the formation of pulse pileup in the electronic readout chain. Individual pulses of heights hi are separated by inter-arrival times ∆ti . A pileup-free count is defined as two pulses separated by more than the pileup resolution time τ . Due to partial overlap of pulses, the spectra are skewed and broadened (tail pileup), while the complete overlap (peak pileup) leads to the formation of double sum pe… view at source ↗
Figure 2
Figure 2. shows the result of (1) and (2) for a situation representative of the proton and carbon-ion beams at the MedAustron facility (Grevillot et al. 2020) with a particle rate of 108 s −1 on a Gaussian beamspot of 5 mm full width at half maximum (FWHM) (Ulrich-Pur et al. 2021, Grevillot et al. 2018). Even with small cross-sectional areas and short shaping times, pileup remains a concern for state-of-the-art microdosimeter… view at source ↗
Figure 3
Figure 3. A) Measured high flux, low flux spectra and extremely low flux spectra for 12C 6+ ions at 238.6 MeV/u. B) Measured high flux and low flux spectra for 12C 6+ ions at 120 MeV/u. The spectral differences due to pileup are evident by the formation of a double sum peak around channel 110. Both measurements with (light colors) and without the internal pileup rejection logic (rich colors) are shown. The pileup rejection ci… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Measured high flux, low flux and pileup-corrected spectra for 12C 6+ ions at 120 MeV/u. Both the pileup simulations and the corrected spectra are depicted with dashed lines. The spectra were measured without online pileup rejection (PUR). (A) Spectrum measured in the p…
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Reviewed August 11, 2026 · model on record in the stance chip above.