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REVIEW 2 major objections 1 minor 2 references

An open-source pipeline using PyPulseq and BART delivers vendor-agnostic 4D phase contrast MRI for muscle velocity, displacement, and strain.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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An open-source vendor-agnostic 4D PC-MRI pipeline using PyPulseq, BART reconstruction, and a gradient probing sequence was developed and validated for muscle velocity, displacement, and strain analysis on two Siemens systems during NMES contractions.

T0 review reviewed 2026-06-27 challenge →

load-bearing objection The paper delivers a practical open-source 4D PC-MRI pipeline with time savings and strain metrics, but the vendor-agnostic claim rests on untested extrapolation from two Siemens systems. the 2 major comments →

arxiv 2606.09444 v1 pith:UYUOEBUF submitted 2026-06-08 eess.IV

Vendor-agnostic 4D Phase Contrast MRI: a complete open-source pipeline for velocities, displacement, and strain analysis

Marta B. Maggioni , Sabine M. R\"auber , Katarina Pu\v{s} , Bostjan \v{S}imuni\v{c} , Xeni Deligianni , Regina M. M. Schlaeger , Francesco Santini This is my paper
classification eess.IV
keywords phase contrast MRI4D flowopen-source pipelinemuscle straincompressed sensingPyPulseqBART reconstructionneuromuscular electrical stimulation
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.

The reading

The paper develops a complete open-source 4D flow PC-MRI pipeline that integrates a compressed sensing sequence, BART reconstruction, and strain analysis tools. It includes a gradient probing sequence to handle velocity sign assignment correctly across different scanner setups. Validation occurred on two Siemens 3T systems during NMES-induced contractions in forearm and thigh muscles, cutting acquisition times from 35-80 minutes down to 5-11 minutes. The work extracts peak strain, mean strain, and buildup rates from sigmoid-fitted curves, revealing strains roughly ten times higher in the vastus lateralis than in the flexor digitorum superficialis. This supplies a reproducible framework for quantitative muscle motion imaging without proprietary vendor software.

Core claim

The pipeline demonstrates that a compressed sensing-accelerated 4D PC-MRI sequence implemented in PyPulseq, reconstructed via BART, and paired with a gradient probing sequence produces consistent velocity and strain measurements across two different 3T Siemens scanners, enabling extraction of muscle strain parameters from NMES contractions in under 11 minutes per scan.

What carries the argument

The gradient probing sequence that ensures correct velocity sign assignment across scanner orientations and vendors, together with the PyPulseq-implemented compressed sensing sequence and BART reconstruction for 4D flow data.

Load-bearing premise

The gradient probing sequence ensures correct velocity sign assignment and the results from two Siemens 3T systems generalize to other vendors and field strengths.

What would settle it

Applying the pipeline to a non-Siemens scanner or different field strength and observing mismatched velocity directions or strain values that cannot be corrected by the probing sequence.

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

If this is right

  • Strain maps and sigmoid-fitted curves yield extractable values for peak strain, mean strain, and buildup rate during muscle contraction.
  • Vastus lateralis strains reach median peak values around 0.49 compared to 0.063 in flexor digitorum superficialis.
  • Compressed sensing reduces forearm and thigh acquisition times to 5 and 11 minutes respectively.
  • The framework supports quantitative muscle imaging with demonstrated consistency on two distinct 3T Siemens platforms.

Where Pith is reading between the lines

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

  • The open pipeline could support direct comparison of muscle function metrics between independent research groups without vendor-specific processing steps.
  • Extending the gradient probing approach to additional field strengths might allow strain measurements in pediatric or high-field settings with minimal recalibration.
  • Integration with other open-source tools for NMES modeling could link electrical stimulation parameters directly to measured strain buildup rates.
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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

2 major / 1 minor

Summary. The manuscript presents a fully open-source 4D phase contrast MRI pipeline that integrates a compressed-sensing accelerated sequence implemented in PyPulseq, BART-based reconstruction, and a gradient probing sequence for velocity sign assignment. It reports validation on two Siemens 3T systems (Prisma and Vida Fit) for forearm (n=9) and thigh (n=10) muscle strain analysis during NMES-induced contractions, with acquisition time reductions from 35/80 min to 5/11 min and extraction of peak/mean strain and buildup rate via sigmoid fitting.

Significance. If the vendor-agnostic aspects hold, the work would offer a significant contribution by supplying a reproducible open-source framework for quantitative muscle imaging. The provision of a fully open-source pipeline with PyPulseq and BART integration, together with the demonstrated acceleration via compressed sensing, are explicit strengths that promote accessibility and standardization in the field.

major comments (2)
  1. [Abstract] Abstract and validation description: the central claim of a 'vendor-agnostic' pipeline (title and abstract) rests on data acquired exclusively on two Siemens 3T systems; no acquisitions, sign-consistency checks, or strain metrics from GE, Philips, or other field strengths are presented, directly undermining the generalization required by the title and the gradient-probing motivation.
  2. [Methods] Methods (gradient probing sequence) and Results: the gradient probing sequence is stated to ensure correct velocity sign assignment across vendors, yet all reported data (n=9 arm, n=10 leg) and sign validation remain confined to the same Siemens pair, leaving transferability of sequence timing and reconstruction assumptions untested.
minor comments (1)
  1. [Results] Results (strain metrics): the reported median peak strain values (0.49 vs. 0.063) and order-of-magnitude difference lack accompanying error bars, fit uncertainties, or statistical comparisons, which would strengthen the quantitative claims even if the primary validation scope is addressed.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments on the scope of the vendor-agnostic claims. We address each point below, acknowledging the limitations of our current validation dataset while defending the design rationale based on the open-source tools employed.

read point-by-point responses

  1. Referee: [Abstract] Abstract and validation description: the central claim of a 'vendor-agnostic' pipeline (title and abstract) rests on data acquired exclusively on two Siemens 3T systems; no acquisitions, sign-consistency checks, or strain metrics from GE, Philips, or other field strengths are presented, directly undermining the generalization required by the title and the gradient-probing motivation.

    Authors: We agree that the empirical validation is confined to two Siemens 3T systems and that this limits the strength of the generalization in the title and abstract. The vendor-agnostic framing derives from the use of PyPulseq (which generates sequences portable across vendors without proprietary pulse sequence environments), BART (vendor-independent reconstruction), and the gradient probing sequence (intended to resolve velocity sign conventions that can differ by vendor or orientation). We will revise the abstract, title if appropriate, and discussion to state explicitly that the pipeline is designed to be vendor-agnostic and demonstrated on Siemens platforms, with the gradient probing method positioned as enabling future cross-vendor use. No additional scanner data will be added. revision: partial

  2. Referee: [Methods] Methods (gradient probing sequence) and Results: the gradient probing sequence is stated to ensure correct velocity sign assignment across vendors, yet all reported data (n=9 arm, n=10 leg) and sign validation remain confined to the same Siemens pair, leaving transferability of sequence timing and reconstruction assumptions untested.

    Authors: The gradient probing sequence acquires short calibration data to determine velocity sign polarity independently of vendor-specific assumptions about gradient directions or coordinate systems; it is implemented entirely in PyPulseq and uses the same BART reconstruction pipeline. Sign consistency was verified on the Siemens datasets to confirm the method functions as intended. We acknowledge that transferability of timing parameters and reconstruction assumptions to other vendors has not been empirically tested. In revision we will expand the methods to detail the general design and add an explicit limitations paragraph noting the need for future multi-vendor validation. revision: partial

standing simulated objections not resolved
  • Empirical validation of sequence portability, sign assignment, and strain metrics on non-Siemens vendors or other field strengths is unavailable.

Circularity Check

0 steps flagged

No circularity: empirical pipeline validation with no derivation chain

full rationale

The paper describes construction and empirical validation of an open-source 4D PC-MRI pipeline (PyPulseq sequence, BART reconstruction, gradient probing, strain analysis) on two Siemens 3T systems. No mathematical derivations, first-principles results, parameter fittings, or uniqueness theorems are claimed. All quantitative outputs (strain maps, peak/mean strain, buildup rate) are direct measurements from acquired data rather than quantities predicted from fitted inputs or self-referential definitions. The vendor-agnostic claim rests on experimental compatibility checks within the tested systems, not on any loop that reduces to the paper's own assumptions by construction. This is a standard methods-and-validation manuscript with no load-bearing self-citation chains or ansatz smuggling.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The pipeline rests on standard MRI physics for phase contrast velocity encoding and compressed sensing reconstruction assumptions; no new physical entities are introduced and no free parameters are fitted to support the central compatibility claim.

free parameters (1)
  • sigmoid curve parameters for strain
    Strain time courses are fitted with a sigmoid to extract peak strain, mean strain, and buildup rate; these parameters are data-dependent but not central to the pipeline claim.
axioms (2)
  • standard math Phase contrast MRI signal phase encodes velocity linearly under standard gradient conditions
    Invoked throughout the velocity and displacement analysis sections.
  • domain assumption Compressed sensing reconstruction faithfully recovers the accelerated 4D flow data
    Used to justify the 5- and 11-minute acquisition times.

reviewed 2026-06-27 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Vendor-agnostic 4D Phase Contrast MRI: a complete open-source pipeline for velocities, displacement, and strain analysis." pith.science (2026). https://pith.science/paper/UYUOEBUF

@misc{pith2026260609444,
  author       = {Pith},
  title        = {Pith review of: Vendor-agnostic 4D Phase Contrast MRI: a complete open-source pipeline for velocities, displacement, and strain analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UYUOEBUF}},
  note         = {Machine review of arXiv:2606.09444}
}
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read the original abstract

Phase contrast MRI (PC MRI) enables quantitative assessment of tissue motion and strain. Although it is increasingly used, standardized, vendor-agnostic pipelines for accelerated acquisitions remain scarce. We present a fully open-source 4D flow PC-MRI pipeline integrating a compressed sensing-accelerated sequence implemented in PyPulseq, BART-based reconstruction, and strain analysis. Additionally, a gradient probing sequence was developed to ensure correct velocity sign assignment across scanner orientations and vendors. The pipeline was validated across two Siemens MRI systems (3T MAGNETOM Prisma and 3T Vida Fit) in two anatomical applications: forearm (Flexor Digitorum Superficialis, n=9) and thigh (Vastus Lateralis, n=10) during Neuromuscular Electrical Stimulation (NMES)-induced contractions. Compressed sensing reduced acquisition times from 35 and 80 minutes to 5 and 11 minutes for the arm and leg acquisitions, respectively. Muscle strain maps and sigmoid-fitted strain curves enabled extraction of peak strain, mean strain, and buildup rate. Strains in the Vastus Lateralis were approximately one order of magnitude higher than in the Flexor Digitorum Superficialis (median peak strain 0.49 vs. 0.063, mean strain 0.31 vs. 0.031). The pipeline demonstrates multi-platform compatibility and provides a reproducible, open framework for quantitative muscle imaging.

discussion (0)

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Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

  1. [1]

    Phase-Contrast MRI: Physics, Techniques, and Clinical Applications,

    D. T. Wymer, K. P. Patel, W. F. Burke, and V. K. Bhatia, “Phase-Contrast MRI: Physics, Techniques, and Clinical Applications,” RadioGraphics , vol. 40, no. 1, pp. 122–140, Jan. 2020, doi: 10.1148/rg.2020190039. [2] M. Markl, A. Frydrychowicz, S. Kozerke, M. Hope, and O. Wieben, “4D flow MRI,” J. Magn. Reson. Imaging JMRI , vol. 36, no. 5, pp. 1015–1036, N...

    work page doi:10.1148/rg.2020190039 2020

  2. [2]

    ORMIR-MIDS: an open standard for curating and sharing musculoskeletal imaging data,

    F. Santini et al. , “ORMIR-MIDS: an open standard for curating and sharing musculoskeletal imaging data,” JBMR Plus , vol. 10, no. 3, p. ziag013, Mar. 2026, doi: 10.1093/jbmrpl/ziag013. [25] N. J. Pelc, M. A. Bernstein, A. Shimakawa, and G. H. Glover, “Encoding strategies for three-direction phase-contrast MR imaging of flow,” J. Magn. Reson. Imaging JMRI...

    work page doi:10.1093/jbmrpl/ziag013 2026

This paper was first reviewed by grok-4.3 on June 27, 2026.