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Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates

T0 review · 3 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A biventricular statistical shape model of 271 healthy hearts, built with universal-coordinate correspondence, identifies ventricular size, elongation, and chamber position as the leading anatomical modes and links them to real ECG and…

desk verdict Solid biventricular SSM with a genuinely new UVC-based correspondence method and useful released resources; the main soft spot is that the correspondence is only indirectly validated, but the paper is worth refereeing. read the letter →

arxiv 2501.04504 v2 pith:2H5MLLWT submitted 2025-01-08 q-bio.TO

Lore Van Santvliet , Elena Zappon , Matthias A.F. Gsell , Franz Thaler , Maarten Blondeel , Steven Dymarkowski , Guido Claessen , Rik Willems
show 4 more authors
Martin Urschler Bert Vandenberk Gernot Plank Maarten De Vos
This is my paper · ORCID
classification q-bio.TO
keywords statisticalshapemodeluniversalventricularcoordinatesbiventricularanatomycardiacdigitaltwinelectrocardiogramprincipalcomponentanalysissyntheticcohorthealthypopulation
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 sets out to show that a statistical shape model of the healthy biventricular heart can be built cheaply and reliably by using universal ventricular coordinates to place corresponding points on every heart, bypassing the costly nonlinear registration step that earlier shape models rely on. Trained on 271 high-resolution CT scans of healthy individuals, the model finds that ventricular size (40.34% of variance), elongation (11.19%), and the relative apical-basal position of the left and right ventricles (9.59%) are the three leading modes, jointly explaining 61.12% of shape variability. The authors then connect these modes to clinical measurements: after multiple-testing correction, larger hearts correlate with shorter RR intervals, larger R-wave amplitudes, taller height, and larger body surface area, among other significant associations. If these correlations are correct, they give digital-twin builders a real-world, population-level link between anatomy and electrocardiography that can be used to calibrate and validate electrophysiological simulations, and they make a synthetic cohort of 100 ready-to-simulate volumetric meshes with fiber orientations available.

What carries the argument

The central object is the universal ventricular coordinate (UVC) system, which assigns every ventricular location four coordinates: an apico-basal coordinate $z \in [0,1]$, a transmural coordinate $\rho \in [0,1]$ from endocardium to epicardium, a rotational coordinate $\phi$, and a chamber label $\nu \in \{-1,1\}$ for left or right ventricle. The paper samples a regular grid of $(z, \rho, \phi, \nu)$ tuples, extracts the corresponding point from each subject's volumetric mesh (by interpolation when needed), and triangulates the resulting point clouds directly, producing 31,724 corresponding points per heart. This turns shape correspondence into a simple coordinate lookup, so ordinary PCA on translation- and rotation-aligned point clouds yields modes that describe biological shape differences rather than artifacts of a registration procedure.

What would settle it

Rebuild the SSM from the same 271 CT scans using a conventional nonlinear registration method for correspondence instead of UVC sampling, then compare the top three PCs and the PC1-RR-interval correlation: if the leading modes or the sign and significance of the reported correlations shift materially, the UVC correspondence assumption is the cause.

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Extended reading notes

Core claim

The central claim is that biventricular anatomy in a healthy population varies primarily along three interpretable axes: overall ventricular size, ventricular elongation (most visible in the right ventricle), and the relative apical-basal positioning of the left and right ventricles. These axes are the first three principal components of a statistical shape model trained on 271 healthy CT scans, built by sampling a fixed grid of universal ventricular coordinates on every geometry and applying PCA to the resulting point clouds. The paper further claims that these anatomical modes carry real electrophysiological signal: after Holm-Bonferroni correction, the size mode correlates significantly with ECG-derived features such as the RR interval ($R = -0.50$), QT interval, T-wave duration, and R-wave amplitudes, and with demographic variables including height ($R = -0.62$), weight, body surface area, BMI, and age. A leave-one-out reconstruction error of 0.61 mm indicates that the model represents unseen healthy geometries about as well as the CT resolution allows.

Load-bearing premise

The load-bearing premise is that sampling the same grid of universal ventricular coordinates on every heart yields the same anatomical points on each heart; if that correspondence is distorted, the principal components and all reported correlations with ECGs and demographics would be misleading.

Editorial extensions

If this is right

  • A digital-twin pipeline can obtain patient-specific biventricular anatomy from CT without a nonlinear registration step, because UVC grids give point correspondence directly.
  • The significant anatomy-to-ECG correlations provide real-patient targets for validating cardiac electrophysiology simulations: simulated ECGs from size-varying meshes should reproduce the observed signs and rough magnitudes of association.
  • The released model and 100 synthetic meshes with fibers and anatomical labels allow large, privacy-preserving virtual cohorts for in silico trials and training data-driven calibration models.
  • Because size is the dominant mode, accurately representing heart size in a digital twin is the highest-leverage anatomical choice for reproducing clinically measured ECG features.

Reading between the lines

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

  • If the same UVC-grid approach were extended to the atria, as the authors suggest as future work, the analogous shape-to-P-wave correlations could be tested directly with the released workflow.
  • Because the size mode is tightly correlated with height and body surface area, a follow-up analysis that regresses out body-size variables before correlating PC1 with ECG features would separate anatomical heart size from systemic body scaling.
  • One could generate synthetic meshes at extreme PC1 values and run reaction-eikonal simulations to check whether the predicted R-wave and RR-interval changes reproduce the observed correlations in magnitude, not just in sign.
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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 / 4 minor

Summary. This paper presents a biventricular statistical shape model (SSM) built from 271 healthy cardiac CT scans, using universal ventricular coordinates (UVCs) to sample corresponding point clouds, followed by generalized Procrustes alignment and PCA. The authors report that the first three PCs encode ventricular size, elongation, and relative LV/RV positioning, jointly explaining 61.12% of the variance; a leave-one-out cross-validation reconstruction error of 0.61 mm; a synthetic cohort of 100 volumetric meshes with fiber orientations and anatomical labels; and statistically significant correlations between PC scores and ECG-derived and demographic features, notably PC1 with RR interval (R = -0.50) and height (R = -0.62). The paper also studies dataset-size effects on reconstruction error and sex-related representativeness of the SSM, and it releases the SSM, code, and synthetic meshes.

Significance. If the UVC-based correspondence is valid, this is a valuable contribution: the dataset is larger and at higher spatial resolution than most prior cardiac SSM studies, the anatomical twinning pipeline is highly automated, the LOOCV evaluation is carefully executed, multiple-testing correction is applied, and the public release of ready-to-simulate meshes with fibers and labels directly supports downstream electrophysiological simulation studies. The reported correlations between anatomy and clinical ECG features are also of practical interest for anchoring digital-twin validation. However, the central methodological premise—that fixed UVC tuples correspond to the same anatomical location across subjects—is only indirectly validated, and the correlation analysis is performed in-sample without adjustment for body size or sex. These issues affect the strength of the paper's main claims and require further analysis before the anatomy-to-ECG link can be considered established.

major comments (3)
  1. [Sections 2.2.1, 2.2.3, and 3.1 (reconstruction error and UVC correspondence)] The reconstruction error reported as 0.61 mm is defined as the mean distance between corresponding points in the original and reconstructed UVC-resampled point clouds, not as a point-to-surface distance to the original 0.9 mm anatomical meshes. Since both point clouds are generated by the same UVC sampling and triangulation, this metric measures how well the SSM reproduces UVC-sampled coordinates, not how faithfully it reconstructs the underlying anatomy. The validation in Appendix A against the original meshes concerns sampling error for choosing the mesh size, not the SSM reconstruction error. The pointwise error map in Figure 8 shows the largest errors at the RV apex and the ventricular base/LV-RV junction, which are precisely the regions where UVC alignment is hardest (singular coordinate geometry at the apex, valve-ring dependence at the base). Because PC2 and PC3 encode elongation and relative LV/RV position, these modes—and hence the correlations in Table 3—may contain variance that is an artifact of the correspondence construction rather than true anatomical variation. I ask the authors to validate the correspondence more directly, for example by computing point-to-surface reconstruction errors of the reconstructed meshes against the original anatomical surfaces, or by comparing PC modes with a registration-based SSM on a subset of the data.
  2. [Section 2.4.2 and Table 3 (in-sample correlation analysis)] The correlation analysis uses PC scores estimated from the same 271 subjects that define the PCA, and the reported p-values do not account for the uncertainty in the estimated PC loadings. More importantly, no adjustment is made for sex, height, or body surface area. Since PC1 is described as overall ventricular size, the strong correlations of PC1 with height (R = -0.62) and BSA (R = -0.55) are partly expected from the construction of the size mode, and the PC1-ECG correlations such as RRint (R = -0.50) could be driven by body size and sex differences rather than by ventricular shape per se. To support the claimed anatomy-ECG link in Section 4.7, the authors should report partial correlations controlling for sex, height, and BSA, or validate the correlations in an independent cohort. Without this, the directional claims such as 'larger ventricles are associated with longer RR and QT intervals' remain vulnerable to confounding.
  3. [Sections 2.1.3, 2.2.1, and 4.3 (fixed RV wall thickness and Laplacian smoothing)] The manuscript states in Section 2.1.3 that RV myocardial thickness variations are not captured but fixed, and Section 4.3 acknowledges this limitation. This means the SSM is not a complete biventricular shape model, and the synthetic cohort inherits the same restriction; correlations with ECG features cannot be interpreted as reflecting full biventricular anatomical variability. In addition, Section 2.2.1 applies Laplacian smoothing to LV basal regions selected by visual inspection. Because the same smoothing is applied to all meshes to preserve correspondence, it can systematically shift corresponding points in exactly the regions where reconstruction errors are largest, potentially affecting PC loadings and downstream correlations. I ask the authors to quantify the sensitivity of the PC scores and of the main correlations to the smoothing procedure, or to justify the smoothing region choice with a quantitative criterion rather than visual inspection alone.
minor comments (4)
  1. [Table 2 and Section 2.4.1] The electrical axis is listed as an ECG-derived feature in Section 2.4.2 but is not included in Table 2; please add it to the table or clarify its source and measurement lead configuration.
  2. [Table 3] The p-values in Table 3 are reported in units of 10^-3, but the caption does not state this, and the text refers to p = 0.00; please clarify the scaling and avoid presenting rounded values as exactly zero.
  3. [Section 2.3.1 and Appendix E] Synthetic geometries are sampled from 94 PCs under the assumption that PC scores are normally distributed, but the normality verification in Appendix E is reported only for the first 16 PCs; please either verify the remaining PCs or explicitly state the assumption for PCs 17-94.
  4. [Figure 8] The left panel is described as a 'random subject'; please clarify whether the same subject is used for the pointwise error map and for the average error map, or specify how the representative subject was chosen.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the anatomical SSM and its ECG/demographic correlations are not reduced to their inputs by construction, and the UVC correspondence premise rests on an external published coordinate framework rather than a self-citation chain.

full rationale

The central derivation chain is observational rather than self-referential. The SSM is built from UVC-sampled point clouds of 271 CT anatomies via Procrustes alignment and PCA (Sections 2.2.1-2.2.3), and the ECG features are extracted independently from 12-lead ECGs with ECGdeli (Section 2.4.1); no ECG or demographic value enters the PCA, so the reported correlations (Table 3) are not forced by construction. The main cited premise, UVC-based correspondence, is a published coordinate system ([29], with the computation pipeline described in [11]); while some authors overlap with the present paper, the UVC framework is an external, parameter-free anatomical coordinate definition, not an ansatz introduced ad hoc to obtain the reported PCs, and no uniqueness theorem is invoked to forbid alternatives. The paper's reconstruction error metric is computed between corresponding UVC-sampled point clouds and therefore chiefly validates dimensionality reduction rather than anatomical correspondence; the authors explicitly rely on agreement with prior SSMs for indirect validation of the correspondence (Section 4.2), which is a limitation but not a circular step. The in-sample estimation of PCs and correlations on the same 271 subjects means p-values do not account for PC uncertainty; that is a statistical caveat, not a derivation-level circularity. No equation or fitted parameter is equivalent to the claimed output by construction.

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

The SSM is fitted entirely from the 271-subject dataset, so no physical constants or external benchmarks are used to set the PCA. The main imported assumptions are the UVC parametrization, a fixed RV wall thickness, the Gaussian assumption for synthetic sampling, and the healthy status of the mixed cohort. The free parameters listed are modeling choices such as sampling density, smoothing regions, and PC truncation rather than physical constants.

free parameters (4)
  • UVC sampling scheme parameters = 80 z levels; LV phi 160 (80 near apex, 40 nearest apex); RV phi 80 (40 near apex, 20 nearest apex); coarsening…
    Appendix A compares five schemes and selects scheme 4 as the default; this choice affects point cloud density, reconstruction error, and all downstream PCA results.
  • Laplacian smoothing regions = z in [0.70,1], phi_LV in [pi/4,3pi/4] and [-9pi/16,-3pi/16]
    Section 2.2.1 applies smoothing to regions selected by visual inspection of several meshes; the same regions are applied to all subjects, altering the geometry that enters the PCA.
  • Number of principal components for synthetic cohort = 94 (99% variance)
    Section 2.3.1 samples synthetic geometries from the first 94 PCs because they explain 99% of variance; this truncation is a modeling choice that shapes the synthetic cohort.
  • Procrustes convergence tolerance = 1e-5
    Section 2.2.2 stops iterative Procrustes alignment when the Frobenius norm between successive averages falls below 1e-5, a numerical convergence choice.
assumptions (4)
  • domain assumption UVCs provide a consistent anatomical parametrization across hearts
    All correspondence and point extraction are based on universal ventricular coordinates defined in prior work [29], invoked in Section 2.2.1. The method assumes equal UVC values mark the same anatomical location in every subject.
  • domain assumption RV wall thickness can be fixed using population priors
    Section 2.1.3 fixes RV wall thickness by extrusion with prior knowledge [42,43]. Patient-specific RV wall thickness variation is therefore absent from the shape model and cannot be captured by the PCs.
  • ad hoc to paper PC scores are normally distributed for all sampled components
    Section 2.3.1 samples 94 PCs from N(0, lambda_k), while Appendix E verifies normality only for the first 16 PCs. The remaining 78 components are assumed normal without evidence.
  • domain assumption The enrolled subjects represent a healthy population
    Section 2.1.1 and Section 4.9 describe different inclusion criteria for male (Master@Heart trial) and female (clinical referral) subjects. The assumption that both groups are equivalently healthy is load-bearing for the sex and size comparisons.

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Cite this review

Pith. "Pith review of Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates." pith.science (2026). https://pith.science/paper/2H5MLLWT

@misc{pith2026250104504,
  author       = {Pith},
  title        = {Pith review of: Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2H5MLLWT}},
  note         = {Machine review of arXiv:2501.04504}
}
read the original abstract

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to the patient-specific cardiac anatomy. In a number of studies, the effect of anatomical variation on clinically relevant functional measurements like electrocardiograms (ECGs) is investigated, using computational simulations. While such a simulation environment provides researchers with a carefully controlled ground truth, the impact of anatomical differences on functional measurements in real-world patients remains understudied. In this study, we develop a biventricular statistical shape model and use it to quantify the effect of biventricular anatomy on ECG-derived and demographic features, providing novel insights for the development of digital twins of cardiac electrophysiology. To this end, a dataset comprising high-resolution cardiac CT scans from 271 healthy individuals, including athletes, is utilized. Furthermore, a novel, universal, ventricular coordinate-based method is developed to establish lightweight shape correspondence. The performance of the shape model is rigorously established, focusing on its dimensionality reduction capabilities and the training data requirements. Additionally, a comprehensive synthetic cohort is made available, featuring ready-to-use biventricular meshes with fiber structures and anatomical region annotations. These meshes are well-suited for electrophysiological simulations.

Figures

Figures reproduced from arXiv: 2501.04504 by the authors.

Figure 1
Figure 1. Overview of the UVCs, with important ventricular structures and viewpoints used throughout this paper. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Apical coarsening shown in various views on the ventricles. Top left: anterior-posterior view, bottom left: [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Graphical representation of the UVC-based point extraction process. The points extracted at two subsequent [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: Graphical representation of the set of points extracted at the base of the RV [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Generated endo- and epicardial LV and RV surfaces, shown in basal-apical, anterior-posterior and posterior [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Effect of smoothing on the LV. The regions where smoothing is applied, are shown in orange. The top row [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Overview of the statistical shape model, trained on the unbiased study population. The effect of each principal [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Pointwise reconstruction error mapped to biventricular geometry. Left: subject-specific pointwise reconstruc [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Reconstruction errors in the leave-one-out cross-validation experiment incorporating all subjects, displayed [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Decrease of reconstruction errors in the leave-one-out cross-validation experiments with increasing numbers [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: Reconstruction errors for male and female subjects in a series of SSMs constructed using a male, female or [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: Distribution of scores for PC1 for different demographic subgroups. The scores are scaled by their standard [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]
Figure 13
Figure 13. Figure 13: Synthetic geometries, obtained from sampling from 94 PCs of the full SSM. Each row shows one synthetic [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]
Figure 14
Figure 14. Figure 14: Decrease of sampling errors with increasing mesh sizes for UVC-based resampling. The means, extrema, [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Reconstruction errors in the leave-one-out cross-validation experiment incorporating 56 subjects from [PITH_FULL_IMAGE:figures/full_fig_p021_15.png]
Figure 16
Figure 16. Figure 16: Heart size-independent reconstruction errors in the leave-one-out cross-validation experiment incorporating [PITH_FULL_IMAGE:figures/full_fig_p022_16.png]
Figure 17
Figure 17. Figure 17: Heart size-independent reconstruction errors for male and female subjects in a series of SSMs constructed [PITH_FULL_IMAGE:figures/full_fig_p022_17.png]
Figure 18
Figure 18. Figure 18: Distribution of scores for various PCs for the different demographic subgroups. The PC scores are scaled by [PITH_FULL_IMAGE:figures/full_fig_p022_18.png]
Figure 19
Figure 19. Figure 19: Distribution of the PC scores of the first 16 PCs in the full training set, compared to the normal distribution. [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]

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Pith tools

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