arxiv: 2604.28179 · v1 · submitted 2026-04-30 · 💻 cs.CV

Recognition: unknown

Stop Holding Your Breath: CT-Informed Gaussian Splatting for Dynamic Bronchoscopy

Andrea Dunn Beltran , Daniel Rho , Aarav Mehta , Xinqi Xiong , Ra\'ul San Jos\'e Est\'epar , Ron Alterovitz , Marc Niethammer , Roni Sengupta

Authors on Pith no claims yet

Pith reviewed 2026-05-07 07:16 UTC · model grok-4.3

classification 💻 cs.CV

keywords bronchoscopyGaussian splattingrespiratory motionCT registrationdynamic reconstructionendoscopic navigationdeformation modeling

0 comments

The pith

Registering inhale-exhale CT scans reduces airway motion to a single phase scalar that constrains Gaussian splatting for continuous 1.22 mm accurate reconstruction without breath-holds.

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

The paper establishes that respiratory deformation during bronchoscopy, which shifts anatomy by 5-20 mm, can be managed by registering already-acquired paired inhale and exhale CT scans to define a patient-specific deformation space. This space is reduced to a scalar breathing phase per endoscopic frame, which a lightweight estimator predicts directly from RGB images. The phase then anchors and deforms a mesh-guided Gaussian splatting model so that every reconstruction stays within anatomically observed configurations across the full respiratory cycle. A sympathetic reader would care because this removes the need for hard-to-reproduce breath-hold protocols that currently disrupt workflow and limit localization reliability.

Core claim

By registering paired inhale-exhale CT scans to reduce respiratory motion to a single scalar breathing phase per frame and embedding this representation within a mesh-anchored Gaussian splatting framework that infers phase directly from endoscopic RGB, the method produces geometrically faithful, deformation-aware 3D reconstructions throughout the respiratory cycle without breath-holds or external sensing, achieving 1.22 mm target localization accuracy within 3 mm clinical tolerances and over 20x faster training than unconstrained single-CT baselines.

What carries the argument

Mesh-anchored Gaussian splatting whose Gaussians are deformed according to a scalar breathing phase inferred from RGB; the phase selects a configuration inside the deformation space obtained by registering the patient's inhale and exhale CT volumes.

If this is right

Continuous navigation becomes possible without interrupting procedures for breath-holds.
Training time drops by more than a factor of 20 compared with unconstrained single-CT baselines.
Target localization reaches 1.22 mm, staying inside the 3 mm clinically accepted tolerance.
Reconstructions remain geometrically faithful to observed anatomy across the full breathing cycle.
Quantitative benchmarking is enabled by the introduced RESPIRE pipeline that supplies per-frame ground truth for geometry, pose, phase, and deformation.

Where Pith is reading between the lines

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

The same CT-derived deformation space and phase constraint could be tested in other motion-sensitive endoscopic settings such as upper GI procedures.
If the phase estimator proves robust across patients, the method might reduce the number of CT acquisitions needed per case.
Combining the RGB phase signal with existing vital-sign monitors could provide a low-cost backup when image quality drops.
The RESPIRE simulation itself supplies a ready testbed for comparing future dynamic reconstruction techniques against the same ground-truth deformation fields.

Load-bearing premise

Paired inhale-exhale CT scans are available for every patient and can be accurately registered to define the deformation space, and breathing phase can be reliably inferred from endoscopic RGB images alone by a lightweight estimator.

What would settle it

Localization error exceeding 3 mm on the RESPIRE simulation when the registered CT deformation space is replaced by a single static CT or when phase estimates are deliberately offset by 20 percent of the cycle.

Figures

Figures reproduced from arXiv: 2604.28179 by Aarav Mehta, Andrea Dunn Beltran, Daniel Rho, Marc Niethammer, Ra\'ul San Jos\'e Est\'epar, Ron Alterovitz, Roni Sengupta, Xinqi Xiong.

**Figure 1.** Figure 1: RESPIRE Overview. Top: Existing bronchoscopy datasets lack dense geometric and respiratory ground truth; RESPIRE provides all six annotation types. Bottom: The RESPIRE pipeline generates realistic bronchoscopy sequences with perframe annotations from paired inspiration/expiration CTs. simulation framework that generates realistic bronchoscopic video with perframe respiratory deformation from any pair of… view at source ↗

**Figure 2.** Figure 2: RESPIRE Results. Top: rendered RGB and depth versus ground truth. Our method maintains geometric fidelity under respiratory deformation; BridgeSplat produces plausible appearance but incorrect geometry, with the mesh drifting outside the airway lumen in distal segments. Bottom: recovered breathing phase trajectory and endpoint contour overlay, showing sub-millimeter alignment at the final frame. Rendering … view at source ↗

read the original abstract

Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creating CT-to-body divergence that limits localization accuracy. In practice, this is mitigated through breath-hold protocols, which attempt to match the intraoperative anatomy to a static CT, but are difficult to reproduce and disrupt clinical workflow. We propose to eliminate the need for breath-hold protocols by leveraging patient-specific respiratory modeling. Paired inhale-exhale CT scans, already acquired for planning, implicitly define the patient-specific deformation space of the breathing airway. By registering these scans, we reduce respiratory motion to a single scalar breathing phase per frame, constraining all reconstructions to anatomically observed configurations. We embed this representation within a mesh-anchored Gaussian splatting framework, where a lightweight estimator infers breathing phase directly from endoscopic RGB, enabling continuous, deformation-aware reconstruction throughout the respiratory cycle without breath-holds or external sensing. To enable quantitative evaluation, we introduce RESPIRE, a physically grounded bronchoscopy simulation pipeline with per-frame ground truth for geometry, pose, breathing phase, and deformation. Experiments on RESPIRE show that our approach achieves geometrically faithful reconstruction, over 20x faster training, and 1.22 mm target localization accuracy (within the 3mm clinically relevant tolerances) outperforming unconstrained single-CT baselines. Please check out our website for additional visuals: https://asdunnbe.github.io/RESPIRE/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper reduces breathing motion to a scalar phase via paired inhale-exhale CT registration, infers it from RGB, and drives mesh-anchored Gaussian splatting for dynamic bronchoscopy recon, delivering 1.22 mm sim accuracy and 20x speedup but with no real-data results shown.

read the letter

The paper's main contribution is a way to handle respiratory deformation in bronchoscopy navigation by registering paired inhale-exhale CT scans to collapse the motion into a single scalar phase. They then train a lightweight estimator to predict that phase from the endoscopic video frames and use it to condition a mesh-anchored Gaussian splatting model for reconstruction. This is new in how it combines the CT-derived deformation prior with Gaussian splatting for this specific clinical setting. The mesh anchoring helps keep the Gaussians geometrically consistent with the airway structure. On their new RESPIRE simulation, they report 1.22 mm target accuracy and over 20 times faster training than baselines, which is a solid result if it holds. The soft spot is the reliance on the simulation for all quantitative claims. The abstract mentions no real bronchoscopy data, so we don't see how well the phase estimator works when actual breathing includes variations not spanned by the two CT scans. If the registration between inhale and exhale has errors or if the motion isn't truly one-dimensional, the fidelity claim weakens. The method is grounded in patient-specific CT, which is realistic since those scans are already part of planning. No obvious circularity in the evaluation. This work is for researchers in medical image reconstruction and computer-assisted interventions. A reader interested in dynamic neural rendering for endoscopy would get value from the technical setup and the simulation pipeline. It deserves a serious referee because the problem is clinically relevant and the proposed framework is clearly described with quantitative support on a controlled testbed. I would send it for peer review to get feedback on the real-data gap and the robustness of the phase reduction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a CT-informed Gaussian splatting method for dynamic bronchoscopy that registers paired inhale-exhale CT scans to reduce respiratory deformation to a single scalar breathing phase. A lightweight estimator infers this phase from endoscopic RGB frames, which then constrains mesh-anchored 3D Gaussians to produce deformation-aware reconstructions without breath-hold protocols. The authors introduce the RESPIRE simulation pipeline, which supplies per-frame ground truth for geometry, pose, phase, and deformation. Experiments on RESPIRE report 1.22 mm target localization accuracy (within the 3 mm clinical tolerance) and over 20x faster training relative to unconstrained single-CT baselines.

Significance. If the central claims hold, the work addresses a clinically relevant problem in bronchoscopic navigation by eliminating disruptive breath-hold protocols while leveraging routinely acquired paired CT data. The reported 1.22 mm accuracy and substantial training speedup would be meaningful for real-time applications. The introduction of RESPIRE as a physically grounded simulation with explicit per-frame ground truth for multiple quantities is a clear positive contribution that can support future benchmarking. The approach of embedding patient-specific CT-derived deformation directly into the Gaussian splatting pipeline is technically interesting and avoids external sensors.

major comments (3)

[Abstract and Method] Abstract and Method section on deformation modeling: The claim that registering paired inhale-exhale CT scans 'reduce[s] respiratory motion to a single scalar' is load-bearing for the geometric fidelity and 1.22 mm accuracy results, yet the manuscript provides no quantitative bound on registration error or residual deformation variance after projection onto this scalar (e.g., from asymmetric lobe motion or posture shifts). Without such analysis, it is unclear whether the mesh-anchored Gaussians remain in anatomically valid configurations under realistic intraoperative variability.
[Abstract and Experiments] Abstract and Experiments section: The quantitative results (1.22 mm accuracy, 20x speedup) are obtained on the newly introduced RESPIRE simulation, but the paper lacks details on phase estimator training (supervision signal, architecture, loss, dataset splits), phase prediction error metrics, and propagation of phase error into localization error. This information is required to assess whether the reported accuracy is robust or sensitive to estimator performance.
[Experiments] Experiments section: No ablation or sensitivity study examines the effect of registration quality or unmodeled deformation degrees of freedom on final reconstruction and localization metrics. A table relating phase estimation error to target localization error would directly test the load-bearing assumption that the 1D phase manifold suffices for geometrically faithful results.

minor comments (2)

[Abstract] The provided website link is useful for visuals, but the manuscript should state whether the RESPIRE pipeline code or trained models will be released to support reproducibility.
[Method] Notation for the breathing phase scalar and its mapping to Gaussian parameters could be formalized with an equation in the Method section to improve clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and for acknowledging the clinical relevance of eliminating breath-hold protocols as well as the utility of the RESPIRE simulator. We address each major comment below and will revise the manuscript accordingly to provide the requested quantitative analyses, implementation details, and sensitivity studies.

read point-by-point responses

Referee: [Abstract and Method] Abstract and Method section on deformation modeling: The claim that registering paired inhale-exhale CT scans 'reduce[s] respiratory motion to a single scalar' is load-bearing for the geometric fidelity and 1.22 mm accuracy results, yet the manuscript provides no quantitative bound on registration error or residual deformation variance after projection onto this scalar (e.g., from asymmetric lobe motion or posture shifts). Without such analysis, it is unclear whether the mesh-anchored Gaussians remain in anatomically valid configurations under realistic intraoperative variability.

Authors: We agree that a quantitative characterization of registration error and residual deformation variance is necessary to substantiate the scalar-phase reduction. In the revised manuscript we will add a dedicated paragraph in the Method section that reports: (i) landmark-based registration accuracy (using the carina and five major bronchial bifurcations) between the inhale and exhale CT volumes, (ii) the L2 norm of residual deformation after projection onto the learned 1-D phase manifold, and (iii) a sensitivity experiment in RESPIRE that injects controlled asymmetric lobe deformations and posture-induced shifts not captured by the scalar model. These additions will explicitly bound the geometric fidelity of the mesh-anchored Gaussians under the variability present in our simulator. revision: yes
Referee: [Abstract and Experiments] Abstract and Experiments section: The quantitative results (1.22 mm accuracy, 20x speedup) are obtained on the newly introduced RESPIRE simulation, but the paper lacks details on phase estimator training (supervision signal, architecture, loss, dataset splits), phase prediction error metrics, and propagation of phase error into localization error. This information is required to assess whether the reported accuracy is robust or sensitive to estimator performance.

Authors: We acknowledge the need for complete reproducibility details. The revised manuscript will expand the Experiments section (with an appendix for full hyper-parameters) to specify: the phase estimator architecture (EfficientNet-B0 backbone with a two-layer regression head), supervision (per-frame ground-truth phase provided by RESPIRE), loss (L1 on phase normalized to [0,1]), and dataset splits (70/15/15 train/val/test across 12 simulated patient sequences). We will also report phase-prediction MAE (0.048 phase units) and include a new error-propagation plot that sweeps injected phase noise from 0 to 0.15 and measures the resulting target-localization error, confirming that the system stays within the 3 mm clinical tolerance for the observed estimator accuracy. revision: yes
Referee: [Experiments] Experiments section: No ablation or sensitivity study examines the effect of registration quality or unmodeled deformation degrees of freedom on final reconstruction and localization metrics. A table relating phase estimation error to target localization error would directly test the load-bearing assumption that the 1D phase manifold suffices for geometrically faithful results.

Authors: We agree that an explicit sensitivity analysis is required. We will add a new subsection and table in the Experiments section that (i) perturbs the inhale-exhale CT registration by 0–5 mm and reports the resulting change in localization error, (ii) introduces synthetic unmodeled deformation modes (lobe-specific scaling and torsion) not representable by the scalar phase and measures reconstruction PSNR and localization degradation, and (iii) presents a binned table of phase-estimation error versus target-localization error. All experiments will be performed on RESPIRE, directly testing the sufficiency of the 1-D manifold for the claimed accuracy. revision: yes

Circularity Check

0 steps flagged

No significant circularity; central claims rest on independent CT registration and external simulation evaluation.

full rationale

The derivation registers paired inhale-exhale CT scans (acquired independently for planning) to define a scalar breathing-phase manifold, then trains a separate lightweight RGB estimator to recover that scalar per frame. These steps are not self-referential: the deformation space is supplied by external CT data, the estimator is not fitted to the final reconstruction metric, and quantitative claims (1.22 mm accuracy, geometric fidelity) are measured against the RESPIRE simulation pipeline whose per-frame ground truth is generated separately from the method itself. No equation reduces the output accuracy to a parameter defined by the output; no load-bearing uniqueness theorem or ansatz is imported via self-citation. This is the normal non-circular case.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that respiratory motion can be captured by registering paired inhale-exhale CT scans and reduced to a single scalar phase, plus the availability of such scans for each patient.

free parameters (1)

phase estimator parameters
The lightweight estimator is trained on data, implying fitted parameters whose exact count and values are not specified in the abstract.

axioms (1)

domain assumption Respiratory motion of the airway can be reduced to a single scalar breathing phase per frame after registering paired inhale-exhale CT scans.
This reduction is invoked to constrain all reconstructions to anatomically observed configurations.

invented entities (1)

RESPIRE simulation pipeline no independent evidence
purpose: Generates per-frame ground truth for geometry, pose, breathing phase, and deformation to enable quantitative evaluation of the reconstruction method.
Newly introduced in the paper specifically for testing; no independent evidence outside this work.

pith-pipeline@v0.9.0 · 5591 in / 1597 out tokens · 124990 ms · 2026-05-07T07:16:12.378783+00:00 · methodology

discussion (0)

Reference graph

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