Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/conditional-diffusion-for-3d-ct-volume-reconstruction-from-2d-x-rays

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 54
Source count: 3
Coverage: 83%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID conditional-diffusion-for-3d-ct-volume-reconstruction-from-2d-x-rays | Route /signal-canvas/conditional-diffusion-for-3d-ct-volume-reconstruction-from-2d-x-rays

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: 54

Proof: Verification pending

Freshness state: computing

Source paper: Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

PDF: https://arxiv.org/pdf/2603.26509v1

Repository: https://github.com/ai-med/AXON

Source count: 3

Coverage: 83%

Last proof check: 2026-03-30T20:30:26.830Z

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Ready for execution: Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

/buildability/conditional-diffusion-for-3d-ct-volume-reconstruction-from-2d-x-rays

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Subject: Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

Verdict

Build Now

Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.

Preparing verified analysis

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Last commit

3/30/2026

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Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
In this work, we propose AXON, a multi-stage diffusion-based framework that reconstructs high-fidelity 3D CT volumes directly from real X-rays.
Implicationpartial
This is a core claim stated in the abstract and supported by the overall description of the framework.
Verificationpartial
partial
Evidencepartial
AXON employs a coarse-to-fine strategy, with a Brownian Bridge diffusion model-based initial stage for global structural synthesis, followed by a ControlNet-based refinement stage for local intensity optimization.
Implicationpartial
The abstract explicitly details the two-stage approach and the models used in each stage.
Verificationpartial
partial
Evidencepartial
A super-resolution network is integrated to upscale the generated volumes to achieve diagnostic-grade resolution.
Implicationpartial
The abstract clearly states the inclusion of a super-resolution network for achieving diagnostic-grade resolution.
Verificationpartial
partial
Evidencepartial
Evaluations on both public and external datasets demonstrate that AXON significantly outperforms state-of-the-art baselines, achieving a 11.9% improvement in PSNR and a 11.0% increase in SSIM with robust generalizability across disparate clinical distributions.
Implicationpartial
Specific quantitative results are provided in the abstract, directly comparing AXON to baselines.
Verificationpartial
partial
Evidencepartial
Evaluations on both public and external datasets demonstrate that AXON significantly outperforms state-of-the-art baselines, achieving a 11.9% improvement in PSNR and a 11.0% increase in SSIM with robust generalizability across disparate clinical distributions.
Implicationpartial
The abstract explicitly mentions robust generalizability as a key outcome of the evaluations.
Verificationpartial
partial
Evidencepartial
The approach depends on the quality of X-ray input and may be limited by anatomical variability.
Implicationpartial
This is mentioned as a caveat in the analysis section.
Verificationpartial
partial
Evidencepartial
The approach depends on the quality of X-ray input and may be limited by anatomical variability.
Implicationpartial
This is mentioned as a limitation in the analysis section.
Verificationpartial
partial
Evidencepartial
It also supports bi-planar X-ray input to mitigate depth ambiguities inherent in 2D-to-3D reconstruction.
Implicationpartial
This technical detail is explicitly stated in the abstract as a feature of AXON.
Verificationpartial
partial
Evidencepartial
In this work, we propose AXON, a multi-stage diffusion-based framework that reconstructs high-fidelity 3D CT volumes directly from real X-rays.
Implicationpartial
This is the core assertion of the abstract and the paper's title.
Verificationpartial
partial
Evidencepartial
AXON employs a coarse-to-fine strategy, with a Brownian Bridge diffusion model-based initial stage for global structural synthesis, followed by a ControlNet-based refinement stage for local intensity optimization.
Implicationpartial
The abstract explicitly describes the two-stage approach and the models used in each stage.
Verificationpartial
partial
Evidencepartial
A super-resolution network is integrated to upscale the generated volumes to achieve diagnostic-grade resolution.
Implicationpartial
The abstract clearly states the inclusion of a super-resolution network for achieving diagnostic-grade resolution.
Verificationpartial
partial
Evidencepartial
Evaluations on both public and external datasets demonstrate that AXON significantly outperforms state-of-the-art baselines, achieving a 11.9% improvement in PSNR and a 11.0% increase in SSIM with robust generalizability across disparate clinical distributions.
Implicationpartial
The abstract provides specific quantitative improvements in PSNR and SSIM.
Verificationpartial
partial

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Conditional Diffusion for 3D CT Volume Reconstruction from 2D X-rays

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