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ARXIV:2603.06852 · MEDICAL AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.06852MEDICAL AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction.
Opportunity summary
Pain An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and…
Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate sparse-view CT reconstruction.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experimental results on arbitrary-trajectory CT benchmarks demonstrate that our density-guided perturbation strategy effectively eliminates geometric artifacts and consistently outperforms existing baselines in progressive…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction.
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10.48550/arXiv.2603.06852An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction.
Abstract
Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate sparse-view CT reconstruction. Despite these algorithmic advancements, practical reconstruction fidelity remains fundamentally bounded by the quality of the captured data, raising the crucial yet underexplored problem of X-ray active view selection. Existing active view selection methods are primarily designed for natural-light scenes and fail to capture the unique geometric ambiguities and physical attenuation properties inherent in X-ray imaging. In this paper, we present Perturbed Gaussian Ensemble, an active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting. Specifically, we identify low-density Gaussian primitives that are likely to be uncertain and apply stochastic density scaling to construct an ensemble of plausible Gaussian density fields. For each candidate projection, we measure the structural variance of the ensemble predictions and select the one with the highest variance as the next best view. Extensive experimental results on arbitrary-trajectory CT benchmarks demonstrate that our density-guided perturbation strategy effectively eliminates geometric artifacts and consistently outperforms existing baselines in progressive tomographic reconstruction under unified view selection protocols.
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Dimensions overall score 7.0
PROBLEM
An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate spar...
METHOD
Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate sparse-view CT reconstruction.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experimental results on arbitrary-trajectory CT benchmarks demonstrate that our density-guided perturbation strategy effectively eliminates geometric artifacts and consistently outperforms exist...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate sparse-view CT reconstruction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Sparse-view computed tomography (CT) is critical for reducing radiation exposure to patients. Recent advances in radiative 3D Gaussian Splatting (3DGS) have enabled fast and accurate sparse-view CT reconstruction.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Extensive experimental results on arbitrary-trajectory CT benchmarks demonstrate that our density-guided perturbation strategy effectively eliminates geometric artifacts and consistently outperforms existing baselines in progressive tomographic reconstruction under unified view selection protocols.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An active view selection framework that integrates uncertainty modeling with sequential decision-making, tailored for X-ray Gaussian Splatting, to improve sparse-view CT reconstruction.
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Medical AI
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Commercial read
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