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computed tomography

topic3 events
papersTODAY 04:00 UTC

Calibrated Uncertainty Maps for Radiative Gaussian Splatting in Sparse-View CT

Researchers examine whether uncertainty maps reliably indicate where a sparse-view CT reconstruction is erroneous, and find a discrepancy between whole-volume scoring and localization of errors within the imaged object. They derive analytic, clamp-aware moments to produce calibrated uncertainty estimates for radiative Gaussian splatting reconstructions. The work suggests standard variance-based uncertainty is not equivalent to an error map in this setting.

medical-imaging/uncertainty/gaussian-splatting/sparse-view-ct
papersTODAY 04:00 UTC

Radiomics Method Separates Voxel Geometry From Interpolation Effects in CT/MRI

A new arXiv preprint addresses a known pitfall in radiomic texture analysis: features are typically calculated over voxel-index neighborhoods, which assumes uniform spatial spacing. In anisotropic scans, this assumption mixes true voxel geometry with signal changes introduced by interpolation. The authors propose a physically aware approach that avoids interpolation so the two effects can be disentangled.

medical-imaging/radiomics/research/arxiv
papersSEP 10 04:00 UTC

Method reconstructs volumetric CT scans from single chest X-rays via multi-pass blended learning

Researchers propose a multi-pass, multi-view blended learning approach for generating full 3D chest CT volumes from a single 2D chest radiograph. The task is an ill-posed inverse problem, made harder by the limited availability of paired X-ray and CT training data. The paper builds on earlier methods that relied on digitally reconstructed radiographs to overcome data scarcity.

medical-imaging/ct-synthesis/research/deep-learning