Sparse-View Lung Nodule Volumetry from Digitally Reconstructed Radiographs via AReT: Anatomy-Regularized TensoRF
arXiv:2606.02639v1 Announce Type: cross Abstract: We identify and resolve a previously unreported failure mode in TensoRF when applied to X-ray attenuation fields: the default density shift of -10, originally introduced for RGB scene reconstruction, suppresses density gradients and prevents sparse-view medical reconstruction regardless of learning rate or regularization strategy. Setting the density shift to zero restores gradient flow and enables stable volumetric reconstruction of pulmonary nodules from only three orthogonal X-ray projections. Building on this, we propose AReT, an anatomy-re
The continuous advancements in AI and imaging techniques are pushing the boundaries of medical diagnostics, making resolution of prior limitations a timely development.
This development significantly enhances the ability to perform early and accurate lung nodule detection with fewer X-ray projections, reducing radiation exposure and improving accessibility.
Sparse-view X-ray diagnostic capabilities in medical imaging are improved, allowing for more efficient and safer volumetric reconstruction of lung nodules.
- · Medical AI companies
- · Healthcare providers
- · Patients
Improved early detection rates for lung cancer through AI-enhanced sparse-view X-rays.
Reduced healthcare costs associated with screening and specialized imaging equipment due to fewer projections and potentially simpler devices.
Broader adoption of AI-driven diagnostic tools in regions with limited advanced medical imaging infrastructure.
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Read at arXiv cs.AI