REPRIOR: MEASUREMENT-RECONCILED PRIOR CORRECTION FOR SPARSE-VIEW CBCT RECONSTRUCTION

Sparse-view cone-beam computed tomography (CBCT) is severely under- constrained by limited X-ray projections. Learned reconstruction can provide complementary anatomical information, but using it as a prior in patient-specific Gaussian reconstruction introduces two challenges. First, the discrepancy between the learned prior and current reconstruction is spatially non-uniform: some local differences contain useful missing 3D information, whereas others reflect prior bias. Second, even a volumetrically beneficial correction can conflict with the observed X-ray measurements. Since X-ray measurements are line integrals, spa- tially separated corrections may affect overlapping rays, with their signed projec- tion responses reinforcing or partially canceling in the measurement residual, so their feasibility cannot always be assessed independently. In this paper, we intro- duce RePrior, a multi-stage progressive prior-correction framework that addresses correction actionability, measurement feasibility, and representation expressivity. Selective Correction Proposal (SCP) first identifies actionable signed local correc- tions from prior–reconstruction discrepancies. Measurement-Constrained Recon- ciliation (MCR) then jointly enforces their consistency with patient-specific X- ray measurements, while Hybrid Residual Enhancement (HRE) recovers addi- tional correction capacity through complementary voxel- and Gaussian-domain degrees of freedom around the reconciled anchor. RePrior requires neither test- time ground truth nor case-specific network fine-tuning. RePrior gains approx- imately 2.6 dB in 3D PSNR over R2-Gaussian across LUNA16, PANORAMA, and PENGWIN. Beyond prior-assisted reconstruction, it delivers additional gains in volumetric quality and observed-view projection fidelity, with consistent gains in held-out-view fidelity in the backbone-matched evaluation.