Soft Tuy-Completeness for Robust Projection Selection in Cone-Beam CT

2026-05-20

AI Summary

This study addresses the challenge of projection selection for region-of-interest imaging in cone-beam CT, where the classical Tuy completeness condition—being binary—fails to balance feature scale and optimization efficiency. The authors generalize this condition into a continuous, differentiable soft near-orthogonality score and integrate it with a resolution-aware saturation coverage objective. They propose a submodular greedy algorithm with a (1−1/e)-approximation guarantee and formulate a mixed-integer linear program (MILP) to certify solution optimality. Experiments across six target regions, multiple projection budgets, and four occlusion scenarios demonstrate that the median ratio between greedy and MILP-optimal objective values reaches 0.998, with numerous instances provably globally optimal. Furthermore, the proposed Effective Spatial Resolution (ESR) metric reliably predicts reconstruction quality.

Abstract

This work introduces a continuous soft near-orthogonality score and a resolution-aware saturated coverage objective for projection selection in region-of-interest focused cone-beam CT, grounded in Tuy's completeness theory. Replacing the binary hit-or-miss model of classical Tuy completeness with a graded, differentiable formulation preserves a direct link to achievable feature sizes while enabling both efficient approximate and exact optimisation. We establish that the underlying discrete decision problems are NP-complete via polynomial-time reductions from Set Cover, motivating a submodular greedy algorithm with proven $(1-1/\mathrm{e})$ approximation guarantees and a mixed-integer linear program (MILP) that provides certified optimality bounds. The MILP serves as a quality certificate for the greedy solution rather than a competing optimiser. The primary empirical finding confirms this relationship: across a systematic benchmark spanning six target regions, multiple projection budgets, and four controlled occlusion conditions, the pooled median greedy-to-MILP objective ratio was 0.998, with a substantial fraction of cases certified globally optimal. A binary formulation is included as a diagnostic baseline; it strengthens hard directional completeness but is weaker on the continuous coverage scale. We additionally introduce Effective Spatial Resolution (ESR), a physically interpretable trajectory-level diagnostic that maps directional sampling gaps to achievable feature sizes. ESR correlates reliably with matched reconstruction quality across projection budgets and occlusion levels, providing a practical bridge between the selection stage and the image domain without requiring reconstruction.

Problem

• projection selection
• Tuy completeness
• cone-beam CT
• region-of-interest
• spatial resolution

Innovation

• soft Tuy-completeness
• projection selection
• submodular optimization
• mixed-integer linear programming
• effective spatial resolution