Gradient-Based Trajectory Optimisation over Continuous Poses for Sparse-View Cone-Beam CT

2026-10-07

AI Summary

This study addresses the limitations of fixed candidate pools and object-dependent precomputation in sparse-view CT trajectory optimization by proposing a continuous-pose manifold gradient ascent method. By modeling source poses as continuous variables, the approach jointly optimizes soft Tuy coverage, view covariance, and an analytical decay-aware penalty on kinematic manifolds, enabling off-grid refinement and unified adaptation to diverse parameterized manifolds. The proposed method eliminates the need for object-specific reconstruction bases and completes trajectory selection within seconds. It successfully recovers defects invisible to circular orbits while matching or surpassing the performance of discrete search strategies. Furthermore, its practical feasibility is validated on a robotic CT platform.

Abstract

Trajectory optimisation for cone-beam computed tomography (CT) determines which information sparse-view scans acquire. Fixed candidate pools prevent off-grid refinement and require new object-specific precomputation for each acquisition manifold. We make every source pose an individual continuous variable and move all poses jointly by gradient ascent on the scanner's kinematic manifold. The objective combines soft-Tuy plane coverage, continuous View Covariance Loss, and an analytic attenuation-aware ray-bundle penalty. The same optimiser handles circular, limited C-arm, two-axis, and freesphere parametrisations. On a Defrise flange, continuous selection recovers laminar defects invisible to a circular orbit, matches discrete swap search on the free sphere at the sparser budget, and leads at the denser one, with the same objective evaluated in every arm. A moderate elevation band already recovers most of the free-sphere gain at the defects, so the same optimiser transfers to bounded scanner envelopes. Photon noise preserves the ordering on the flange and compresses it on a dense fuel nozzle. Sparseprescan planning benefits from matching prescan and planned acquisition manifolds. Selection takes seconds rather than minutes without an object-specific reconstruction basis. Prescan-planned poses were executed on a robot CT bench and reconstructed in a common frame, demonstrating feasibility but no consistent metric gain over uniform band sampling. Continuous pose optimisation incorporates attenuation and scanner constraints directly into sparse-view acquisition design.

Problem

• Cone-Beam CT
• Trajectory Optimisation
• Sparse-View
• Continuous Poses
• Acquisition Design

Innovation

• trajectory optimisation
• continuous poses
• sparse-view cone-beam CT
• gradient ascent
• attenuation-aware penalty