DRACO: differentiable reconstruction for arbitrary CBCT orbits.

Objective: This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms.

Approach: The proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability.