Deep Learning Computed Tomography based on the Defrise and Clack
  Algorithm
Chengze Ye Linda-Sophie Schneider Yipeng Sun Andreas Maier
This study presents a novel approach for reconstructing cone beam computed
tomography (CBCT) for specific orbits using known operator learning. Unlike
traditional methods, this technique employs a filtered backprojection type
(FBP-type) algorithm, which integrates a unique, adaptive filtering process.
This process involves a series of operations, including weightings,
differentiations, the 2D Radon transform, and backprojection. The filter is
designed for a specific orbit geometry and is obtained using a data-driven
approach based on deep learning. The approach efficiently learns and optimizes
the orbit-related component of the filter. The method has demonstrated its
ability through experimentation by successfully learning parameters from
circular orbit projection data. Subsequently, the optimized parameters are used
to reconstruct images, resulting in outcomes that closely resemble the
analytical solution. This demonstrates the potential of the method to learn
appropriate parameters from any specific orbit projection data and achieve
reconstruction. The algorithm has demonstrated improvement, particularly in
enhancing reconstruction speed and reducing memory usage for handling specific
orbit reconstruction.