Framework for iterative cone-beam micro-CT reconstruction

We have developed a computational framework for iterative cone-beam micro-CT reconstruction. When applied to small animal imaging and other high-resolution applications, iterative algorithms pose significant implementation difficulties due to the associated high computational burden. To address this burden, we use threads and message-passing (MPI) to facilitate multiprocessor cluster computing. The system matrix can be either pre-computed or calculated on-the-fly. Additionally, the system matrix can be based on trilinear interpolation or volumetric intersection methods. In the case of storing the system matrix, we exploit symmetries in the cone-beam geometry to reduce the storage requirements by a factor of nearly eight. We chose simultaneous iterative reconstruction technique (SIRT) to demonstrate the framework, although other algorithms are easily implemented. When pre-computing the system matrix, each iteration of SIRT completes several times faster than when using an on-the-fly system matrix computation. The pre-computed method remains faster when implementing ordered subsets, but the performance advantage diminishes as the number of subsets increases. We present reconstructions of the 3D Shepp-Logan head phantom as well as mouse data acquired by a small animal X-ray CT scanner.