Multi-resolution system for artifact removal and edge enhancement in computerized tomography images
Pattern Recognition Letters
GPU-based 3D cone-beam CT image reconstruction for large data volume
Journal of Biomedical Imaging
High-performance cone beam reconstruction using CUDA compatible GPUs
Parallel Computing
Accelerating cone beam reconstruction using the CUDA-enabled GPU
HiPC'08 Proceedings of the 15th international conference on High performance computing
GPU-based cone beam computed tomography
Computer Methods and Programs in Biomedicine
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Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry.