A GPU-based multi-resolution approach to iterative reconstruction algorithms in x-ray 3D dual spectral computed tomography

Abstract

The technology of X-ray dual spectral computed tomography (DSCT) scans an object using two different x-ray energy spectra. The acquired DSCT raw data from this procedure can be used to measure the object by reconstructing its basis material images. Dual energy iterative algorithms can be applied to produce highly quantitative basis material images. However, they are not commonly employed in current medical applications due to the extensive computational demands required, such as for 3D cone-beam DSCT reconstruction. In order to improve the performance of the iterative algorithms for high-resolution cone-beam DSCT reconstruction, a graphics processing unit (GPU)-based multi-resolution approach is proposed. This approach divides the reconstructed volume into multiple sub-volumes, and each sub-volume is reconstructed at high resolution from the corrective polychromatic projections evaluated by a pre-reconstructed low-resolution volume. After all the sub-volumes are reconstructed, the sub-volumes are combined to yield the whole volume at high resolution. This approach requires less GPU memory, and significantly improves the performance of iterative reconstruction while maintaining a high image quality. The advantages of this approach were verified by numerical experiments in which FORBILD head phantom and thorax phantom were iteratively reconstructed from noise-free and noisy polychromatic projections.