Experiment-based scatter correction for cone-beam computed tomography using the statistical method.

Scatter signals in cone-beam computed tomography (CBCT) cause a significant problem that degrades image quality of reconstructed images, such as inaccuracy of CT numbers and cupping artifacts. In this paper, we will present an experiment-based scatter correction method by pre-processing projection images using a statistical model combined with experimental kernels. The convolution kernels are estimated by using different thickness of PMMA plates attached to a beam stop lead sheet such that the scatter signal values can be measure in the shadow area of the projection images caused by the lead sheet. The scatter signal values of different thickness levels can be measured in the shadow area of projection images caused by the lead sheet. Then, the projection images are convolved with the kernels that are derived from the actual measurement of scatter signals in PMMA plates. Finally, the primary signals can be estimated using the maximum likelihood expectation maximization method. Experimental results by using the proposed method show that the quality of the reconstruction images is significantly improved. The CT numbers become more accurate and the cupping artifact is reduced.