Correction for cone beam CT image artifacts via a deep learning method

Flat-panel detector based cone-beam CT systems have been widely used in image-guided interventions and image-guided radiation therapy. However, several notoriously difficult challenges persist in these cone-beam CT systems: given the relatively large cone angles used in data acquisition, scatter induced artifacts significantly degrade image quality and thus the algorithms to reduce these artifacts have remained an active area of research through out the past decade. To accommodate for the limited detector dynamic range, these systems often use auto-exposure control to homogenize the noise distribution, and as a result, both kV and mA are modulated in some systems making beam hardening correction extremely difficult. Additionally, when data acquisition time is long, inadvertent motion artifacts often exacerbate the situation. In this paper, we develop a deep learning method to empirically correct these most often observed artifacts in flat-panel based CBCT images.