A novel image-domain-based cone-beam computed tomography enhancement algorithm.

Kilo-voltage (kV) cone-beam computed tomography (CBCT) plays an important role in image-guided radiotherapy. However, due to a large cone-beam angle, scatter effects significantly degrade the CBCT image quality and limit its clinical application. The goal of this study is to develop an image enhancement algorithm to reduce the low-frequency CBCT image artifacts, which are also called the bias field. The proposed algorithm is based on the hypothesis that image intensities of different types of materials in CBCT images are approximately globally uniform (in other words, a piecewise property). A maximum a posteriori probability framework was developed to estimate the bias field contribution from a given CBCT image. The performance of the proposed CBCT image enhancement method was tested using phantoms and clinical CBCT images. Compared to the original CBCT images, the corrected images using the proposed method achieved a more uniform intensity distribution within each tissue type and significantly reduced cupping and shading artifacts. In a head and a pelvic case, the proposed method reduced the Hounsfield unit (HU) errors within the region of interest from 300 HU to less than 60 HU. In a chest case, the HU errors were reduced from 460 HU to less than 110 HU. The proposed CBCT image enhancement algorithm demonstrated a promising result by the reduction of the scatter-induced low-frequency image artifacts commonly encountered in kV CBCT imaging.