# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 1 Introduction  
The purpose of radiotherapy is to deliver a planned radiation dose to a delineated cancerous region to kill cancer cells while preserving the surrounding normal structures and leaving tissues unharmed [1; 2; 3]. It is very difficult to accurately radiate the target region and ensure the surrounding normal areas are not damaged [4; 5]. Image-guided radiotherapy (IGRT) is a new cancer treatment technology in which images are used to guide the whole radiation process including position guidance, pre-operative and postoperative analysis [6; 7; 8]. The key step of IGRT is to collect and analyze CT and CBCT images of the target area. High-quality CT images only need to be collected a few times for diagnosis and treatment planning, and CBCT images need to be collected many times for patient positioning guidance during the radiotherapy. CBCT imaging is used to replace CT imaging as much as possible to reduce the harm caused by the high dose of CT to patients. When IGRT-based treatment is implemented, some image technologies need to be used to supervise and guide the whole treatment process. Due to this process often requiring multiple acquisitions and analyses of images, CBCT, with its lower imaging dose, is usually used as the guidance image to reduce the harm to the patient [9; 10]. In IGRT, the most important step is to analyze the position information between the CBCT image and the delineated CT images; that is, the radiologist needs to register and align the CBCT and the CT images. As shown in Figure 1, CBCT images usually have low contrast and visual quality due to their small imaging dose compared with CT images. Compared with CT, CBCT hasa small imaging dose, which means that its imaging is more susceptible to interference from environments such as a strong magnetic field, resulting in more noise in the CBCT image. The degradation of CBCT will reduce image contrast and increase the ambiguity of structures of important regions, which makes it unable to play its due role in IGRT [11]. [PERSON] et al. proved that the accuracy of radiotherapy can be improved by using CBCT enhancement technology in IGRT [12]. Thus, it has become a new research topic to use image enhancement technology to improve the quality of CBCT. In recent decades, different types of efficient methods have been proposed for image enhancement [13; 14; 15], such as the well-known histogram equalization (HE) method [16], dark channel prior (DCP) method [17], signal scale retinex (SSR) method [18], multiscale retinex with color restoration (MSRCR) method [19], semi-decoupled decomposition (SSD) method [20], and statistical methods with image processing concepts (SMIPC) [21]. Among them, HE is a classical image enhancement algorithm, which mainly improve the visual quality by calculating and adjusting the distribution of pixel values and increasing the gray level difference of the input image. The DCP method assumes that the image is degraded during the imaging process and then calculates a transmission map to estimate the degradation to enhance the degraded image. The DCP method has better performance in foggy image enhancement. SSR and MSRCR are typical methods based on retinex theory. SSR is a single-scale retinex algorithm, which achieves enhancement by establishing an illumination reflection model in the imaging process and performing high-frequency enhancement processing on the reflected component. MSRCR is a multi-SSR-based algorithm with a color restoration factor to adjust the contrast of the input image and improve the color information. SSD is a Gaussian total variation model performing retinex decomposition in a semi-decoupled fashion for simultaneous brightness enhancement and noise suppression. SMIPC-based methods enhance the contrast of medical images using statistical methods with image processing concepts.