In the three dimensional reconstruction of cone-beam CT, a fast reconstruction method based on the smallest three dimensional convex hull was proposed for actual scan data. First, according to the definition of the minimum three dimensional convex hull, the minimum three dimensional convex hull of the detected object was obtained in the actual scan using a segmentation algorithm based on the projected images, and then with the Z-line data first algorithm, the image reconstruction area was limited to the minimum three dimensional convex hull to enhance the cone-beam CT reconstruction speed by reducing the redundant computing. The experimental results show that this method can effectively reduce the memory consumption, and significantly improve the reconstruction speed and reduce the noise surrounding the object imaging area.

For the problem of image quality degradation in cone-beam CT (CBCT) based on flat panel detector (FPD), a genetic algorithm based on pre-segmentation (PS-GA) is proposed for CBCT projected image restoration. According to the characteristic of that most of the area of the projected image is empty and without the tested object, a robust segmentation algorithm is used in this method to segment the smallest rectangle that contains the tested object, and the calculating range is limited to the smallest rectangle by the specially designed genetic algorithm, which significantly reduced the amount of calculated data. The experimental results show that the method raised the edge sharpness, contrast-to-noise ratio (CNR) and average gradient (AG) of the projected images and slice images, and there is no visible artifacts introduced.

For the problem of image quality degradation of cone-beam Computed Tomography (CBCT) based on flat panel detector (FPD), a constrained least squares iteration (CLSI) restoration method based on pre-filtering is proposed. Firstly, the original projected images are denoised with bilateral filtering algorithm. Then, the denoised projected images are restored with CLSI. Finally, the final restored images are obtained by adding the noise images, which got by subtracting the projected images before and after denoising, to the restored images. The experimental results show that the method well inhibits the noise amplification phenomenon in image restoration, and increases the edge sharpness and contrast-to-noise ratio (CNR) of the projected images and slice images. The CBCT image quality is significantly improved with this method.