As CBCT is widely used in dental and maxillofacial imaging, it is important for users as well as referring practitioners to understand the basic concepts of this imaging modality. This review covers the technical aspects of each part of the CBCT imaging chain. First, an overview is given of the hardware of a CBCT device. The principles of cone beam image acquisition and image reconstruction are described. Optimization of imaging protocols in CBCT is briefly discussed. Finally, basic and advanced visualization methods are illustrated. Certain topics in these review are applicable to all types of radiographic imaging (e.g. the principle and properties of an X-ray tube), others are specific for dental CBCT imaging (e.g. advanced visualization techniques).

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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.

Due to accurate 3D information, computed tomography (CT), especially cone-beam CT or dental CT, has been widely used for diagnosis and treatment planning in dentistry. Axial images acquired from both medical and dental CT scanners can generate synthetic panoramic images similar to typical 2D panoramic radiographs. However, the conventional way to reconstruct the simulated panoramic images is to manually draw the dental arch on axial images. In this paper, we propose a new fast algorithm for automatic detection of the dental arch. Once the dental arch is computed, a series of synthetic panoramic images as well as a ray-sum panoramic image can be automatically generated. We have tested the proposed algorithm on 120 CT axial images and all of them can provide the decent estimate of the dental arch. The results show that our proposed algorithm can mostly detect the correct dental arch.

Soft tissue images from portable cone beam computed tomography (CBCT) scanners can be used for diagnosis and detection of tumor, cancer, intracerebral hemorrhage, and so forth. Due to large field of view, X-ray scattering which is the main cause of artifacts degrades image quality, such as cupping artifacts, CT number inaccuracy, and low contrast, especially on soft tissue images. In this work, we propose the X-ray scatter correction method for improving soft tissue images. The X-ray scatter correction scheme to estimate X-ray scatter signals is based on the deconvolution technique using the maximum likelihood estimation maximization (MLEM) method. The scatter kernels are obtained by simulating the PMMA sheet on the Monte Carlo simulation (MCS) software. In the experiment, we used the QRM phantom to quantitatively compare with fan-beam CT (FBCT) data in terms of CT number values, contrast to noise ratio, cupping artifacts, and low contrast detectability. Moreover, the PH3 angiography phantom was also used to mimic human soft tissues in the brain. The reconstructed images with our proposed scatter correction show significant improvement on image quality. Thus the proposed scatter correction technique has high potential to detect soft tissues in the brain.