CT reconstruction from metal-embedded data usually produces streak artifacts that reduce the quality of the reconstructed images. In this paper, we propose a new technique for metal artifact reduction in cone-beam CT based on statistical reconstruction. First, the metal objects are segmented in the reconstructed images and then reprojected to obtain the measurement data of the metal objects using cone-beam reconstruction. The original measurement data in the metal area are corrected through cubic interpolation. y, the corrected measurement data are reconstructed with the penalized likelihood using the modified convex algorithm. The simulation results show that the reconstructed images of the metal object using the proposed metal artifact reduction technique are superior to conventional filtered backprojection reconstruction.
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.
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.
Cone-beam computed tomography (CBCT) has become increasingly popular in dental and maxillofacial imaging due to its accurate 3D information, minimal radiation dose, and low machine cost. In this paper, we have proposed the newly developed CBCT scanner, called DentiiScan. Our gantry system consisting of a cone-beam X-ray source and an amorphous silicon flat panel detector is rotated around a patient's head. With the large area detector, only a single rotation is needed to reconstruct the field-of-view area from chin to eyes and our reconstructed algorithm based on GPU calculation is about 30 times faster than the CPU-based algorithm. The radiation dose was measured and compared to other dental and medical CT machines. The absorbed radiation dose from our proposed CBCT machine is significantly low. In addition, geometric accuracy was analyzed when the test object was scanned at the normal position as well as the inclined position. The results from three observers repeated for five times confirm that the machine can produce reconstructed images with high accuracy.
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