Iterative Ring Artifact Removal Method for Helical Computed Tomography Scans.

OBJECTIVE: In this article, a statistical-based iterative ring removal (IRR) algorithm that effectively removes ring artifacts generated by defective detector cells is proposed. METHODS: The physical state of computed tomography (CT) detector elements can change dynamically owing to their temperature dependence and the varying irradiation caused by focal spot movements. This variation in the properties of cells may cause false pixel values in sinograms, resulting in rings or segments of rings in reconstructed images. In this article, the proposed algorithm is studied on clinical CT. Two patients were scanned using a clinical CT scanner (AnyScan SPECT/CT, Mediso). Artificial rings and band rings were generated on the real sinogram data to examine the algorithm in different cases. The method was performed also on real ring artifacts. RESULTS: The IRR can correct both single and band-like ring artifacts with one or more defective pixels. The proposed algorithm can detect the period when pixels contain false signals and only those periods are corrected. The IRR reduces ring artifacts, even in cases where low-contrast rings occur in the reconstructed image. CONCLUSIONS: This statistical correction method efficiently detects and corrects false pixel values in the projection data without causing new artifacts in the reconstructed image. The algorithm is less sensitive to its parameters.

Comparison of iterative reconstruction implementations for multislice helical CT.

The most mature image reconstruction algorithms in multislice helical computed tomography are based on analytical and iterative methods. Over the past decades, several methods have been developed for iterative reconstructions that improve image quality by reducing noise and artifacts. In the regularization step of iterative reconstruction, noise can be significantly reduced, thereby making low-dose CT. The quality of the reconstructed image can be further improved by using model-based reconstructions. In these reconstructions, the main focus is on modeling the data acquisition process, including the behavior of the photon beams, the geometry of the system, etc. In this article, we propose two model-based reconstruction algorithms using a virtual detector for multislice helical CT. The aim of this study is to compare the effect of using a virtual detector on image quality for the two proposed algorithms with a model-based iterative reconstruction using the original detector model. Since the algorithms are implemented using multiple GPUs, the merging of separately reconstructed volumes can significantly affect image quality. This issue is often referred to as the "long object" problem, for which we also present a solution that plays an important role in the proposed reconstruction processes. The algorithms were evaluated using mathematical and physical phantoms, as well as patient cases. The SSIM, MS-SSIM and L1 metrics were utilized to evaluate the image quality of the mathematical phantom case. To demonstrate the effectiveness of the algorithms, we used the CatPhan 600 phantom. Additionally, anonymized patient scans were used to showcase the improvements in image quality on real scan data.

Comparison of CT noise reduction performances with deep learning-based, conventional, and combined denoising algorithms.

Conventional noise reduction algorithms have been used in image processing for a very long time, but recently, deep learning-based algorithms have been shown to significantly reduce the noise in CT images. In this paper, a comparison of CT noise reduction of a deep learning-based, a conventional, and their combined denoising algorithms is presented. A conventional adaptive 3D bilateral filter and a 2D deep learning-based noise reduction algorithm and a combination of these are compared. For comparison, we used the noise power spectrum and the task transfer function which were measured on original CT images and the effective dose saving factors were also calculated. The noise reduction effect, the noise power spectrum and the task-transfer function are studied using Catphan 600 phantom and 26 clinical cases with more than 100,000 images. We also show that the effect of noise reduction of a 2D deep learning-based algorithm can be further enhanced by using conventional 3D spatial noise reduction algorithms.