Hashemi SayedMasoud, Paul Narinder S, Beheshti Soosan, Cobbold Richard S C
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada M5S 3G9.
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada M5S 3G9 ; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, Toronto, ON, Canada M5G 2N2.
Comput Math Methods Med. 2015;2015:638568. doi: 10.1155/2015/638568. Epub 2015 May 24.
Improving image quality is a critical objective in low dose computed tomography (CT) imaging and is the primary focus of CT image denoising. State-of-the-art CT denoising algorithms are mainly based on iterative minimization of an objective function, in which the performance is controlled by regularization parameters. To achieve the best results, these should be chosen carefully. However, the parameter selection is typically performed in an ad hoc manner, which can cause the algorithms to converge slowly or become trapped in a local minimum. To overcome these issues a noise confidence region evaluation (NCRE) method is used, which evaluates the denoising residuals iteratively and compares their statistics with those produced by additive noise. It then updates the parameters at the end of each iteration to achieve a better match to the noise statistics. By combining NCRE with the fundamentals of block matching and 3D filtering (BM3D) approach, a new iterative CT image denoising method is proposed. It is shown that this new denoising method improves the BM3D performance in terms of both the mean square error and a structural similarity index. Moreover, simulations and patient results show that this method preserves the clinically important details of low dose CT images together with a substantial noise reduction.
提高图像质量是低剂量计算机断层扫描（CT）成像的关键目标，也是CT图像去噪的主要关注点。当前最先进的CT去噪算法主要基于目标函数的迭代最小化，其性能由正则化参数控制。为了获得最佳结果，需要谨慎选择这些参数。然而，参数选择通常是临时进行的，这可能导致算法收敛缓慢或陷入局部最小值。为了克服这些问题，使用了一种噪声置信区域评估（NCRE）方法，该方法迭代评估去噪残差，并将其统计数据与加性噪声产生的统计数据进行比较。然后在每次迭代结束时更新参数，以更好地匹配噪声统计数据。通过将NCRE与块匹配和三维滤波（BM3D）方法的基本原理相结合，提出了一种新的迭代CT图像去噪方法。结果表明，这种新的去噪方法在均方误差和结构相似性指数方面都提高了BM3D的性能。此外，模拟和患者结果表明，该方法在大幅降低噪声的同时，保留了低剂量CT图像的临床重要细节。
