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A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

机译：基于稀疏表示和分层贝叶斯-MAP的CT图像盲恢复与重建新方法

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摘要

Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).
机译：计算机断层扫描（CT）图像的重建和恢复在医学图像处理中非常重要，并且被关联在一起成为一个反问题。图像迭代重建是增加CT成像的适用性并减少辐射剂量的关键工具。然而，传统的图像迭代重建方法受到采样定理的限制，并且投影数据的模糊也会在重建的图像中传播不受阻碍的伪像。为了克服这些问题，应该开发图像恢复技术以准确地校正各种图像降级效果，以便有效地改善图像重建。本文将盲图像恢复技术嵌入到压缩感测CT图像重建中，可以使用较少的投影数据生成高质量的重建图像。由于可以在更短的时间内通过辐射获得少量数据，因此使用较少数据进行高质量的图像重建等效于减少辐射剂量。从技术上讲，清晰的CT图像的模糊过程和稀疏表示都首先被建模为一系列参数。清晰的CT图像将从估计的稀疏表示中获得。然后，通过分层贝叶斯最大后验公式估计模型参数。最后，优化估计的模型参数以获得最终的图像重建。我们在峰值信噪比（PSNR）和结构相似性指标（SSIM）方面通过仿真实验证明了该方法的有效性。

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