基于深度学习的CT图像汉字域金属伪影校正

Sinogram domain metal artifact correction of CT via deep learning.

作者信息

Zhu Yulin, Zhao Hanqing, Wang Tangsheng, Deng Lei, Yang Yupeng, Jiang Yuming, Li Na, Chan Yinping, Dai Jingjing, Zhang Chulong, Li Yunhui, Xie Yaoqin, Liang Xiaokun

机构信息

The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523808, China.

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

出版信息

Comput Biol Med. 2023 Mar;155:106710. doi: 10.1016/j.compbiomed.2023.106710. Epub 2023 Feb 20.

DOI:10.1016/j.compbiomed.2023.106710

PMID:36842222

Abstract

PURPOSE

Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning. Beam hardening artifacts are often manifested as severe strip artifacts in the image domain, affecting the overall quality of the reconstructed CT image. In the sinogram domain, metal is typically located in specific areas, and image processing in these regions can preserve image information in other areas, making the model more robust. To address this issue, we propose a region-based correction of beam hardening artifacts in the sinogram domain using deep learning.

METHODS

We present a model composed of three modules: (a) a Sinogram Metal Segmentation Network (Seg-Net), (b) a Sinogram Enhancement Network (Sino-Net), and (c) a Fusion Module. The model starts by using the Attention U-Net network to segment the metal regions in the sinogram. The segmented metal regions are then interpolated to obtain a sinogram image free of metal. The Sino-Net is then applied to compensate for the loss of organizational and artifact information in the metal regions. The corrected metal sinogram and the interpolated metal-free sinogram are then used to reconstruct the metal CT and metal-free CT images, respectively. Finally, the Fusion Module combines the two CT images to produce the result.

RESULTS

Our proposed method shows strong performance in both qualitative and quantitative evaluations. The peak signal-to-noise ratio (PSNR) of the CT image before and after correction was 18.22 and 30.32, respectively. The structural similarity index measure (SSIM) improved from 0.75 to 0.99, and the weighted peak signal-to-noise ratio (WPSNR) increased from 21.69 to 35.68.

CONCLUSIONS

Our proposed method demonstrates the reliability of high-accuracy correction of beam hardening artifacts.

摘要

目的

金属伪影会显著降低计算机断层扫描（CT）图像的质量。当X射线穿透植入的金属时就会出现这种情况，导致严重衰减并在CT图像中产生金属伪影。图像质量的这种下降会妨碍后续的临床诊断和治疗计划。束硬化伪影在图像域中通常表现为严重的条纹伪影，影响重建CT图像的整体质量。在正弦图域中，金属通常位于特定区域，对这些区域进行图像处理可以保留其他区域的图像信息，使模型更加强健。为了解决这个问题，我们提出了一种基于区域的深度学习方法，用于在正弦图域中校正束硬化伪影。

方法

我们提出了一个由三个模块组成的模型：（a）正弦图金属分割网络（Seg-Net），（b）正弦图增强网络（Sino-Net），以及（c）融合模块。该模型首先使用注意力U-Net网络对正弦图中的金属区域进行分割。然后对分割出的金属区域进行插值，以获得不含金属的正弦图图像。接着应用Sino-Net来补偿金属区域中组织和伪影信息的损失。然后分别使用校正后的金属正弦图和插值后的无金属正弦图来重建金属CT图像和无金属CT图像。最后，融合模块将这两幅CT图像合并以产生结果。