通过具有频谱损失的级联ResUnet进行低剂量CT成像。

Low-dose CT imaging via cascaded ResUnet with spectrum loss.

作者信息

Liu Jin, Kang Yanqin, Qiang Jun, Wang Yong, Hu Dianlin, Chen Yang

机构信息

College of Computer and Information, Anhui Polytechnic University, Wuhu, China; Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing, China.

出版信息

Methods. 2022 Jun;202:78-87. doi: 10.1016/j.ymeth.2021.05.005. Epub 2021 May 14.

DOI:10.1016/j.ymeth.2021.05.005

PMID:33992773

Abstract

The suppression of artifact noise in computed tomography (CT) with a low-dose scan protocol is challenging. Conventional statistical iterative algorithms can improve reconstruction but cannot substantially eliminate large streaks and strong noise elements. In this paper, we present a 3D cascaded ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for reducing artifact noise in low-dose CT imaging. The imaging workflow consists of four components. The first component is filtered backprojection (FBP) reconstruction via a domain transformation module for suppressing artifact noise. The second is a ResUnet neural network that operates on the CT image. The third is an image compensation module that compensates for the loss of tiny structures, and the last is a second ResUnet neural network with modified spectrum loss for fine-tuning the reconstructed image. Verification results based on American Association of Physicists in Medicine (AAPM) and United Image Healthcare (UIH) datasets confirm that the proposed strategy significantly reduces serious artifact noise while retaining desired structures.

摘要

在低剂量扫描协议的计算机断层扫描（CT）中抑制伪影噪声具有挑战性。传统的统计迭代算法可以改善重建效果，但不能大幅消除大的条纹和强噪声元素。在本文中，我们提出了一种具有改进噪声功率谱损失的三维级联ResUnet神经网络（Ca-ResUnet）策略，用于减少低剂量CT成像中的伪影噪声。成像工作流程由四个部分组成。第一部分是通过域变换模块进行滤波反投影（FBP）重建，以抑制伪影噪声。第二部分是在CT图像上运行的ResUnet神经网络。第三部分是一个图像补偿模块，用于补偿微小结构的损失，最后一部分是具有改进频谱损失的第二个ResUnet神经网络，用于对重建图像进行微调。基于美国医学物理学会（AAPM）和联影医疗（UIH）数据集的验证结果证实，所提出的策略在保留所需结构的同时，显著降低了严重的伪影噪声。