影像科学与光化学2024，Vol.42Issue(4)：385-391,7.DOI:10.7517/issn.1674-0475.2024.04.13
深度学习重建算法在肝脏肿瘤患者CT检查中的应用进展
Advancements and Applications of Deep Learning Image Reconstruction Algorithm in Computed Tomography Examinations for Patients with Liver Tumors
冯俏 1全江海 1程留慧2
作者信息
- 1. 河南中医药大学,河南郑州 450000
- 2. 河南中医药大学第一附属医院,河南郑州 450000
- 折叠
摘要
Abstract
The occurrence of liver tumors is prevalent in clinical practice,particularly among patients diagnosed with liver cancer.It represents one of the most frequently encountered malignant neoplasms within the digestive system,posing a significant threat to individuals'physical and psychological well-being.The deep learning reconstruction algorithm based on convolutional neural network is an emerging technology in computed tomography reconstruction algorithms,which is gradually maturing and has been applied in both phantom studies and clinical practice.In comparison to the filtered back projection and iterative reconstruction algorithm,the deep learning image reconstruction algorithm offers some advantages,such as reduced radiation dose,effective noise reduction,optimized display of fine structures and improved subjective diagnostic confidence.Radiomics can extract high-dimensional imaging features with high throughput,and the application of deep learning image reconstruction algorithm can enhance the reliability of radiomics features in clinical setting.This article provides a review of related research on deep learning image reconstruction algorithm in liver tumors,aiming to deepen our understanding of its clinical progress and accumulate evidence for future studies.关键词
深度学习/肝肿瘤/重建算法/X线计算机体层摄影Key words
deep learning/liver tumors/reconstruction algorithm/X-ray computed tomography分类
医药卫生引用本文复制引用
冯俏,全江海,程留慧..深度学习重建算法在肝脏肿瘤患者CT检查中的应用进展[J].影像科学与光化学,2024,42(4):385-391,7.
