降噪
级联
噪音(视频)
人工神经网络
计算机科学
人工智能
卷积神经网络
模式识别(心理学)
松弛法
一致性(知识库)
磁共振成像
图像(数学)
化学
放射科
医学
色谱法
自旋回波
作者
Qiqi Lu,Changqing Wang,Zifeng Lian,Mengjie Zhang,Wei Yang,Qianjin Feng,Yanqiu Feng
出处
期刊:Bioengineering
[MDPI AG]
日期:2023-02-04
卷期号:10 (2): 209-209
被引量:2
标识
DOI:10.3390/bioengineering10020209
摘要
MRI of effective transverse relaxation rate (R2*) measurement is a reliable method for liver iron concentration quantification. However, R2* mapping can be degraded by noise, especially in the case of iron overload. This study aimed to develop a deep learning method for MRI R2* relaxometry of an iron-loaded liver using a two-stage cascaded neural network. The proposed method, named CadamNet, combines two convolutional neural networks separately designed for image denoising and parameter mapping into a cascade framework, and the physics-based R2* decay model was incorporated in training the mapping network to enforce data consistency further. CadamNet was trained using simulated liver data with Rician noise, which was constructed from clinical liver data. The performance of CadamNet was quantitatively evaluated on simulated data with varying noise levels as well as clinical liver data and compared with the single-stage parameter mapping network (MappingNet) and two conventional model-based R2* mapping methods. CadamNet consistently achieved high-quality R2* maps and outperformed MappingNet at varying noise levels. Compared with conventional R2* mapping methods, CadamNet yielded R2* maps with lower errors, higher quality, and substantially increased efficiency. In conclusion, the proposed CadamNet enables accurate and efficient iron-loaded liver R2* mapping, especially in the presence of severe noise.
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