Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver.
Cascade of Denoising and Mapping Neural Networks for MRI R2* Relaxometry of Iron-Loaded Liver.
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DOI：
10.3390/bioengineering10020209
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发表时间：
2023-02-04
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影响因子：
--
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中科院分区：
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MRI测量有效横向弛豫速率(R2*)是一种可靠的肝铁浓度定量方法。然而，R2*映射可能会受到噪声的影响，特别是在铁超载的情况下。本研究旨在开发一种基于两级级联神经网络的铁负荷肝脏磁共振R2*弛豫测量的深度学习方法。该方法将两个分别用于图像去噪和参数映射的卷积神经网络组合到一个级联框架中，并将基于物理的R2*衰变模型引入到映射网络的训练中，以进一步加强数据的一致性。CadamNet使用带有莱斯噪声的模拟肝脏数据进行训练，该数据是从临床肝脏数据构建的。CadamNet的性能在不同噪声水平的模拟数据和临床肝脏数据上进行了定量评估，并与单级参数映射网络(MappingNet)和两种传统的基于模型的R2*映射方法进行了比较。CadamNet始终获得高质量的R2*地图，并在不同的噪声水平下表现优于MappingNet。与传统的R2*映射方法相比，CadamNet生成的R2*映射误差更小，质量更高，效率也大大提高。总之，建议的CadamNet能够准确和高效地定位铁负荷的肝脏R2*，特别是在存在严重噪声的情况下。
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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影响因子：
4.4
作者：
Kirk, Paul;He, Taigang;Anderson, Lisa J.;Roughton, Michael;Tanner, Mark A.;Lam, Wynnie W. M.;Au, Wing Y.;Chu, Winnie C. W.;Chan, Godfrey;Galanello, Renzo;Matta, Gildo;Fogel, Mark;Cohen, Alan R.;Tan, Ru San;Chen, Kevin;Ng, Ivy;Lai, Angie;Fucharoen, Suthat;Laothamata, Jiraporn;Chuncharunee, Suporn;Jongjirasiri, Sutipong;Firmin, David N.;Smith, Gillian C.;Pennell, Dudley J.
通讯作者：
Pennell, Dudley J.
DOI：
10.1073/pnas.1907377117
发表时间：
2020-12-01
影响因子：
11.1
作者：
Antun, Vegard;Renna, Francesco;Hansen, Anders C.
通讯作者：
Hansen, Anders C.
影响因子：
5.5
作者：
Labranche, Roxanne;Gilbert, Guillaume;Tang, An
通讯作者：
Tang, An
影响因子：
5.9
作者：
Henninger, Benjamin;Alustiza, Jose;Gandon, Yves
通讯作者：
Gandon, Yves
影响因子：
9.8
作者：
Regev, A;Berho, M;Schiff, ER
通讯作者：
Schiff, ER