Training a neural network for Gibbs and noise removal in diffusion MRI.
Training a neural network for Gibbs and noise removal in diffusion MRI.
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DOI：
10.1002/mrm.28395
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发表时间：
2021-01
影响因子：
3.3
通讯作者：
Knoll F
中科院分区：
文献类型：
--
作者：
Muckley MJ;Ades-Aron B;Papaioannou A;Lemberskiy G;Solomon E;Lui YW;Sodickson DK;Fieremans E;Novikov DS;Knoll F
开发和评估基于神经网络的吉布斯伪影和噪声消除方法。卷积神经网络 (CNN) 旨在消除扩散加权成像数据中的伪影。考虑了两种实现方式：一种用于幅度图像，一种用于复杂图像。两种模型都基于相同的编码器-解码器结构，并通过在合成的非 MRI 图像上模拟 MRI 采集来进行训练。两种机器学习方法都能够减轻扩散加权图像和扩散参数图中的伪影。用于复杂图像的 CNN 还能够减少部分傅立叶采集中的伪影。所提出的 CNN 扩展了扩散 MRI 中伪影校正的能力。这里描述的机器学习方法可以独立应用于每个成像切片，使其能够在临床应用中灵活使用。
To develop and evaluate a neural network–based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
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影响因子：
3.3
作者：
Chaudhari AS;Fang Z;Kogan F;Wood J;Stevens KJ;Gibbons EK;Lee JH;Gold GE;Hargreaves BA
通讯作者：
Hargreaves BA
影响因子：
3.3
作者：
Hammernik K;Klatzer T;Kobler E;Recht MP;Sodickson DK;Pock T;Knoll F
通讯作者：
Knoll F
影响因子：
3.3
作者：
Kellner, Elias;Dhital, Bibek;Reisert, Marco
通讯作者：
Reisert, Marco
影响因子：
3.3
作者：
Knoll F;Hammernik K;Kobler E;Pock T;Recht MP;Sodickson DK
通讯作者：
Sodickson DK
影响因子：
4.6
作者：
Deniz CM;Xiang S;Hallyburton RS;Welbeck A;Babb JS;Honig S;Cho K;Chang G
通讯作者：
Chang G