Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI.
Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI.
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DOI：
10.1109/tmi.2021.3134496
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发表时间：
2022-05
影响因子：
10.6
通讯作者：
中科院分区：
文献类型：
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作者：
磁化率引起的失真是影响弥散MRI（dMRI）数据分析的主要伪影。在人类神经连接组计划（HCP）中，用于校正这种失真的最先进的方法是利用来自反相编码图像中的B 0图像的位移场。然而，传统的和基于学习的方法在实现某些脑区域（例如脑干）的高校正精度方面都有局限性。通过利用从dMRI计算的纤维方向分布（FOD），我们提出了一种新的深度学习框架，称为DistoRtion Correction Net（DrC-Net），该框架由用于从4D FOD图像捕获潜在信息的U-Net和用于传播位移场并反向传播变形FOD图像之间的损失的空间Transformer网络组成。实验是在两个数据集上进行的，这两个数据集是用不同的相位编码（PE）方向采集的，包括HCP和人类连接组低视力（HCLV）数据集。与两种传统方法topup和FODReg以及两种深度学习方法S-Net和flow-net相比，该方法在分数各向异性（FA）图像的均方差（MSD）以及白色物质和脑干区域中两个PE之间的最小角度差方面实现了显著改善。同时，提出的DrC-Net只需几秒钟就可以预测位移场，比FODReg方法快得多。
Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement field from the B0 image in the reversed phase encoding images. However, both the traditional and learning-based approaches have limitations in achieving high correction accuracy in certain brain regions, such as brainstem. By utilizing the fiber orientation distribution (FOD) computed from the dMRI, we propose a novel deep learning framework named DistoRtion Correction Net (DrC-Net), which consists of the U-Net to capture the latent information from the 4D FOD images and the spatial transformer network to propagate the displacement field and back propagate the losses between the deformed FOD images. The experiments are performed on two datasets acquired with different phase encoding (PE) directions including the HCP and the Human Connectome Low Vision (HCLV) dataset. Compared to two traditional methods topup and FODReg and two deep learning methods S–Net and flow–net, the proposed method achieves significant improvements in terms of the mean squared difference (MSD) of fractional anisotropy (FA) images and minimum angular difference between two PEs in white matter and also brainstem regions. In the meantime, the proposed DrC-Net takes only several seconds to predict a displacement field, which is much faster than the FODReg method.
DOI：
10.1007/978-3-030-59728-3_30
发表时间：
2020
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
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作者：
Qiao Y;Shi Y
通讯作者：
Shi Y