Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning.
Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning.
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DOI：
10.3389/fnins.2021.653213
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发表时间：
2021
影响因子：
4.3
通讯作者：
Styner MA
中科院分区：
文献类型：
--
作者：
Peng L;Lin L;Lin Y;Chen YW;Mo Z;Vlasova RM;Kim SH;Evans AC;Dager SR;Estes AM;McKinstry RC;Botteron KN;Gerig G;Schultz RT;Hazlett HC;Piven J;Burrows CA;Grzadzinski RL;Girault JB;Shen MD;Styner MA
婴儿大脑经历了一个显著的神经发育时期，这对于认知和行为能力的发展至关重要（Hasegawa等人）。纵向磁共振成像（MRI）能够表征发育轨迹，在早期脑发育的神经影像学研究中至关重要。然而，在纵向研究中，由于受试者流失和扫描失败，不同时间点的数据缺失是不可避免的。与丢弃不完整数据相比，数据插补被认为是解决此类缺失数据的更好解决方案，以保留所有可用样本。在本文中，我们将生成对抗网络（GAN）应用于一个新的应用：生命第一年结构MRI的纵向图像预测。与GAN现有的医学图像到图像转换应用相比，输入和输出共享非常接近的解剖结构，我们的任务更具挑战性，因为输入数据和预测数据之间的大脑大小，形状和组织对比度变化很大。提出了对现有GAN方法的几项改进，以解决我们任务中的这些挑战。为了增强预测图像的真实性，清晰度和准确性，我们将传统的体素重建损失以及感知损失项纳入对抗学习方案。由于T1 w和T2 w MR图像在生命的第一年中的不同对比度变化，我们将多对比度图像引入我们提出的3D多对比度感知对抗网络（MPGAN）。进行广泛的评估，以评估预测图像的qualityand保真度，包括定性和定量评估的图像外观，以及定量评估的两个分割任务。我们的实验结果表明，我们的MPGAN是一个有效的解决方案，纵向MR图像数据填补在婴儿大脑。我们进一步将我们的预测/估算图像应用于两个实际任务，一个回归任务和一个分类任务，以突出图像估算后增强的任务相关性能。结果表明，在这两个任务中的模型性能得到改善，包括额外的插补数据，证明了从我们的方法生成的预测图像的可用性。
The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al.,). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.
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DOI：
10.1038/nrn.2018.1
发表时间：
2018-02-16
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Gilmore JH;Knickmeyer RC;Gao W
通讯作者：
Gao W
影响因子：
7.3
作者：
Emami, Hajar;Aliabadi, Majid Moradi;Chinnam, Ratna Babu
通讯作者：
Chinnam, Ratna Babu
影响因子：
4.3
作者：
Hasegawa C;Takahashi T;Yoshimura Y;Nobukawa S;Ikeda T;Saito DN;Kumazaki H;Minabe Y;Kikuchi M
通讯作者：
Kikuchi M
DOI：
10.1007/978-3-642-38868-2_60
发表时间：
2013
期刊：
Information processing in medical imaging : proceedings of the ... conference
影响因子：
--
作者：
Fishbaugh, James;Prastawa, Marcel;Gerig, Guido;Durrleman, Stanley
通讯作者：
Durrleman, Stanley
影响因子：
4.8
作者：
Meng Y;Li G;Rekik I;Zhang H;Gao Y;Lin W;Shen D
通讯作者：
Shen D