Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI.
Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI.
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DOI：
10.1007/978-3-030-87602-9_8
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发表时间：
2021-10
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MRI序列期间的受试者运动可能会导致相位编码方向上的重影效应或扩散图像噪声，因此可能会使神经成像研究中的结果产生偏差。检测运动伪影通常依赖于专家视觉检查MRI，这是主观的且昂贵的。为了提高这种检测，我们开发了一个框架，自动量化的严重程度的运动伪影在大脑MRI。我们将此任务制定为回归问题，并从具有各种运动伪影的MRI数据集训练回归量。为了解决丢失细粒度地面实况标签（伪影水平）的问题，我们提出了对抗贝叶斯优化（ABO）来推断运动参数的分布（即，旋转和平移），然后将从该估计分布采样的合成运动伪影注入到无运动MRI中。在对合成数据进行回归训练后，我们应用该模型量化了由国家青少年酒精和神经发育联盟收集的990个MRI中的运动水平。实验结果表明，该方法比传统的基于熵聚焦准则和人工定义的二进制标签的运动水平更可靠。
Subject motion during an MRI sequence can cause ghosting effects or diffuse image noise in the phase-encoding direction and hence is likely to bias findings in neuroimaging studies. Detecting motion artifacts often relies on experts visually inspecting MRIs, which is subjective and expensive. To improve this detection, we develop a framework to automatically quantify the severity of motion artifact within a brain MRI. We formulate this task as a regression problem and train the regressor from a data set of MRIs with various amounts of motion artifacts. To resolve the issue of missing fine-grained ground-truth labels (level of artifacts), we propose Adversarial Bayesian Optimization (ABO) to infer the distribution of motion parameters (i.e., rotation and translation) underlying the acquired MRI data and then inject synthetic motion artifacts sampled from that estimated distribution into motion-free MRIs. After training the regressor on the synthetic data, we applied the model to quantify the motion level in 990 MRIs collected by the National Consortium on Alcohol and Neurodevelopment in Adolescence. Results show that the motion level derived by our approach is more reliable than the traditional metric based on Entropy Focus Criterion and manually defined binary labels.