Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK; Department of Forensic & Neurodevelopmental Sciences, King's College London, London, UK.
Centre for the Developing Brain, King's College London, London, UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
Med Image Anal. 2021 Dec;74:102255. doi: 10.1016/j.media.2021.102255. Epub 2021 Sep 25.
MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces inter-slice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project.
MRI 扫描仪和序列不完善，以及重建和成像技术的进步，以提高运动鲁棒性，可能导致 EPI 中的切片间强度变化。利用深度卷积神经网络作为通用图像滤波器，我们提出了一种数据驱动的方法，用于校正采集伪影，这些伪影表现为切片间不一致，而不管其来源如何。通过将该技术嵌入到重建流水线中，可以将其应用于运动伪影和丢失数据。该网络在没有真实数据的情况下进行训练，并最终应用于重建的多壳高角分辨率扩散成像信号，以产生校正的切片强度调制场。可以在运动校正或散射源空间中执行此校正。我们专注于通过内置的空间频率和强度约束来控制学习滤波器和图像数据的一致性。最终产品是从原始原始数据重建的校正图像，由乘法场调制，可以检查和验证以匹配伪影的预期特征。在平面内，校正大致保留了扩散信号的对比度，并且在整个图像系列中，它减少了切片内和切片间的不一致性，而不会对数据产生偏差。我们将我们的管道应用于增强在发展中的人类连接组计划中采集的新生儿多壳高角分辨率数据的超分辨率重建。
