IEEE Trans Med Imaging. 2022 Jul;41(7):1813-1825. doi: 10.1109/TMI.2022.3148728. Epub 2022 Jun 30.
Functional ultrasound (fUS) is a rapidly emerging modality that enables whole-brain imaging of neural activity in awake and mobile rodents. To achieve sufficient blood flow sensitivity in the brain microvasculature, fUS relies on long ultrasound data acquisitions at high frame rates, posing high demands on the sampling and processing hardware. Here we develop an image reconstruction method based on deep learning that significantly reduces the amount of data necessary while retaining imaging performance. We trained convolutional neural networks to learn the power Doppler reconstruction function from sparse sequences of ultrasound data with compression factors of up to 95%. High-quality images from in vivo acquisitions in rats were used for training and performance evaluation. We demonstrate that time series of power Doppler images can be reconstructed with sufficient accuracy to detect the small changes in cerebral blood volume (~10%) characteristic of task-evoked cortical activation, even though the network was not formally trained to reconstruct such image series. The proposed platform may facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or in clinical scanners.
功能超声（fUS）是一种快速发展的模态，能够在清醒和移动的啮齿动物中实现全脑神经活动成像。为了在脑微血管中获得足够的血流灵敏度，fUS 需要在高帧率下进行长时间的超声数据采集，这对采样和处理硬件提出了很高的要求。在这里，我们开发了一种基于深度学习的图像重建方法，可以在保持成像性能的同时显著减少所需的数据量。我们训练卷积神经网络，从具有高达 95%压缩因子的稀疏超声数据序列中学习功率多普勒重建函数。使用来自大鼠体内采集的高质量图像进行训练和性能评估。我们证明，即使网络没有经过正式训练来重建这种图像序列，也可以以足够的精度重建功率多普勒图像时间序列，以检测到与任务诱发皮层激活相关的脑血容量的微小变化（约 10%）。该平台可以促进这种神经影像学模式在任何没有专用硬件的环境中的发展，或者在临床扫描仪中。
