20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction.
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction.
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DOI：
10.1109/embc46164.2021.9631107
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Akcakaya M
中科院分区：
文献类型：
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作者：
Demirel OB;Yaman B;Dowdle L;Moeller S;Vizioli L;Yacoub E;Strupp J;Olman CA;Ugurbil K;Akcakaya M
整个大脑的高空间和时间分辨率是fMRI准确分辨神经活动的关键。因此，加速成像技术以具有高时空分辨率的改进的覆盖为目标。同步多切片（SMS）成像结合平面内加速度用于涉及双磁场fMRI的大型研究，例如人类连接组项目。然而，对于甚至更高的加速速率，由于混叠和噪声伪影，这些方法不能被可靠地利用。深度学习（DL）重建技术最近在改善高加速MRI方面获得了极大的兴趣。DL重建的监督学习通常需要完全采样的训练数据集，这对于高分辨率fMRI研究是不可用的。为了应对这一挑战，已经提出了自监督学习来训练仅使用欠采样数据集的DL重建，表现出与监督学习相似的性能。在这项研究中，我们利用一个自我监督的物理引导DL重建的5倍SMS和4倍面内加速7T的fMRI数据。我们的研究结果表明，我们的自监督DL重建在20倍加速下产生高质量的图像，大大改善了现有方法，同时与标准的10倍加速采集相比，在后续分析中显示出类似的功能精度和时间效应。
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slice (SMS) imaging combined with in-plane acceleration are used in large studies that involve ultrahigh field fMRI, such as the Human Connectome Project. However, for even higher acceleration rates, these methods cannot be reliably utilized due to aliasing and noise artifacts. Deep learning (DL) reconstruction techniques have recently gained substantial interest for improving highly-accelerated MRI. Supervised learning of DL reconstructions generally requires fully-sampled training datasets, which is not available for high-resolution fMRI studies. To tackle this challenge, self-supervised learning has been proposed for training of DL reconstruction with only undersampled datasets, showing similar performance to supervised learning. In this study, we utilize a self-supervised physics-guided DL reconstruction on a 5-fold SMS and 4-fold in-plane accelerated 7T fMRI data. Our results show that our self-supervised DL reconstruction produce high-quality images at this 20-fold acceleration, substantially improving on existing methods, while showing similar functional precision and temporal effects in the subsequent analysis compared to a standard 10-fold accelerated acquisition.