Gu Hongyi, Zhang Chi, Yu Zidan, Rettenmeier Christoph, Stenger V Andrew, Akçakaya Mehmet
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.
Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA.
Proc IEEE Int Symp Biomed Imaging. 2024 May;2024. doi: 10.1109/isbi56570.2024.10635551. Epub 2024 Aug 22.
Functional MRI (fMRI) is an important tool for non-invasive studies of brain function. Over the past decade, multi-echo fMRI methods that sample multiple echo times has become popular with potential to improve quantification. While these acquisitions are typically performed with Cartesian trajectories, non-Cartesian trajectories, in particular spiral acquisitions, hold promise for denser sampling of echo times. However, such acquisitions require very high acceleration rates for sufficient spatiotemporal resolutions. In this work, we propose to use a physics-driven deep learning (PD-DL) reconstruction to accelerate multi-echo spiral fMRI by 10-fold. We modify a self-supervised learning algorithm for optimized training with non-Cartesian trajectories and use it to train the PD-DL network. Results show that the proposed self-supervised PD-DL reconstruction achieves high spatio-temporal resolution with meaningful BOLD analysis.
功能磁共振成像（fMRI）是用于脑功能无创研究的重要工具。在过去十年中，对多个回波时间进行采样的多回波fMRI方法已变得流行起来，具有改善量化的潜力。虽然这些采集通常采用笛卡尔轨迹进行，但非笛卡尔轨迹，特别是螺旋采集，有望对回波时间进行更密集的采样。然而，这种采集需要非常高的加速率才能获得足够的时空分辨率。在这项工作中，我们建议使用物理驱动的深度学习（PD-DL）重建将多回波螺旋fMRI加速10倍。我们修改了一种自监督学习算法，以便用非笛卡尔轨迹进行优化训练，并使用它来训练PD-DL网络。结果表明，所提出的自监督PD-DL重建在有意义的血氧水平依赖（BOLD）分析中实现了高时空分辨率。
