论文标题
使用插件Denoiser的无校准MRI重建
Calibrationless MRI Reconstruction with a Plug-in Denoiser
论文作者
论文摘要
磁共振成像（MRI）是一种无创成像技术，在不使用电离辐射的情况下提供出色的软组织对比度。 MRI的临床应用可能会受到长期数据获取时间的限制；因此，来自高度采样的K空间数据的MR图像重建一直是一个活跃的研究领域。无校准的MRI不仅可以提高加速度，而且还可以提高采样模式设计的灵活性。为了利用非线性机器学习先验，我们将高维快速卷积框架（HICU）与插件Denoiser配对，并使用2D大脑数据证明了其可行性。
Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI's clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (HICU) with a plug-in denoiser and demonstrate its feasibility using 2D brain data.
