Abstract
Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.
Abstract (translated)
低场磁共振成像（MRI）提供了经济实惠的诊断影像访问途径，但存在采集时间长和图像质量有限的问题。通过k空间欠采样可以实现加速成像，而超分辨率（SR）和图像质量转换（IQT）方法通常依赖于空间域后的处理步骤。在本研究中，我们提出了一种新的框架，直接从欠采样的低场k空间重构高场MR类似的图像。我们的方法采用了一个k空间双通道U-Net架构，该架构能够处理欠采样k空间的实部和虚部组件，以恢复缺失的频率内容。 实验结果表明，在低场脑MRI数据上的应用中，我们提出的基于k空间的图像增强方法在性能上显著优于传统的空间域方法。此外，从欠采样的k空间重构出的图像质量可以与完全采样的k空间采集得到的质量相媲美。据我们所知，这是第一个直接从欠采样k空间研究低场MRI超分辨率和图像质量转换的工作。
URL
https://arxiv.org/abs/2603.00668
