用预优化掩码训练网络，让MRI采样更高效精准
Learning Scan-Adaptive MRI Undersampling Patterns with Pre-Optimized Mask Supervision
- 用预计算的最优掩码作为监督信号，直接学习扫描自适应采样模式
- 在fastMRI膝关节数据集上，采样效率与重建质量显著提升
- 适合需要快速高质量MRI成像的研究者和临床医生
深度学习在加速MRI数据采集并保持图像质量方面受到广泛关注。本文提出一种基于卷积神经网络（CNN）的框架，直接从多线圈MRI数据中学习欠采样模式。与以往依赖训练中掩码优化的方法不同，本方法使用预先计算的扫描自适应优化掩码作为监督标签，实现高效且鲁棒的扫描特异性采样。训练过程交替优化重建器与数据驱动的采样网络，后者从观测到的低频k空间数据生成扫描特定的采样模式。在fastMRI多线圈膝关节数据集上的实验表明，该方法在采样效率和图像重建质量方面均有显著提升，为通过深度学习增强MRI采集提供了稳健框架。
原文摘要 · Abstract (English)
Deep learning techniques have gained considerable attention for their ability to accelerate MRI data acquisition while maintaining scan quality. In this work, we present a convolutional neural network (CNN) based framework for learning undersampling patterns directly from multi-coil MRI data. Unlike prior approaches that rely on in-training mask optimization, our method is trained with precomputed scan-adaptive optimized masks as supervised labels, enabling efficient and robust scan-specific sampling. The training procedure alternates between optimizing a reconstructor and a data-driven sampling network, which generates scan-specific sampling patterns from observed low-frequency $k$-space data. Experiments on the fastMRI multi-coil knee dataset demonstrate significant improvements in sampling efficiency and image reconstruction quality, providing a robust framework for enhancing MRI acquisition through deep learning.
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