论文标题
k空间变压器用于不足采样的MRI重建
K-Space Transformer for Undersampled MRI Reconstruction
论文作者
论文摘要
本文认为MRI重建不足的问题。我们提出了一个基于变压器的新型框架，用于直接在K空间中处理信号，超出了像Convnets一样的常规网格的限制。我们采用K空间频谱图的隐式表示，将空间坐标视为输入，并动态查询稀疏采样点以重建频谱图，即学习K空间中的电感偏置。为了在计算成本和重建质量之间取得平衡，我们用分层结构构建解码器，分别产生低分辨率和高分辨率输出。为了验证我们提出的方法的有效性，我们在两个公共数据集上进行了广泛的实验，并证明了与最先进的方法相当或可比的性能。
This paper considers the problem of undersampled MRI reconstruction. We propose a novel Transformer-based framework for directly processing signal in k-space, going beyond the limitation of regular grids as ConvNets do. We adopt an implicit representation of k-space spectrogram, treating spatial coordinates as inputs, and dynamically query the sparsely sampled points to reconstruct the spectrogram, i.e. learning the inductive bias in k-space. To strike a balance between computational cost and reconstruction quality, we build the decoder with hierarchical structure to generate low-resolution and high-resolution outputs respectively. To validate the effectiveness of our proposed method, we have conducted extensive experiments on two public datasets, and demonstrate superior or comparable performance to state-of-the-art approaches.
