Multi-contrast magnetic resonance imaging is a significant and essential medical imaging technique.However, multi-contrast imaging has longer acquisition time and is easy to cause motion artifacts. In particular, the acquisition time for a T2-weighted image is prolonged due to its longer repetition time (TR). On the contrary, T1-weighted image has a shorter TR. Therefore,utilizing complementary information across T1 and T2-weighted image is a way to decrease the overall imaging time. Previous T1-assisted T2 reconstruction methods have mostly focused on image domain using whole-based image fusion approaches. The image domain reconstruction method has the defects of high computational complexity and limited flexibility. To address this issue, we propose a novel multi-contrast imaging method called partition-based k-space synthesis (PKS) which can achieve super reconstruction quality of T2-weighted image by feature fusion. Concretely, we first decompose fully-sampled T1 k-space data and under-sampled T2 k-space data into two sub-data, separately. Then two new objects are constructed by combining the two sub-T1/T2 data. After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image. Finally, the objective T2 is synthesized by extracting the sub-T2 data of each part. Experimental results showed that our combined technique can achieve comparable or better results than using traditional k-space parallel imaging(SAKE) that processes each contrast independently.

翻译：多对比度磁共振成像是一种重要且必要的医学成像技术。然而，多对比度成像采集时间较长且易产生运动伪影。特别是T2加权图像因其较长的重复时间（TR）而导致采集时间延长。相比之下，T1加权图像具有较短的TR。因此，利用T1和T2加权图像之间的互补信息是缩短总成像时间的一种方法。以往的T1辅助T2重建方法主要集中在图像域，采用基于整体的图像融合方法。图像域重建方法存在计算复杂度高、灵活性有限等缺陷。为解决这一问题，我们提出一种名为基于分区的K空间合成（PKS）的新型多对比度成像方法，该方法通过特征融合可实现T2加权图像的超高质量重建。具体而言，我们首先将全采样的T1 K空间数据和欠采样的T2 K空间数据分别分解为两个子数据，然后通过组合两组子T1/T2数据构建两个新对象，接着将这两个新对象作为完整数据实现T2加权图像的重建，最后通过提取各部分的子T2数据合成目标T2图像。实验结果表明，我们的组合技术可获得与传统K空间并行成像（SAKE）相当或更优的重建结果，而传统方法需要独立处理每个对比度。