Huang Yuxia, Wu Zhonghui, Xu Xiaoling, Zhang Minghui, Wang Shanshan, Liu Qiegen
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China.
Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, Chinese Academy of Sciences, Shenzhen 518055, China.
Magn Reson Imaging. 2025 Apr;117:110297. doi: 10.1016/j.mri.2024.110297. Epub 2024 Dec 6.
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 better 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.
Experimental results showed that the developed PKS scheme can achieve comparable or better results than using traditional k-space parallel imaging (SAKE) that processes each contrast independently. At the same time, our method showed good adaptability and robustness under different contrast-assisted and T1-T2 ratios. Efficient target modal image reconstruction under various conditions were realized and had excellent performance in restoring image quality and preserving details.
This work proposed a PKS multi-contrast method to assist in target mode image reconstruction. We have conducted extensive experiments on different multi-contrast, diverse ratios of T1 to T2 and different sampling masks to demonstrate the generalization and robustness of our proposed model.
多对比度磁共振成像是一种重要且必不可少的医学成像技术。然而，多对比度成像具有较长的采集时间，并且容易产生运动伪影。特别是，由于其较长的重复时间（TR），T2加权图像的采集时间会延长。相反，T1加权图像的TR较短。因此，利用T1和T2加权图像的互补信息是减少整体成像时间的一种方法。先前的T1辅助T2重建方法大多集中在基于全图像融合方法的图像域。图像域重建方法具有计算复杂度高和灵活性有限的缺陷。为了解决这个问题，我们提出了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，该方法可以通过特征融合实现更好的T2加权图像重建质量。
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。然后通过组合两个子T1/T2数据构建两个新对象。之后，将这两个新对象作为整体数据来实现T2加权图像的重建。
实验结果表明，所开发的PKS方案可以取得与独立处理每个对比度的传统k空间并行成像（SAKE）相当或更好的结果。同时，我们的方法在不同的对比度辅助和T1-T2比率下表现出良好的适应性和鲁棒性。在各种条件下实现了高效的目标模态图像重建，并且在恢复图像质量和保留细节方面具有出色的性能。
这项工作提出了一种PKS多对比度方法来辅助目标模态图像重建。我们在不同的多对比度、不同的T1与T2比率以及不同的采样掩码上进行了广泛的实验，以证明我们提出的模型的通用性和鲁棒性。
