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arXiv · 2312.00387

Partition-based K-space Synthesis for Multi-contrast Parallel Imaging

Yuxia Huang·Zhonghui Wu·Xiaoling Xu·Minghui Zhang·Shanshan Wang·Qiegen Liu
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Abstract

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.

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Yuxia Huang, Zhonghui Wu, Xiaoling Xu, Minghui Zhang, Shanshan Wang, Qiegen Liu. 2023-12-01. Partition-based K-space Synthesis for Multi-contrast Parallel Imaging. https://arxiv.org/abs/2312.00387

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