Partition-based K-space Synthesis for Multi-contrast Parallel Imaging
Yuxia Huang Zhonghui Wu Xiaoling Xu Minghui Zhang Shanshan Wang Qiegen Liu
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.