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Multi-contrast magnetic resonance imaging is a significant and essentialmedical imaging technique.However, multi-contrast imaging has longeracquisition time and is easy to cause motion artifacts. In particular, theacquisition time for a T2-weighted image is prolonged due to its longerrepetition time (TR). On the contrary, T1-weighted image has a shorter TR.Therefore,utilizing complementary information across T1 and T2-weighted imageis a way to decrease the overall imaging time. Previous T1-assisted T2reconstruction methods have mostly focused on image domain using whole-basedimage fusion approaches. The image domain reconstruction method has the defectsof high computational complexity and limited flexibility. To address thisissue, we propose a novel multi-contrast imaging method called partition-basedk-space synthesis (PKS) which can achieve super reconstruction quality ofT2-weighted image by feature fusion. Concretely, we first decomposefully-sampled T1 k-space data and under-sampled T2 k-space data into twosub-data, separately. Then two new objects are constructed by combining the twosub-T1/T2 data. After that, the two new objects as the whole data to realizethe reconstruction of T2-weighted image. Finally, the objective T2 issynthesized by extracting the sub-T2 data of each part. Experimental resultsshowed that our combined technique can achieve comparable or better resultsthan using traditional k-space parallel imaging(SAKE) that processes eachcontrast independently.

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