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Adaptive k-space learning and high-dimensional subsets embedding for parallel MRI reconstruction

Year: 2024
Type: article
Abstract: Magnetic resonance imaging (MRI) inherently requires considerable time for data acquisition, but obtaining multi-contrast MRI data further prolongs this process, thereby increasing susceptibility to motion artifacts. It is worth noting that the multi-contrast MR images have both structural similarities and unique contrast information. Therefore, to take advantage of their similarities while preserving their distinctive characteristics, we proposed a new method called high-dimensional subsets embedding (HDSE). This novel approach is based on the frame of low-rank modeling of local k -space neighborhoods with parallel imaging (P-LORAKS). Specifically, our approach utilizes the structural similarity of multi-contrast MR images to process different k -space data through two independent channels. In one channel, we individually separate the complementary T 1 - T 2 k -space data and directly construct a new subset of local k -space, allowing the model to better capture structural correlations between multiple contrasts. In another channel, we provide global under-sampled T 2 -weighted k -space data further constrain image acquisition in high-dimensional space to maintain image consistency and reduce noise amplification. These two different channels information is fused together to form high-dimensional feature objects. Besides, we embed the constructed objects into P-LORAKS in various ways to enhance the reconstruction performance. Experimental results demonstrated that the aided reconstruction of local subsets fusion and the high-dimensional reconstruction of adaptive global constraints can improve the accuracy of image reconstruction and enhance the robustness of the model.
DOI: 10.1016/j.mrl.2024.200175
Institution: Nanchang University
Language: English
Field: Medicine
Open Access status: diamond
OpenAlex ID: W4405884495
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Cites: 41
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Related to: 10