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- W4386754865 abstract "Magnetic resonance imaging (MRI) always suffered from the problem of long acquisition time. MRI reconstruction is one solution to reduce scan time by skipping certain phase-encoding lines and then restoring high-quality images from undersampled measurements. Recently, implicit neural representation (INR) has emerged as a new deep learning method that represents an object as a continuous function of spatial coordinates, and this function is normally parameterized by a multilayer perceptron (MLP). In this paper, we propose a novel MRI reconstruction method based on INR, which represents the fully-sampled images as the function of pixel coordinates and prior feature vectors of undersampled images for overcoming the generalization problem of INR. Specifically, we introduce a scale-embedded encoder to produce scale-independent pixel-specific features from MR images with different undersampled scales and then concatenate with coordinates vectors to recover fully-sampled MR images via an MLP, thus achieving arbitrary scale reconstruction. The performance of the proposed method was assessed by experimenting on publicly available MRI datasets and compared with other reconstruction methods. Our quantitative evaluation demonstrates the superiority of the proposed method over alternative reconstruction methods." @default.
- W4386754865 created "2023-09-15" @default.
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- W4386754865 date "2023-09-12" @default.
- W4386754865 modified "2023-09-28" @default.
- W4386754865 title "Batch Implicit Neural Representation for MRI Parallel Reconstruction" @default.
- W4386754865 doi "https://doi.org/10.48550/arxiv.2309.06067" @default.
- W4386754865 hasPublicationYear "2023" @default.
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