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This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring...
In this paper, we propose a model based clustering method for functional magnetic resonance imaging (fMRI) data to detect the functional connectivity network. The Potts model, which represents spatial interactions of neighboring voxels, is introduced to integrate the temporal mixture regression modeling into one single unified model. The estimation of the parameters is achieved through a restoration...
Parallel magnetic resonance imaging (pMRI) cannot achieve its maximum reduction factor due to practical limitations. The combination of pMRI and distributed compressed sensing (DCS) for further acceleration is of great interest. In this paper, we propose a method to combine sensitivity encoding (SENSE), one of the standard methods for pMRI, and M-FOCUSS, an algorithm solving DCS reconstruction problem...
Segmentation is one of the basic problems in MRI analysis. We consider the problem of simultaneously segmenting multiple MR images, which, for example, could be a series of (2D/3D) images of the same tissue scanned over time, different slices of a volume image, or images of symmetric parts. The multiple MR images to be segmented share common structure information and hence they are able to assist...
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