195 research outputs found
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data
Aggregating multi-subject functional magnetic resonance imaging (fMRI) data
is indispensable for generating valid and general inferences from patterns
distributed across human brains. The disparities in anatomical structures and
functional topographies of human brains warrant aligning fMRI data across
subjects. However, the existing functional alignment methods cannot handle well
various kinds of fMRI datasets today, especially when they are not
temporally-aligned, i.e., some of the subjects probably lack the responses to
some stimuli, or different subjects might follow different sequences of
stimuli. In this paper, a cross-subject graph that depicts the
(dis)similarities between samples across subjects is used as a priori for
developing a more flexible framework that suits an assortment of fMRI datasets.
However, the high dimension of fMRI data and the use of multiple subjects makes
the crude framework time-consuming or unpractical. To address this issue, we
further regularize the framework, so that a novel feasible kernel-based
optimization, which permits nonlinear feature extraction, could be
theoretically developed. Specifically, a low-dimension assumption is imposed on
each new feature space to avoid overfitting caused by the
highspatial-low-temporal resolution of fMRI data. Experimental results on five
datasets suggest that the proposed method is not only superior to several
state-of-the-art methods on temporally-aligned fMRI data, but also suitable for
dealing `with temporally-unaligned fMRI data.Comment: 17 pages, 10 figures, Proceedings of the Association for the
Advancement of Artificial Intelligence (AAAI-20
SEGAN: Structure-Enhanced Generative Adversarial Network for Compressed Sensing MRI Reconstruction
Generative Adversarial Networks (GANs) are powerful tools for reconstructing
Compressed Sensing Magnetic Resonance Imaging (CS-MRI). However most recent
works lack exploration of structure information of MRI images that is crucial
for clinical diagnosis. To tackle this problem, we propose the
Structure-Enhanced GAN (SEGAN) that aims at restoring structure information at
both local and global scale. SEGAN defines a new structure regularization
called Patch Correlation Regularization (PCR) which allows for efficient
extraction of structure information. In addition, to further enhance the
ability to uncover structure information, we propose a novel generator SU-Net
by incorporating multiple-scale convolution filters into each layer. Besides,
we theoretically analyze the convergence of stochastic factors contained in
training process. Experimental results show that SEGAN is able to learn target
structure information and achieves state-of-the-art performance for CS-MRI
reconstruction.Comment: 9 pages,5 figures, Proceedings of the Association for the Advancement
of Artificial Intelligence (AAAI-19
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