Since the spatial resolution of diffusion weighted magnetic resonance imaging(DWI)is subject to scanning time and other constraints,its spatial resolution is relatively limited.In view of this,a new non-local DWI imag...Since the spatial resolution of diffusion weighted magnetic resonance imaging(DWI)is subject to scanning time and other constraints,its spatial resolution is relatively limited.In view of this,a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution.Based on the non-local strategy,we use the joint information of adjacent scan directions to implement a new weighting scheme.The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI.However,the algorithm ran slowly because of the joint information.In order to apply the algorithm to the actual scene,we compare the proposed algorithm on CPU and GPU respectively.It is found that the processing time on GPU is much less than on CPU,and that the highest speedup ratio to the traditional algorithm is more than 26 times.It raises the possibility of applying reconstruction algorithms in actual workplaces.展开更多
semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size ...semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size of the receptivefield.To address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The influence of these two correlations on seman-tic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models.展开更多
基金This work was supported by the Youth Foundation of Education Department in Sichuan(Grant No.2017JQ0030).
文摘Since the spatial resolution of diffusion weighted magnetic resonance imaging(DWI)is subject to scanning time and other constraints,its spatial resolution is relatively limited.In view of this,a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution.Based on the non-local strategy,we use the joint information of adjacent scan directions to implement a new weighting scheme.The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI.However,the algorithm ran slowly because of the joint information.In order to apply the algorithm to the actual scene,we compare the proposed algorithm on CPU and GPU respectively.It is found that the processing time on GPU is much less than on CPU,and that the highest speedup ratio to the traditional algorithm is more than 26 times.It raises the possibility of applying reconstruction algorithms in actual workplaces.
基金supported by the Sichuan Science and Technology Program(Grant No.2019ZDZX0005,2019YFG0496,2020YFG0143,2019JDJQ0002 and 2020YFG0009).
文摘semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size of the receptivefield.To address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The influence of these two correlations on seman-tic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models.
