Background:Magnetic resonance imaging(MRI)is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics.Acquiring high-quality MRI data is of paramount import...Background:Magnetic resonance imaging(MRI)is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics.Acquiring high-quality MRI data is of paramount importance.Super-resolution reconstruction serves as a post-acquisition method capable of improving MRI data quality.Current methods predominantly utilize convolutional neural networks in super-resolution reconstruction.However,convolutional layers have inherent limitations in capturing extensive spatial dependencies due to their localized nature.Methods:We developed a new methodology that enables rapid and high-quality MRI data acquisition through a novel super-resolution approach.We proposed an innovative architecture using transformers to exploit long-range spatial dependencies present in images,allowing for an unsupervised learning framework specifically designed for super-resolution tasks tailored to individual subject.We validated our approach using both simulated data and clinical data comprising 40 scans acquired with a 3-T MRI system.Results:We obtained images with T2 contrast at an isotropic spatial resolution of 500μm in just 4 min of imaging time,and simultaneously,the signal-to-noise ratio and contrast-to-noise ratio were improved by 13.23% and 18.45%,respectively,in comparison to current leading super-resolution techniques.Conclusions:The results demonstrated that incorporating long-range spatial dependencies substantially improved super-resolution reconstruction,thereby allowing for the acquisition of high-quality MRI data with reduced imaging time.展开更多
基金supported in part by the Beijing Natural Science Foundation under Award Number L258055in part by the Major Program of the National Natural Science Foundation of China under Award Numbers 62394310 and 62394312in part by the National Institutes of Health(NIH)under Award Numbers R01 EB019483,R01 NS106030,R01 NS124212,R01 LM013608,R01 HD109395,R01 EB031849,R01 NS133228,R01 NS121657,R21 EB036105,and S10OD025111.
文摘Background:Magnetic resonance imaging(MRI)is of considerable importance due to its wide range of applications in both scientific research and clinical diagnostics.Acquiring high-quality MRI data is of paramount importance.Super-resolution reconstruction serves as a post-acquisition method capable of improving MRI data quality.Current methods predominantly utilize convolutional neural networks in super-resolution reconstruction.However,convolutional layers have inherent limitations in capturing extensive spatial dependencies due to their localized nature.Methods:We developed a new methodology that enables rapid and high-quality MRI data acquisition through a novel super-resolution approach.We proposed an innovative architecture using transformers to exploit long-range spatial dependencies present in images,allowing for an unsupervised learning framework specifically designed for super-resolution tasks tailored to individual subject.We validated our approach using both simulated data and clinical data comprising 40 scans acquired with a 3-T MRI system.Results:We obtained images with T2 contrast at an isotropic spatial resolution of 500μm in just 4 min of imaging time,and simultaneously,the signal-to-noise ratio and contrast-to-noise ratio were improved by 13.23% and 18.45%,respectively,in comparison to current leading super-resolution techniques.Conclusions:The results demonstrated that incorporating long-range spatial dependencies substantially improved super-resolution reconstruction,thereby allowing for the acquisition of high-quality MRI data with reduced imaging time.
