Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstr...Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.展开更多
The work of this paper analyzes the performance of Sensitivity Encoding (SENSE) through actual data sets and determines the problem of computational efficiency. It corrects the error of the detection signal through th...The work of this paper analyzes the performance of Sensitivity Encoding (SENSE) through actual data sets and determines the problem of computational efficiency. It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment. The actual data set handles the image resolution. The performance and experimental results of SENSE are analyzed through actual data sets.展开更多
The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review inve...The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following:Technical challenges and corresponding strategies:Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities.Advanced techniques including high‐performance gradient systems,parallel imaging,multi‐shot acquisition,and parallel transmission can mitigate these issues.Comparison of 3‐Tesla and 7T diffusion imaging:Technologies such as multiplexed sensitivity encoding and deep learning reconstruction(DLR)have been developed to mitigate artifacts and improve image quality.This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system,facilitating enhanced visualization of microstructural changes.Despite greater geometric distortions and signal inhomogeneity at 7T,the system shows clear advantages in high b‐value imaging and highresolution diffusion tensor imaging.Additionally,multiplexed sensitivity encoding significantly reduces image blurring and distortion,and DLR substantially improves the signal‐to‐noise ratio and image sharpness.7T diffusion applications in structural analysis and disease characterization:This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.展开更多
基金the National Natural Science Foundation of China(No.61861023)the Yunnan Fundamental Research Project(No.202301AT070452)。
文摘Sensitivity encoding(SENSE)is a parallel magnetic resonance imaging(MRI)reconstruction model by utilizing the sensitivity information of receiver coils to achieve image reconstruction.The existing SENSE-based reconstruction algorithms usually used nonadaptive sparsifying transforms,resulting in a limited reconstruction accuracy.Therefore,we proposed a new model for accurate parallel MRI reconstruction by combining the L0 norm regularization term based on the efficient sum of outer products dictionary learning(SOUPDIL)with the SENSE model,called SOUPDIL-SENSE.The SOUPDIL-SENSE model is mainly solved by utilizing the variable splitting and alternating direction method of multipliers techniques.The experimental results on four human datasets show that the proposed algorithm effectively promotes the image sparsity,eliminates the noise and artifacts of the reconstructed images,and improves the reconstruction accuracy.
文摘The work of this paper analyzes the performance of Sensitivity Encoding (SENSE) through actual data sets and determines the problem of computational efficiency. It corrects the error of the detection signal through the calibration function of the percentage signal change, and uses the three-dimensional sensor image reconstruction technology to calibrate the sensitivity of the blood to the magnetic change, enhances the sensitivity of the magnetic susceptibility gradient, and reduces the scanning time of the MRI experiment. The actual data set handles the image resolution. The performance and experimental results of SENSE are analyzed through actual data sets.
文摘The development of 7‐Tesla(7T)magnetic resonance imaging systems has opened new avenues for exploring the advantages of diffusion imaging at higher field strengths,especially in neuroscience research.This review investigates whether 7T diffusion imaging offers significant benefits over lower field strengths by addressing the following:Technical challenges and corresponding strategies:Challenges include achieving shorter transverse relaxation/effective transverse relaxation times and greater B0 and B1 inhomogeneities.Advanced techniques including high‐performance gradient systems,parallel imaging,multi‐shot acquisition,and parallel transmission can mitigate these issues.Comparison of 3‐Tesla and 7T diffusion imaging:Technologies such as multiplexed sensitivity encoding and deep learning reconstruction(DLR)have been developed to mitigate artifacts and improve image quality.This comparative analysis demonstrates significant improvements in the signal‐to‐noise ratio and spatial resolution at 7T with a powerful gradient system,facilitating enhanced visualization of microstructural changes.Despite greater geometric distortions and signal inhomogeneity at 7T,the system shows clear advantages in high b‐value imaging and highresolution diffusion tensor imaging.Additionally,multiplexed sensitivity encoding significantly reduces image blurring and distortion,and DLR substantially improves the signal‐to‐noise ratio and image sharpness.7T diffusion applications in structural analysis and disease characterization:This review discusses the potential applications of 7T diffusion imaging in structural analysis and disease characterization.
