结合GRAPPA与压缩感知加速磁共振成像
发布时间：2019-01-06 11:17
【摘要】：加快扫描速度是磁共振成像(Magnetic Resonance Imaging,MRI)发展中重要的一步,并行成像(ParallelImaging,PI)利用相阵列线圈的空间信息和特定的重建算法,能有效缩短扫描时间。其中在临床医疗中应用最广的是SENSE(Sensitivity Encoding for Fast MRI)和 GRAPPA(Generalized Auto-calibrating Partially Parallel Acquisition)。压缩感知(CompressedSensing,CS)是从另一个角度进行加速采样的新技术,该方法基于磁共振图像的稀疏特性,对k空间进行随机欠采样,并通过优化重建算法去除非相干伪影,得到可以用于临床诊断的磁共振图像。由于并行成像和压缩感知基础理论的不同以及采样方式上的不同,已有学者结合两种算法来进一步提高磁共振图像的扫描速度,并通过特定算法来重建得到最终图像。本文提出了一种新的结合并行成像和压缩感知加速磁共振成像的方法。在重建过程中,采用分步重建的方式,用GRAPPA重建填充每个通道的部分k空间数据,再用CS重建每个通道的全k空间数据,最后再进行通道合并得到最终重建图像。其中在采样方式上,本文设计了一种局部沿着相位编码方向等间隔采样模板,再利用该模板对全k空间进行随机采样,该方法可以有效的利用GRAPPA尽可能重建更多的k空间数据。同时本文也讨论了 GRAPPA的重建数据与后续CS重建中保真权重的关系,进而优化了 CS中的目标函数,借助GRAPPA重建数据来提高CS重建图像的质量。本文通过大量不同模态的磁共振图像后处理模拟实验,证明了本文提出的方法可以在相同采样率的情况下,获得较好的重建结果。
[Abstract]:Accelerating scanning speed is an important step in the development of magnetic resonance imaging (Magnetic Resonance Imaging,MRI). Parallel imaging (ParallelImaging,PI) can effectively shorten the scanning time by using the spatial information of phase array coil and special reconstruction algorithm. SENSE (Sensitivity Encoding for Fast MRI) and GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisition). Are the most widely used in clinical medicine. Compression sensing (CompressedSensing,CS) is a new technique for accelerating sampling from another point of view. Based on the sparse characteristic of magnetic resonance images, this method performs random under-sampling in k-space, and uses an optimized reconstruction algorithm to remove coherent artifacts. Magnetic resonance imaging can be used for clinical diagnosis. Due to the difference of the basic theory of parallel imaging and compression sensing and the difference of sampling methods, some scholars have combined two algorithms to further improve the scanning speed of magnetic resonance image and reconstruct the final image by a specific algorithm. In this paper, a new method combining parallel imaging with compression sensing accelerated magnetic resonance imaging is proposed. In the process of reconstruction, the partial k space data of each channel is reconstructed with GRAPPA, then the full k space data of each channel is reconstructed by CS, and the final reconstructed image is obtained by channel merging. In the sampling mode, this paper designs a local sampling template with equal interval along the direction of phase coding, and then uses the template to sample the whole k space randomly. This method can effectively use GRAPPA to reconstruct more k-space data as much as possible. At the same time, this paper also discusses the relationship between GRAPPA reconstruction data and fidelity weight in subsequent CS reconstruction, and then optimizes the objective function in CS, and improves the quality of CS reconstructed image by GRAPPA reconstruction data. In this paper, a large number of post-processing simulations of magnetic resonance images with different modes are carried out, and it is proved that the proposed method can obtain better reconstruction results under the same sampling rate.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
本文编号：2402725
[Abstract]:Accelerating scanning speed is an important step in the development of magnetic resonance imaging (Magnetic Resonance Imaging,MRI). Parallel imaging (ParallelImaging,PI) can effectively shorten the scanning time by using the spatial information of phase array coil and special reconstruction algorithm. SENSE (Sensitivity Encoding for Fast MRI) and GRAPPA (Generalized Auto-calibrating Partially Parallel Acquisition). Are the most widely used in clinical medicine. Compression sensing (CompressedSensing,CS) is a new technique for accelerating sampling from another point of view. Based on the sparse characteristic of magnetic resonance images, this method performs random under-sampling in k-space, and uses an optimized reconstruction algorithm to remove coherent artifacts. Magnetic resonance imaging can be used for clinical diagnosis. Due to the difference of the basic theory of parallel imaging and compression sensing and the difference of sampling methods, some scholars have combined two algorithms to further improve the scanning speed of magnetic resonance image and reconstruct the final image by a specific algorithm. In this paper, a new method combining parallel imaging with compression sensing accelerated magnetic resonance imaging is proposed. In the process of reconstruction, the partial k space data of each channel is reconstructed with GRAPPA, then the full k space data of each channel is reconstructed by CS, and the final reconstructed image is obtained by channel merging. In the sampling mode, this paper designs a local sampling template with equal interval along the direction of phase coding, and then uses the template to sample the whole k space randomly. This method can effectively use GRAPPA to reconstruct more k-space data as much as possible. At the same time, this paper also discusses the relationship between GRAPPA reconstruction data and fidelity weight in subsequent CS reconstruction, and then optimizes the objective function in CS, and improves the quality of CS reconstructed image by GRAPPA reconstruction data. In this paper, a large number of post-processing simulations of magnetic resonance images with different modes are carried out, and it is proved that the proposed method can obtain better reconstruction results under the same sampling rate.
【学位授予单位】：华东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
【参考文献】
相关硕士学位论文 前1条
1 高芒;磁共振成像压缩感知同步重建研究[D];华东师范大学;2015年
，本文编号：2402725
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