Li Ya, Yang Ran, Zhang Cishen, Zhang Jingxin, Jia Sen, Zhou Zhiyang
School of Information Science and Technology, Sun Yat-sen University, Guangzhou 510009, China.
School of Mobile Information Engineering, Sun Yat-sen University, Zhuhai 519082, China.
Med Phys. 2015 Sep;42(9):5530-44. doi: 10.1118/1.4928152.
The application of compressed sensing (CS) technology in magnetic resonance imaging (MRI) is to accelerate the MRI scan speed by incoherent undersampling of k-space data and nonlinear iterative reconstruction of MRI images. This paper generalizes the existing rosette trajectories to configure the sampling patterns for undersampled k-space data acquisition in MRI scans. The arch and curvature characteristics of the generalized rosette trajectories are analyzed to explore their feasibility and advantages for CS reconstruction of MRI images.
Two key properties crucial to the CS MRI application, the scan speed and sampling incoherence of the generalized rosette trajectories, are analyzed. The analysis on the scan speed of generalized rosette trajectories is based on the transversal time derived from the curvature of the trajectories, and the sampling incoherence is based on the evaluation of the point spread function for the measurement matrix. The results of analysis are supported by extensive simulations where the performances of rosette, spiral, and radial sampling patterns at different acceleration factors are compared.
It is shown that compared with spiral trajectories, the arch and curvature characteristics of the generalized rosette trajectories are more feasible to meet the physical requirements of undersampled k-space data acquisition in terms of time shortness and scan area. It is further shown that the sampling pattern of the rosette trajectory has higher incoherence than that of the other trajectories and can thus achieve higher reconstruction performance. Reconstruction performances illustrate that the rosette trajectory can achieve about 10% higher peak signal-to-noise ratio than radial and spiral trajectories under the high acceleration factor R = 10.
The generalized rosette trajectories can be a desirable candidate for CS reconstruction of MRI.
压缩感知（CS）技术在磁共振成像（MRI）中的应用是通过对k空间数据进行非相干欠采样以及对MRI图像进行非线性迭代重建来加快MRI扫描速度。本文对现有的玫瑰花结轨迹进行推广，以配置用于MRI扫描中欠采样k空间数据采集的采样模式。分析了广义玫瑰花结轨迹的拱形和曲率特征，以探索其在MRI图像CS重建中的可行性和优势。
分析了广义玫瑰花结轨迹对CS MRI应用至关重要的两个关键特性，即扫描速度和采样非相干性。对广义玫瑰花结轨迹扫描速度的分析基于从轨迹曲率导出的横向时间，采样非相干性基于对测量矩阵的点扩散函数的评估。分析结果得到了广泛模拟的支持，其中比较了不同加速因子下玫瑰花结、螺旋和径向采样模式的性能。
结果表明，与螺旋轨迹相比，广义玫瑰花结轨迹的拱形和曲率特征在时间短和扫描面积方面更符合欠采样k空间数据采集的物理要求。进一步表明，玫瑰花结轨迹的采样模式比其他轨迹具有更高的非相干性，因此可以实现更高的重建性能。重建性能表明，在高加速因子R = 10时，玫瑰花结轨迹的峰值信噪比相比径向和螺旋轨迹可高出约10%。
广义玫瑰花结轨迹可以成为MRI的CS重建的理想选择。
