欠采样包裹相位图的展开算法

利用四种常见算法对由球面参考光像面数字全息显微术得到的中药饮片海金沙细胞的包裹相位图进行了相位解包裹。结果表明:基于离散余弦的最小二乘算法和预条件共轭梯度算法会使欠采样区域的误差进行传递,使得解包裹相位产生较大误差;质量图导向路径跟踪算法由于未识别残差点,导致出现拉线或者孤岛区域,而且处理速度很慢;基于横向剪切干涉的最小二乘算法处理欠采样包裹相位图的效果最好,而且处理速度较快。     

Stationary wavelet transform for under-sampled MRI reconstruction

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional l_p-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions.