Liver DCE-MRI registration based on sparse recovery of contrast agent curves
Liver DCE-MRI registration based on sparse recovery of contrast agent curves
复制标题
基于造影剂曲线稀疏恢复的肝脏DCE-MRI配准
DOI：
10.1002/mp.15193
复制
发表时间：
2021-09-16
期刊：
影响因子：
3.8
通讯作者：
Feng, Qianjin
中科院分区：
文献类型：
--
作者：
Sun, Yuhang;Zhu, Qiaoyun;Feng, Qianjin
Purpose Dynamic contrast-enhanced MRI (DCE-MRI) registration is a challenging task because of the effect of remarkable intensity changes caused by contrast agent injections. Unrealistic deformation usually occurs by using traditional intensity-based algorithms. To alleviate the effect of contrast agent on registration, we proposed a DCE-MRI registration strategy and investigated the registration performance on the clinical DCE-MRI time series of liver. Method We reconstructed the time-intensity curves of the contrast agent through sparse representation with a predefined dictionary whose columns were the time-intensity curves with high correlations with respect to a preselected contrast agent curve. After reshaping 1D-reconstructed contrast agent time-intensity curves into a 4D contrast agent time series, we aligned the original time series to the reconstructed contrast agent time series through traditional free-form deformation (FFD) registration scheme combined with a residual complexity (RC) similarity and an iterative registration strategy. This study included the DCE-MRI time series of 20 patients with liver cancer. Results Qualitatively, the time-cut images and subtraction images of different registration methods did not obviously differ. Quantitatively, the mean (standard deviation) of temporal intensity smoothness of all the patients achieved 54.910 (18.819), 54.609 (18.859), and 53.391 (19.031) in FFD RC, RDDR, Zhou et al.'s method and the proposed method, respectively. The mean (standard deviation) of changes in the lesion volume were 0.985 (0.041), 0.983 (0.041), 0.981 (0.046), and 0.989 (0.036) in FFD RC, RDDR, Zhou et al.'s method and the proposed method. Conclusion Our proposed method would be an effective registration strategy for DCE-MRI time series, and its performance was comparable with that of three advanced registration methods.
