基于可变形群配准与局部低秩不相似度度量的心肌应变估计改进方法
Improve Myocardial Strain Estimation based on Deformable Groupwise Registration with a Locally Low-Rank Dissimilarity Metric
AI总结：
本文提出Groupwise-LLR方法，通过可变形群配准与局部低秩不相似度度量迭代更新全心周期位移场，以减少漂移效应，并在模拟、公开和患者数据集上验证其心肌追踪与应变估计准确性优于现有CMR-FT方法。
摘要：
背景：当前主流的心血管磁共振特征追踪（CMR-FT）方法，包括光流法和成对配准，常常受到累积追踪误差引起的漂移效应影响。在此，我们开发了一种基于可变形群配准与局部低秩（LLR）不相似度度量的CMR-FT方法，以提高心肌追踪和应变估计的准确性。方法：所提出的方法Groupwise-LLR通过迭代更新所有心脏相位的整个位移场来执行特征追踪，以最小化变形影像序列的逐块信号秩之和。该方法通过模拟数据集（n = 20）、公开电影数据集（n = 100）和内部标记MRI患者数据集（n = 16），与替代CMR-FT方法进行了比较，包括Farneback光流法、顺序成对配准方法以及基于全局低秩性的群配准方法。所提出的方法还在模拟和体内追踪中与两种通用群配准方法nD+t B-Splines和pTVreg进行了比较。结果：在模拟数据集上，Groupwise-LLR实现了最低的点追踪误差以及体素级/全局应变误差。在公开数据集上，Groupwise-LLR实现了最低的轮廓追踪误差，减少了舒张晚期的漂移效应，并保持了与替代方法相似的观察者间可重复性。在患者数据集上，Groupwise-LLR在多个心肌节段和层面上与标记MRI的径向应变相关性优于其他CMR-FT方法。结论：所提出的Groupwise-LLR减少了漂移效应，并提供了比替代方法更准确的心肌追踪和应变估计。因此，该方法可能有助于在心脏功能的临床评估中更准确地估计心肌应变。
英文摘要：
Background: Current mainstream cardiovascular magnetic resonance-feature tracking (CMR-FT) methods, including optical flow and pairwise registration, often suffer from the drift effect caused by accumulative tracking errors. Here, we developed a CMR-FT method based on deformable groupwise registration with a locally low-rank (LLR) dissimilarity metric to improve myocardial tracking and strain estimation accuracy. Methods: The proposed method, Groupwise-LLR, performs feature tracking by iteratively updating the entire displacement field across all cardiac phases to minimize the sum of the patchwise signal ranks of the deformed movie. The method was compared with alternative CMR-FT methods including the Farneback optical flow, a sequentially pairwise registration method, and a global low rankness-based groupwise registration method via a simulated dataset (n = 20), a public cine data set (n = 100), and an in-house tagging-MRI patient dataset (n = 16). The proposed method was also compared with two general groupwise registration methods, nD+t B-Splines and pTVreg, in simulations and in vivo tracking. Results: On the simulated dataset, Groupwise-LLR achieved the lowest point tracking errors and voxelwise/global strain errors. On the public dataset, Groupwise-LLR achieved the lowest contour tracking errors, reduced the drift effect in late-diastole, and preserved similar inter-observer reproducibility as the alternative methods. On the patient dataset, Groupwise-LLR correlated better with tagging-MRI for radial strains than the other CMR-FT methods in multiple myocardial segments and levels. Conclusions: The proposed Groupwise-LLR reduces the drift effect and provides more accurate myocardial tracking and strain estimation than the alternative methods. The method may thus facilitate a more accurate estimation of myocardial strains for clinical assessments of cardiac function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
