Engineering Research Center for Computer Integrated Surgery, Johns Hopkins University, Baltimore, MD 21218, USA.
IEEE Trans Med Imaging. 2011 Apr;30(4):928-45. doi: 10.1109/TMI.2010.2091966. Epub 2010 Nov 11.
This paper introduces two real-time elastography techniques based on analytic minimization (AM) of regularized cost functions. The first method (1D AM) produces axial strain and integer lateral displacement, while the second method (2D AM) produces both axial and lateral strains. The cost functions incorporate similarity of radio-frequency (RF) data intensity and displacement continuity, making both AM methods robust to small decorrelations present throughout the image. We also exploit techniques from robust statistics to make the methods resistant to large local decorrelations. We further introduce Kalman filtering for calculating the strain field from the displacement field given by the AM methods. Simulation and phantom experiments show that both methods generate strain images with high SNR, CNR and resolution. Both methods work for strains as high as 10% and run in real-time. We also present in vivo patient trials of ablation monitoring. An implementation of the 2D AM method as well as phantom and clinical RF-data can be downloaded.
本文介绍了两种基于正则化代价函数分析最小化（AM）的实时弹性成像技术。第一种方法（1D AM）产生轴向应变和整数横向位移，而第二种方法（2D AM）则产生轴向和横向应变。代价函数结合了射频（RF）数据强度和位移连续性的相似性，使得两种 AM 方法对整个图像中存在的小失相关都具有鲁棒性。我们还利用稳健统计学中的技术使这些方法能够抵抗大的局部失相关。我们进一步引入卡尔曼滤波，从 AM 方法给出的位移场计算应变场。模拟和体模实验表明，这两种方法都能生成具有高信噪比、对比度噪声比和分辨率的应变图像。这两种方法都适用于高达 10%的应变，并且可以实时运行。我们还介绍了用于消融监测的体内患者试验。可以下载 2D AM 方法的实现以及体模和临床 RF 数据。
