Lo Jonathan Lok-Chuen, Brady Michael, Moore Niall
Wolfson Medical Vision Laboratory, University of Oxford, Oxford, UK.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):865-72. doi: 10.1007/11866565_106.
The estimation and subsequent use of tissue T1(x) parameters at each image location x can potentially lead to a more reliable classification of breast tissues. T1 values can be estimated using multiple (typically 3) MRI images of different flip angles. However, breathing and other slight movements can render the highly non-linear estimation procedure error-prone. In this paper, a simultaneous multiple image registration method is proposed to solve this problem. The registration method is built upon the idea of conserving inverse consistency and transitivity among the multiple image transformations. The algorithm is applied to both simulated data and real breast MRI images. The performance is compared with existing pairwise image registration method. The results clearly indicate that the simultaneous multiple image registration algorithm leads to much more accurate T1 estimation.
在每个图像位置x处估计组织T1(x)参数并随后加以利用,有可能使乳腺组织的分类更为可靠。可使用不同翻转角的多个(通常为3个)MRI图像来估计T1值。然而,呼吸及其他轻微运动可能会使高度非线性的估计过程容易出错。本文提出了一种同步多图像配准方法来解决这一问题。该配准方法基于保持多个图像变换之间的逆一致性和传递性这一理念构建而成。该算法应用于模拟数据和真实乳腺MRI图像。将其性能与现有的成对图像配准方法进行了比较。结果清楚地表明,同步多图像配准算法能带来更为准确的T1估计。