Technische Universität München, Department of Computer Science, Munich, Germany; GE Global Research, Munich, Germany; Imagerie Adaptative Diagnostique et Interventionnelle, Université de Lorraine, Nancy, France.
Technische Universität München, Department of Computer Science, Munich, Germany; GE Global Research, Munich, Germany; Imagerie Adaptative Diagnostique et Interventionnelle, Université de Lorraine, Nancy, France.
Comput Biol Med. 2018 May 1;96:106-115. doi: 10.1016/j.compbiomed.2018.03.009. Epub 2018 Mar 12.
T mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm.
After nonrigid registration of the image series, a vectorized Levenberg-Marquardt (LM) technique is proposed to improve the robustness of the curve fitting algorithm by allowing spatial regularization of the parametric maps. In addition, a region-based initialization is proposed to improve the initial guess of the T value. The algorithm was validated with cardiac T mapping data from 16 volunteers acquired with saturation-recovery (SR) and inversion-recovery (IR) techniques at 3T, both pre- and post-injection of a contrast agent. Signal models of T relaxation with 2 and 3 parameters were tested.
The vectorized LM fitting showed good agreement with its pixel-wise version but allowed reduced calculation time (60 s against 696 s on average in Matlab with 256 × 256 × 8(11) images). Increasing the spatial regularization parameter led to noise reduction and improved precision of T values in SR sequences. The region-based initialization was particularly useful in IR data to reduce the variability of the blood T.
We have proposed a vectorized curve fitting algorithm allowing spatial regularization, which could improve the robustness of the curve fitting, especially for myocardial T mapping with SR sequences.
T 映射是一种新兴的 MRI 研究工具，用于评估病变心肌组织。最近的研究集中在图像采集协议和运动校正上，但对曲线拟合算法的关注较少。
在图像序列的非刚性配准之后，提出了一种矢量化的 Levenberg-Marquardt（LM）技术，通过允许参数图的空间正则化，提高了曲线拟合算法的稳健性。此外，还提出了一种基于区域的初始化方法，以改善 T 值的初始猜测。该算法使用 3T 上的饱和恢复（SR）和反转恢复（IR）技术对 16 名志愿者的心脏 T 映射数据进行了验证，包括注射对比剂前后的数据。测试了具有 2 个和 3 个参数的 T 弛豫信号模型。
矢量化的 LM 拟合与像素级版本具有很好的一致性，但计算时间更短（在 Matlab 中，对于 256×256×8（11）图像，平均计算时间为 60s，而不是 696s）。增加空间正则化参数可以降低 SR 序列中噪声并提高 T 值的精度。基于区域的初始化在 IR 数据中特别有用，可以降低血液 T 的可变性。
我们提出了一种允许空间正则化的矢量化曲线拟合算法，这可以提高曲线拟合的稳健性，特别是对于具有 SR 序列的心肌 T 映射。
