Glodeck Daniel, Hesser Jürgen, Zheng Lei
Experimental Radiation Oncology, Department of Radiation Oncology, University Medical Center Mannheim, Heidelberg University, Germany.
Magn Reson Imaging. 2016 Feb;34(2):127-36. doi: 10.1016/j.mri.2015.10.032. Epub 2015 Nov 3.
In this paper, a novel strategy for correcting both geometric and image intensity distortions of echo-planar imaging (EPI) MRI data is presented. To deal with small local distortions caused by rapid changes of the magnetic field, an improved multimodal registration framework using normalized mutual information (NMI) in combination with a multi-scale technique is presented to estimate a dense displacement field. To ensure the robustness of this high dimensional ill-posed inverse problem, a novel anisotropic regularization functional is used. In order to quantify geometric distortions, a new quality measure, called standardized contour distance (SCD), is introduced. It uses the outer structure shape (OSS) information as basis for the evaluation. The new registration method was evaluated with one monomodal phantom data set and two multimodal human brain data sets (BrainSuite trainings data, SPM Subject data). By comparing with recent and efficient techniques of the state of the art, in the monomodal case, the new approach achieves results comparable to the sum of squared differences as data term. In the multimodal cases, our new registration strategy improves the mean of the SCD from 0.96±0.11 to 0.60±0.13 in case of the SPM Subject data and from 0.92±0.07 to 0.78±0.11 in case of the BrainSuite trainings data.
本文提出了一种校正回波平面成像（EPI）磁共振成像（MRI）数据几何和图像强度失真的新策略。为处理由磁场快速变化引起的小局部失真，提出了一种改进的多模态配准框架，该框架使用归一化互信息（NMI）并结合多尺度技术来估计密集位移场。为确保这个高维不适定逆问题的鲁棒性，使用了一种新型各向异性正则化泛函。为量化几何失真，引入了一种称为标准化轮廓距离（SCD）的新质量度量。它将外部结构形状（OSS）信息用作评估基础。使用一个单模态体模数据集和两个多模态人脑数据集（BrainSuite训练数据、SPM受试者数据）对新的配准方法进行了评估。通过与最新的高效技术进行比较，在单模态情况下，新方法获得的结果与将平方差之和用作数据项时相当。在多模态情况下，对于SPM受试者数据，我们的新配准策略将SCD的均值从0.96±0.11提高到0.60±0.13；对于BrainSuite训练数据，将SCD的均值从0.92±0.07提高到0.78±0.11。
