用于分析MR-PET脑图像的迭代主轴配准方法

Iterative Principal Axes Registration method for analysis of MR-PET brain images.

作者信息

Dhawan A P, Arata L K, Levy A V, Mantil J

机构信息

Department of Electrical and Computer Engineering and Radiology, University of Cincinnati, OH 45221, USA.

出版信息

IEEE Trans Biomed Eng. 1995 Nov;42(11):1079-87. doi: 10.1109/10.469374.

DOI:10.1109/10.469374

PMID:7498911

Abstract

Computerized automatic registration of MR-PET images of the brain is of significant interest for multimodality brain image analysis. In this paper, we discuss the Principal Axes Transformation for registration of three-dimensional MR and PET images. A new brain phantom designed to test MR-PET registration accuracy determines that the Principal Axes Registration method is accurate to within an average of 1.37 mm with a standard deviation of 0.78 mm. Often the PET scans are not complete in the sense that the PET volume does not match the respective MR volume. We have developed an Iterative Principal Axes Registration (IPAR) algorithm for such cases. Partial volumes of PET can be accurately registered to the complete MR volume using the new iterative algorithm. The quantitative and qualitative analyses of MR-PET image registration are presented and discussed. Results show that the new Principal Axes Registration algorithm is accurate and practical in MR-PET correlation studies.

摘要

脑磁共振成像（MR）与正电子发射断层扫描（PET）图像的计算机自动配准对于多模态脑图像分析具有重要意义。在本文中，我们讨论用于三维MR和PET图像配准的主轴变换。一种旨在测试MR-PET配准精度的新型脑体模确定，主轴配准方法的平均精度在1.37毫米以内，标准差为0.78毫米。通常情况下，PET扫描并不完整，即PET体积与相应的MR体积不匹配。针对这种情况，我们开发了一种迭代主轴配准（IPAR）算法。使用新的迭代算法可以将PET的部分体积准确配准到完整的MR体积上。本文给出并讨论了MR-PET图像配准的定量和定性分析。结果表明，新的主轴配准算法在MR-PET相关性研究中准确且实用。