Image registration and analysis for quantitative myocardial perfusion: application to dynamic circular cardiac CT

被引:19
作者
Isola, A. A. [1 ]
Schmitt, H. [2 ]
van Stevendaal, U. [2 ]
Begemann, P. G. [3 ]
Coulon, P. [4 ]
Boussel, L. [5 ]
Grass, M. [2 ]
机构
[1] Philips Res Labs, Xray Imaging Syst Dept, D-52066 Aachen, Germany
[2] Philips Res Labs, Sect Digital Imaging, D-22335 Hamburg, Germany
[3] Univ Hosp Hamburg Eppendorf, Dept Radiol, D-20246 Hamburg, Germany
[4] Philips Healthcare France, F-92150 Suresnes, France
[5] Louis Pradel Hosp, Dept Radiol, CREATIS, INSERM,U630,CNRS,UMR 5515, Lyon, France
关键词
CONE-BEAM CT; COMPUTED-TOMOGRAPHY; MUTUAL-INFORMATION; MICROVASCULAR OBSTRUCTION; INFARCTION; RECONSTRUCTION; OPTIMIZATION; ENHANCEMENT; MR; VIABILITY;
D O I
10.1088/0031-9155/56/18/010
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Large area detector computed tomography systems with fast rotating gantries enable volumetric dynamic cardiac perfusion studies. Prospectively, ECG-triggered acquisitions limit the data acquisition to a predefined cardiac phase and thereby reduce x-ray dose and limit motion artefacts. Even in the case of highly accurate prospective triggering and stable heart rate, spatial misalignment of the cardiac volumes acquired and reconstructed per cardiac cycle may occur due to small motion pattern variations from cycle to cycle. These misalignments reduce the accuracy of the quantitative analysis of myocardial perfusion parameters on a per voxel basis. An image-based solution to this problem is elastic 3D image registration of dynamic volume sequences with variable contrast, as it is introduced in this contribution. After circular cone-beam CT reconstruction of cardiac volumes covering large areas of the myocardial tissue, the complete series is aligned with respect to a chosen reference volume. The results of the registration process and the perfusion analysis with and without registration are evaluated quantitatively in this paper. The spatial alignment leads to improved quantification of myocardial perfusion for three different pig data sets.
引用
收藏
页码:5925 / 5947
页数:23
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