Free breathing myocardial perfusion data sets for performance analysis of motion compensation algorithms

Abstract

Background: Perfusion quantification by using first-pass gadolinium-enhanced myocardial perfusion magnetic resonance imaging (MRI) has proved to be a reliable tool for the diagnosis of coronary artery disease that leads to reduced blood flow to the myocardium. The image series resulting from such acquisition usually exhibits a breathing motion that needs to be compensated for if a further automatic analysis of the perfusion is to be executed. Various algorithms have been presented to facilitate such a motion compensation, but the lack of publicly available data sets hinders a proper, reproducible comparison of these algorithms.Material: Free breathing perfusion MRI series of ten patients considered clinically to have a stress perfusion defect were acquired; for each patient a rest and a stress study was executed. Manual segmentations of the left ventricle myocardium and the right-left ventricle insertion point are provided for all images in order to make a unified validation of the motion compensation algorithms and the perfusion analysis possible. In addition, all the scripts and the software required to run the experiments are provided alongside the data, and to enable interested parties to directly run the experiments themselves, the test bed is also provided as a virtual hard disk. Findings: To illustrate the utility of the data set two motion compensation algorithms with publicly available implementations were applied to the data and earlier reported results about the performance of these algorithms could be confirmed.Conclusion: The data repository alongside the evaluation test bed provides the option to reliably compare motion compensation algorithms for myocardial perfusion MRI. In addition, we encourage that researchers add their own annotations to the data set, either to provide inter-observer comparisons of segmentations, or to make other applications possible, for example, the validation of segmentation algorithms.