First-pass myocardial perfusion image registration by maximization of normalized mutual information

Purpose: To evaluate a left ventricular image registration algorithm for first-pass MR myocardial perfusion. Materials and Methods: A normalized mutual information based motion correction algorithm was proposed and tested on 27 adenosine stressed myocardial perfusion cases consisting of pretreatment and posttreatment of 15 patients undergone autologous bone marrow mononuclear cell transplant therapy. An image mask approximately covering the left and right ventricles was manually defined to include a region of interest for registration. A two-dimensional multiresolution registration approach was used to register consecutively acquired multislice images with in-plane translations. The method was validated by manual registration and singular value deconvolution based perfusion analysis. Results: The proposed image registration algorithm was found to be robust in minimizing the in-plane motion of the left ventricle in first-pass myocardial perfusion. The image mask including the left and right ventricle was found to be more robust than including the left ventricle alone. A smooth estimate of normalized mutual Information coefficients were achieved for images with large contrast changes. Conclusion: The proposed semiautomatic multlresolution registration algorithm was able to register first-pass MR myocardial perfusion images and may be useful in quantitative perfusion analysis.