Adaptive and self-evaluating registration method for myocardial perfusion assessment

Abstract

With the advent of ultra-fast MRI, it is now possible to assess non-invasively regional myocardial perfusion with multislice coverage and sub-second temporal resolution. First-pass contrast enhanced studies are acquired with ECG-triggering and breath holding. Nevertheless, some respiratory induced movements still remain. Myocardial perfusion can be assessed locally by parametric imaging methods such as Factor Analysis of Medical Image Sequences (FAMIS), provided that residual motion can be corrected. An a posteriori registration method implemented in the image domain is proposed. It is based on an adaptive registration model of the heart combining three elementary shapes (left ventricle, right ventricle and pericardium). The registration procedure is performed on a potential map derived from the distance map. To evaluate the quality of the registration procedure a superimposition score between the registration model and the contour automatically extracted in the sequence is proposed. Rigid transformation hypotheses and registration analysis provide an efficient and automatic method which allows the rejection of outlier images, such as: out of synchronisation images, out of plane acquisitions. When compared to a manual registration method, this approach reduces processing time and requires a minimal intervention from the operator. The proposed method performs registration with a subpixel accuracy. It has been successfully applied to simulated images and clinical data. It should facilitate the use of MR first-pass perfusion studies in clinical practice.