We propose a compressed sensing type of reconstruction method that can handle large amounts of inter-frame motion by including diffeomorphic model-based registration steps within the iterative reconstruction. The method is useful for speeding up imaging of moving structures. It is also suitable for application to a newly proposed type of myocardial perfusion imaging that does not use ECG gating. Such an acquisition is a simpler alternative to conventional ECG gated acquisitions but requires severe undersampling of k-space data and more sophisticated reconstruction methods. The new methods and preliminary results are presented.
Dynamic MRI perfusion studies have proven to be useful for detecting and characterizing myocardial ischemia. Accurate segmentation of the myocardium in the dynamic contrast-enhanced (DCE) MRI images is an important step for estimation of regional perfusion. Although a great deal of research has been done for segmenting MRI scans of heart wall motion, relatively little work has been done to segment DCE MRI studies. We propose a new semi-automatic robust level set based segmentation technique that uses both spatial and temporal information. The evolution of level sets is based on a spectral speed function which is a function of the Mahalanobis distance between each pixel's time curve and the time curves of user-determined seed points in the myocardium. A curvature penalty term is included in the evolution of the contours to ensure smoothness of the evolving level sets. We also make use of shape information to constrain the evolution of the level sets. Shape models were created by using signed distance maps from manually segmented images and performing principal component analysis. Thus the algorithm has the qualities of evolving an active contour both locally, based on image values and curvature, and globally to a maximum a posteriori estimate of the left ventricle shape in order to segment the left ventricle myocardium from DCE cardiac MRI images.
The algorithm was tested on 16 DCE MRI datasets and compared to manual segmentations. The results matched the manual segmentations.