Fast Registration of Cardiac Perfusion MRI

This abstract presents a novel method for registration of cardiac perfusion MRI sequences. By performing complex analyses of variance and clustering in an annotated training set off-line, our method provides real-time segmentation in an on-line setting. This renders the method feasible for live motion-compensation in MR scanners. Changes in image intensity during the bolus passage are modelled by an Active Appearance Model augmented with a cluster analysis of the training set. Preliminary validation carried out using five subjects showed acceptable segmentation accuracy produced very rapidly (below 40 ms per image)

Abstract On Properties of Active Shape Models

Contrary to many other deformable models Active Shape Models (ASM) represents a generalwayofperforming non-rigid object segmentation. Shape variation is extracted from a training set by applying principal component analysis to point distribution models, rather than hand crafting a priori knowledge into the model. In this paper we investigate di erent properties of ASM. Topics treated are the generation of plausible shapes, tangent space transformation and model to image tting assisted by statistical models of gray level variation in the training set. Finally a method forautomatic initialization and a comparison of four model to image tting methods are presented. The initialization part indicates that completely automatic segmentation could be done by ASMs. The comparison part shows an improved t for model to image t methods based ongray level variation in the training set

Motion-compensation of cardiac perfusion MRI using a statistical texture ensemble

Abstract. This paper presents a novel method for segmentation of cardiac perfusion MRI. By performing complex analyses of variance and clustering in an annotated training set off-line, the presented method provides real-time segmentation in an on-line setting. This renders the method feasible for e.g. analysis of large image databases or for live nonrigid motion-compensation in modern MR scanners. Changes in image intensity during the bolus passage is modelled by an Active Appearance Model augmented with a cluster analysis of the training set and priors on pose and shape. Preliminary validation of the method is carried out using 250 MR perfusion images, acquired without breath-hold from five subjects. Quantitative and qualitative results show high accuracy, given the limited number of subjects