Copyright 2007 Society of Photo-Optical Instrumentation Engineers. This paper was (will be) published in SPIE Medical Image Computing and is made available as an electronic reprint (preprint) with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. Novel Methods for Parameter Based Analysis of Myocardial Tissue in MR-Images A. Hennemuth 1 , S. Behrens 1 , C. Kuehnel 1 , S. Oeltze 2 , O. Konrad 1 , H.-O. Peitgen 1 1 MeVis Research GmbH, Universitaetsallee 29, 28359 Bremen, Germany 2 Department of Simulation and Graphics, Faculty of Computer Science,University of Magdeburg, Universitaetsplatz 2, 39106 Magdeburg, Germany ABSTRACT The analysis of myocardial tissue with contrast-enhanced MR yields multiple parameters, which can be used to classify the examined tissue. Perfusion images are often distorted by motion, while late enhancement images are acquired with a different size and resolution. Therefore, it is common to reduce the analysis to a visual inspection, or to the examination of parameters related to the 17-segment-model proposed by the American Heart Association (AHA). As this simplification comes along with a considerable loss of information, our purpose is to provide methods for a more accurate analysis regarding topological and functional tissue features. In order to achieve this, we implemented registration methods for the motion correction of the perfusion sequence and the matching of the late enhancement information onto the perfusion image and vice versa. For the motion corrected perfusion sequence, vector images containing the voxel enhancement curves’ semi-quantitative parameters are derived. The resulting vector images are combined with the late enhancement information and form the basis for the tissue examination. For the exploration of data we propose different modes: the inspection of the enhancement curves and parameter distribution in areas automatically segmented using the late enhancement information, the inspection of regions segmented in parameter space by user defined threshold intervals and the topological comparison of regions segmented with different settings. Results showed a more accurate detection of distorted regions in comparison to the AHA-model-based evaluation. Keywords: myocardial perfusion, tissue classification, late enhancement INTRODUCTION Coronary heart disease causes an ischemia of the supplied myocardium or in worst case a myocardial infarction which is the cause of death for more than seven million people each year [1]. To allow for a preventive therapy, an accurate diagnosis is essential. Cardiac MR provides non-invasive methods for the inspection of myocardial perfusion as well as the detection of infarcted tissue. Thus, it allows for the distinction if tissue is healthy and has sufficient blood supply, if tissue is hypoperfused but could benefit from a therapy or if tissue is already scarred. Images are usually acquired in short axis slices after the bolus injection of a gadolinium-based contrast agent. The perfusion analysis sequence shows the first pass of the contrast agent and has a high temporal resolution of about 1 second depending on the patient’s ECG. Hence, even with parallel imaging technique, no high spatial resolution is possible with current scanner technology and normally only 3 to 6 image slices with a thickness of 6 to 10 mm and gap about 6 mm are acquired. Fig. 1 shows an example dataset with four perfusion slices (yellow border). The curves on the right image illustrate the intensity change over time due to the wash-in and wash-out of the contrast agent, which is related to the perfusion. The myocardial perfusion is measured via so called semi-quantitative parameters describing the behaviour of the intensity curves derived from the image sequence. As acute or chronically infarcted tissue shows an enhanced distribution volume for Gd-DTPA, it can be imaged 10 to 30 minutes after bolus injection as bright image regions. For this purpose, short axis slices with about 8 mm thickness with or without spacing are acquired (slices with orange border in Fig. 1). The analysis of the perfusion sequences requires a pre-processing step to compensate motion artefacts from breathing, myocardium contraction and patient motion. Due to the contrast changes between the images in the perfusion sequence this is a difficult problem and there exist various approaches using different image features [2-6] and model-based