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3 July 1998 Tumor classification by means of eigenimage analysis of MRI perfusion scans
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In this paper we present a method that characterizes a certain tissue class by the shape of the MR signal intensity versus time course obtained from the dynamic series images following a Gadolinium-DTPA bolus injection. This characterization is based on a pharmacokinetic model of the perfusion and leakage of contrast agent in the tissue. An objective classification of malignancy using a priori information is based on matching the actual enhancement time course of each pixel to reference time courses. Eigenimage filtering using characteristic time courses as feature vectors is proposed as an approach to reduce the dynamic series of images to a single image in which pixels with a close match to a particular feature are enhanced. This single image can be used as a mask to obtain homogenous regions for parameterization using the pharmacokinetic model. A newly developed algorithm enables the creation of training sets of standard time courses from dynamic series images of lesions with known histology in an objective way. An automatic segmentation of a new patient scan without user interaction is obtained using the training sets as feature vectors in the eigenimage filter.
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Marco Winkelman, Cornelis H. Slump, Jouke Smink, and Jacques A. den Boer "Tumor classification by means of eigenimage analysis of MRI perfusion scans", Proc. SPIE 3337, Medical Imaging 1998: Physiology and Function from Multidimensional Images, (3 July 1998);

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