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1 May 1994 Knowledge-based factor analysis of multidimensional nuclear medicine image sequences
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We have developed a knowledge-based approach to analyzing dynamic nuclear medicine data sets using factor analysis. Prior knowledge is used as constraints to produce factor images and their associated time functions which are physically and physiologically realistic. These methods have been applied to both planar and tomographic image sequences acquired using various single-photon emitting and positron emitting radiotracers. Computer-simulated data, non-human primate studies, and human clinical studies have been used to develop and evaluate the methodology. The organ systems studied include the kidneys, heart, brain, liver, and bone. The factors generated represent various isolated aspects of physiologic function, such as tissue perfusion and clearance. In some clinical studies, the factors have indicated the potential to isolate diseased tissue from normally functioning tissue. In addition, the factor analysis of data acquired using newly developed radioligands has shown the ability to differentiate the specific binding of the radioligand to the targeted receptors from the non-specific binding. This suggests the potential use of factor analysis in the development and evaluation of radiolabeled compounds as well as in the investigation of specific receptor systems and their role in diagnosing disease.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jeffrey T. Yap, Chin-Tu Chen, Malcolm Cooper, and Jon D. Treffert "Knowledge-based factor analysis of multidimensional nuclear medicine image sequences", Proc. SPIE 2168, Medical Imaging 1994: Physiology and Function from Multidimensional Images, (1 May 1994);

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