Fourier classification images in cardiac nuclear medicine

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Abstract
At present, the diagnosis of cardiac left ventricular regional wall motion abnormalities (RWMA) in nuclear medicine is aided mainly by phase images and amplitude images, which picture the spatial distribution of the phase and of the amplitude of the first harmonics of pixel time activity curves, respectively. However, they do not utilize other information contained in the original radionuclide images, and they do not offer a direct diagnostic interpretation of the data. The proposed Fourier classification images (FCI) overcome these deficiencies. Their pixel intensities express directly the diagnostic class of RWMA. The FCI pixel intensities are functions of pixel coordinates, Fourier features of pixel time activity curves, and their distribution parameters, and they are not limited by the first harmonics model. The derivation of the pixel classifier includes normalization transformation of coordinates and activities. Fourier analysis of raw image data, and teaching the computer by examples of already diagnosed cases with the help of discriminant analysis. FCI offer direct and robust diagnosis of RWMA, superior to that derived from phase and amplitude images, especially in the detection of mild RWMA.