An automatic classification technique for attenuation correction in positron emission tomography

In this paper a clustering technique is proposed for attenuation correction (AC) in positron emission tomography (PET). The method is unsupervised and adaptive with respect to counting statistics in the transmission (TR) images. The technique allows the classification of pre- or post-injection TR images into main tissue components in terms of attenuation coefficients. The classified TR images are then forward projected to generate new TR sinograms to be used for AC in the reconstruction of the corresponding emission (EM) data. The technique has been tested on phantoms and clinical data of brain, heart and whole-body PET studies. The method allows: (a) reduction of noise propagation from TR into EM images, (b) reduction of TR scanning to a few minutes (3 min) with maintenance of the quantitative accuracy (within 6%) of longer acquisition scans (15-20 min), (c) reduction of the radiation dose to the patient, (d) performance of quantitative whole-body studies.