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) imag es. The technique allows the classification of pre- or post-injection TR im ages into main tissue components in terms of attenuation coefficients. The classified TR images are then forward projected to generate new TR sinogram s 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 brai n, heart and whole-body PET studies. The method allows: (a) reduction of no ise propagation from TR into EM images, (b) reduction of TR scanning to a f ew minutes (3 min) with maintenance of the quantitative accuracy (within 6% ) of longer acquisition scans (15-20 min), (c) reduction of the radiation d ose to the patient, (d) performance of quantitative whole-body studies.