A new axial smoothing method based on elastic mapping

Abstract

New PET scanners have higher axial and in-plane spatial resolutions but at the expense of reduced per plane sensitivity, which prevents the higher to be fully realized. Normally, Gaussian weighted interplane axial smoothing is used to reduce noise. In this study, the authors developed a new algorithm to first elastically map from adjacent planes to an expected plane location, then by simple averaging with the measured image at the corresponding axial plane location to reduce the image noise level. Compared with those images obtained by conventional axial-directional smoothing method, the images of the new method which are based on matching image features of adjacent planes have improved signal-to-noise ratio. To quantify the observation, both simulated cardiac images and real cardiac PET images were studied. The in-plane loss was measured by the effective global Gaussian resolution and the noise reduction was evaluated by the cross-correlation coefficient. Various Hanning reconstruction filters with cutoff frequency=0.5, 0.7, 1.0/spl times/Nyquist frequency and Ramp 1.0 filter were tested on simulated images. Results showed that the new method was robust to various noise levels and indicated larger noise reduction or better image feature preservation than by the conventional method.