Spatio-Temporal Positron Emission Tomography Reconstruction with Attenuation and Motion Correction

Abstract

The detection of cancer lesions of a comparable size to that of the typical system resolution of modern scanners is a long-standing problem in Positron Emission Tomography. In this paper, the effect of composing an image-registering convolutional neural network with the modeling of the static data acquisition (i.e., the forward model) is investigated. Two algorithms for Positron Emission Tomography reconstruction with motion and attenuation correction are proposed and their performance is evaluated in the detectability of small pulmonary lesions. The evaluation is performed on synthetic data with respect to chosen figures of merit, visual inspection, and an ideal observer. The commonly used figures of merit—Peak Signal-to-Noise Ratio, Recovery Coefficient, and Signal Difference-to-Noise Ration—give inconclusive responses, whereas visual inspection and the Channelised Hotelling Observer suggest that the proposed algorithms outperform current clinical practice.

Keywords: PET; tomographic reconstruction; motion correction; attenuation correction; deep learning; MLAA