Deep Learning for Suppression of Resolution-Recovery Artefacts in MLEM PET Image Reconstruction

Resolution modelling in maximum likelihood expectation maximisation (MLEM) image reconstruction recovers resolution but at the cost of introducing ringing artefacts. Under-modelling, post-smoothing (PS) and regularisation methods which aim to suppress these artefacts nearly all result in a loss of resolution. This work proposes the use of deep convolutional neural networks (DCNNs) as a post-reconstruction image processing step to reduce reconstruction artefacts without compromising the resolution recovery.The DCNN results successfully suppress ringing arte-facts and furthermore result in an 80% lower normalised root mean squared error (NRMSE) versus MLEM, compared to a best decrease of only 0.2% when an optimal level of PS of MLEM is performed. The resultant images from the DCNN have lower noise, reduced ringing and partial volume effects, as well as sharper edges and improved resolution.