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Respiratory motion in emission tomography leads to reduced image quality. Proposed correction methodology has been concentrating on the use of respiratory synchronised acquisitions leading to gated frames. Such frames however are of low signal to noise ratio as a result of containing reduced statistics. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. In this work we describe the implementation of an elastic transformation within a list-mode based reconstruction for the correction of respiratory motion over the thorax. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List mode data based PET simulated frames were subsequently produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an elastic registration of the NCAT dynamic CT images and were subsequently applied during the image reconstruction of the original emission list mode data. The One-pass list mode EM (OPL-EM) algorithm was modified to integrate the elastic transformation. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lungs and heart, the elastic transformation based correction leads to a more uniform improvement across the lung field for different lesion sizes.