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With the continual improvement in spatial resolution of Nuclear Medicine (NM) scanners, it has become increasingly important to accurately compensate for patient motion during image acquisition. Respiratory motion produced by normal lung ventilation is a major source of artefacts in NM emission imaging that can affect large parts of the abdominal thoracic cavity. As such, a particle filter (PF) is proposed as a powerful method for motion correction in emission imaging which can successfully account for previously unseen motion. This paper explores a basic PF approach and demonstrates that it is possible to estimate temporally non-stationary motion using training data consisting of only a single respiratory cycle. Evaluation using the XCAT phantom suggests that the PF is a highly promising approach, and can appropriately handle the complex data that arises in clinical situations.