Abstract: PET-CT is a multimodal imaging technology that organically combines positron emission tomography (PET) and computed tomography (CT), providing accurate and comprehensive functional imaging and structural information imaging simultaneously. During the imaging of the thorax and heart in vivo, motion blur caused by unavoidable respiratory and cardiac movements severely interferes with quantitative analysis, thus affecting the accuracy of diagnostic results. At the same time, in other applications, the different motion states are of interest, for instance in cardiac studies to find the ejection fraction and wall motion, or in radio-therapy. In response to the issues and needs above, gated imaging methods have been proposed. Gated imaging involves several steps. First, gated signal acquisition is performed. Next, the PET and CT data are grouped based on predefined gated groups. Finally, attenuation correction and reconstruction of PET data are carried out using the grouped CT data. Each step in these first three processes can significantly impact the final imaging results. Early gated signal acquisition relied on external devices. For example, airbags used for collecting respiratory signals and ECG patches used for collecting cardiac signals, which introduced problems such as operational complexity, high costs, reduced patient comfort, and difficulties in accurately correlating the acquired signals with PET and CT data. Currently, commonly used gated imaging techniques address the artifacts caused by respiratory and cardiac motion primarily through external gating devices and data-driven methods. However, existing data-driven gating methods have trouble balancing stable multimodal data processing, accurate cardiac signal extraction, and real-time operation with feedback capabilities. An improved data-driven gating method, partial data principal component analysis (PD-PCA), which optimizes the gating signal extraction approach and preprocesses the raw data was proposed. The combination of these two aspects further enhances the accuracy of gating signal extraction. In the PD-PCA method, the window width is determined based on the biological respiratory cycle, and PCA processing of the local temporal information is performed based on this window width. The PD-PCA method can process the acquired partial data during the data acquisition process, resulting in more accurate heartbeat signals, improves the accuracy of cardiac gating grouping. Additionally, the grouping method during the gated grouping phase for respiratory gating was also improved, using a histogram statistical method to accurately locate stable respiratory periods, and accounting for the biological respiratory characteristics. The methods can improve the accuracy of the classification of stable respiratory periods. The experimental results indicate that the improved gating imaging method ensures higher accuracy in cardiac signal extraction, enabling real-time gated dynamic high-precision imaging.