Device-less gating for PET/CT using PCA

Movement degrades image quality in PET/CT. The first step in correcting for movement is to gate the data into different motion states. The gating is usually based on information from external devices, such as the chest position for respiratory movement, or an ECG signal for cardiac gating. Various groups have proposed methods to extract a gating signal out of the PET and/or CT data. Most of these methods are slow or require prior information (and associated tuning of parameters). Here we propose and evaluate a method that uses a well-known technique for data analysis called Principal Component Analysis (PCA). We test the method on clinical PET list mode data and CINE CT images to extract a gating signal. We show good correlation with the chest position as measured by the Varian RPM system. Total processing time for PET data is less than half a minute of which most is IO related.