Performances of Principal Component Analysis for the extraction of respiratory signal from Time-of-Flight PET coincidences stream

Recently Principal Component Analysis (PCA) was suggested as a potential way to extract motion signals (e.g: cardiac beat and respiratory signals) from the coincidences stream of the PET scan. Proofs of principle ensued. Aim: To assess minimum requirements of the signal for PCA to successfully recover it, in terms of temporal resolution, of total counts needed and of strength of the motion signal in the data. The use of Time-Of-Flight technology, to increase signal-to-background ratio was investigated to see whether it would give improved results. Materials and methods: A General Electrics Medical Systems Discovery-690 PET/CT was used. A phantom with an uniform background and hot spherical sources placed on a moving platform was used. Motion period was set to about 4 seconds and motion range at 10 mm and 20 mm. The motion was recorded with an RPM device. PCA was applied to obtain motion signals with 80, 160 and 320 ms sampling. It was applied to data relative to 327, 163, 81 and 41 seconds of total acquisition time. The correlation to the RPM signal, which for this phantom is virtually noiseless, was used to measure the power of PCA signal to be an effective indicator of the motion signal. PCA was applied to TOF, traditional non-TOF data and non-TOF data with a rejection for all events with a time signature indicating events outside a 40 cm diameter. Data were also analyzed for a cardiac scan of a patient, with samplings of 20, 40 and 80 ms, to try to recover both cardiac and respiratory signal. Results: Correlation coefficients of 0.80 or greater were found in all cases for the phantom with 20 mm motion. For the 10 mm motion markedly lower correlations are found. At 80 ms temporal resolution the correlations are too low or absent to allow motion signal extraction. High enough values (r>0.7) are found only at 320 ms sampling for 81 s (or longer) acquisitions or at 160 ms sampling for 327 s acquisitions. The use of TOF data did not improve results for this, relatively small, phantom. Nonetheless exploiting TOF to improve rebinning resolution and to reject random coincidences improved a bit the results.In the example patient analyzed both the cardiac and the respiratory signal could be extracted at 20 ms sampling with 2 minutes of total duration Conclusion: Principal Component Analysis proved to be effective as a tool to extract motion signal from PET coincidences stream. Sampling durations as fast as 160 ms for respiratory signals with few moving objects in the Field of View or 40 ms sampling for dual motion extraction with large motion signals in the FOV are feasible.