Noise-robust real-time extraction of respiratory motion signal from PET list-data
A breathing motion and signal technology, applied in the field of medical imaging, can solve problems such as low signal-to-noise ratio, low emission signal strength, and data-driven methods that are not easy to emit imaging
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[0022] Methods of retrospective respiratory gating, such as Kesner's method discussed earlier in this article, have certain disadvantages. They need to perform image reconstruction for each time interval (e.g., 0.5 second time window in Kesner) in order to determine the activity of each voxel in each time interval. A trade-off is made between temporal resolution (improved by using shorter time windows) and noise (improved for each reconstructed image by using longer time windows). Image reconstruction is computationally expensive, does not facilitate real-time respiration signal extraction, and does not utilize time-of-flight localization in an efficient manner.
[0023] Noise can in principle be reduced by combining the activity versus time curves of all voxels in the imaging field of view (FOV) to generate a respiration signal. However, most voxels will not have a strong respiration signal component and thus may contribute more noise than the combined respiration signal sig...
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