Diversity processing of coincidence data
In a PET scanner, a method for processing coincidence data includes associating a selected portion of the coincidence data with one of a plurality of subspaces of a coincidence space; deriving, from the coincidence data associated with subspace, a contribution to be used in forming an image; and weighting the contribution by an amount that depends on a likelihood that the coincidence data associated with that subspace includes data indicative of trues.
1. In a PET scanner, a method for processing coincidence data, the method comprising:
associating a selected portion of the coincidence data with one of a plurality of subspaces of a coincidence space;
deriving, from the coincidence data associated with a subspace, a contribution to be used in forming an image, the contribution being a function of a temporal dimension, the function having a derivative that includes more than one impulse;
weighting the contribution by an amount that depends on a likelihood that the coincidence data associated with that subspace includes data indicative of and trues; and
forming an image at least in part on the basis of the weighted contribution.
2. The method of claim 1 , further comprising defining the coincidence space to include selected attributes of events that constitute coincidences.
3. The method of claim 2 , further comprising selecting the attributes to include spatial locations of the events.
4. The method of clainm 2 , further comprising selecting the attributes to include times of occurrence of the events.
5. The method of claim 2 , further comprising selecting the attributes to include energies associated with the events.
6. The method of claim 2 , further comprising selecting the attributes to include a temporal distribution of photons indicative of an event.
7. The method of claim 2 , further comprising selecting the attributes to include a spatial distribution of photons indicative of an event.
8. The method of claim 2 , further comprising selecting the attributes to include an energy distribution of photons indicative of an event.
9. The method of claim 1 , wherein weighting the contribution comprises determining, on the basis of the selected attributes, a probability that coincidences in a particular subspace are trues.
10. The method of claim 1 , wherein weighting the contribution comprises estimating a mean of a trues probability density.
11. The method of claim 10 , wherein estimating the mean of a trues probability comprises;
determining a spatially-dependent error function associated with spatial locations of events;
determining a spatially-independent error function; and
convolving the spatially-dependent error function and the spatially-independent error function.
12. A PET scanner having a processor configured to associate a selected portion of the coincidence data with one of a plurality of subspaces of a coincidence space;
derive, from the coincidence data associated with a subspace, a contribution to be used in forming an image, the contribution being a function of a temporal dimension, the function having a derivative that includes more than one impulse; and
weight the contribution by an amount that depends on a likelihood that the coincidence data associated with that subspace includes data indicative of trues.
13. A computer-readable medium having encoded thereof software for processing coincidence data, the software including instructions for associating a selected portion of the coincidence data with one of a plurality of subspaces of a coincidence space;
deriving, from the coincidence data associated with a subspace, a contribution to be used in forming an image, the contribution being a function of a temporal dimension, the function having a derivative that includes more than one impulse;
weighting the contribution by an amount that depends on a likelihood that the coincidence data associated with that subspace includes data indicative of trues.