Background: In nuclear medicine data can be stored in histogram or listmode format. The most popular histogram format is the planar projection format. Due to the increase in detector blocks, the improved energy resolution and the trends towards time of flight, dynamic and gated imaging, it can be more appropriate to store the data in listmode format. The size of the storage in this format increases linearly with the number of properties (positions, energy, time info) while the histogram format increases exponentially. However, the datasize of listmode data also increases linearly with the number of coincidences. Due to the high number of counts in 3D PET this will lead to very large datasets. Therefore a good compression algorithm for listmode data is very important.
Methods: A sorting and compression method is proposed to reduce the amount of space needed to store the listmode dataset. One event is represented by one number without any information loss compared to the original listmode file. The next step is to sort all events into an array of increasing numbers. These data are compressed by the gzip routine. One of the advantages of 3D PET listmode reconstructions is that they result in a more uniform resolution across the field of view (FOV), which is not always true for other reconstruction algorithms. This improved resolution is shown for the listmode data of a gamma camera operating in PET mode.
Results: First the effect of positional accuracy in the listmode dataset is evaluated by comparing resolution in the reconstructions. It is shown that the highest accuracy is not necessary and a significant reduction in the size of the dataset can be obtained prior to lossless compression. A further reduction can be obtained by using the proposed sorting and compression techniques. It is shown that the storage space decreases linearly with the logarithm of the number of coincidences. The compression obtained by different acquisition matrices was compared. Finally it is shown that the 3D listmode reconstruction of sorted listmode data is faster because of improved cache behaviour. The method can be applied to any kind of listmode data. The compression factors will improve when the ratio of measured events to possible events increases.