A practical method for randoms subtraction in volume imaging PET from detector singles countrate measurements.
A practical method for randoms subtraction in volume imaging PET from detector singles countrate measurements.
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DOI:
10.1109/23.507257
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发表时间:
1996-06-01
影响因子:
1.8
通讯作者:
Karp, JS
中科院分区:
文献类型:
--
作者:
Smith, RJ;Karp, JS
Randoms subtraction in a volume imaging PET scanner is a significant problem due to the high singles countrates experienced. The delayed coincidence method requires double counting of randoms events and results in a lowered countrate capability. Calculations based on detector singles countrates require complex corrections for countrate dependent livetime and event acceptance due to the camera coincidence processing between the detector and rebinned randoms countrates. The profile distribution method has been used to estimate and subtract both scatter and randoms background but this method is a compromise and couples these 2 sources of background together. In order to avoid these problems and provide accurate subtraction of both the distribution and magnitude of randoms contamination in the scan data we have developed an alternative singles based method. The singles distributions are measured across the detectors and are used to construct a randoms distribution sinogram. This distribution is scaled to the appropriate rebinned randoms countrate by means of a lookup table of randoms countrate vs detector singles countrate, generated from phantom calibrations. The advantages of performing randoms subtraction by this method are: (1) there is no increase in camera deadtime, (2) the method compensates for non-uniformities in randoms distributions due to both the activity distribution and non-uniform geometric response of the camera for on and off bankpairs, and (3) it deals with randoms subtraction independently of scatter so that different scatter correction routines may then be applied to the data. Phantom tests at high countrates with F-18, and also with a single-photon emitter, Cs-137, where all measured events are randoms, have shown the method to reliably represent the randoms distribution. Comparisons have been made with the profile distribution and delayed coincidence methods and the new method has been successfully applied to body PET studies.