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Model-based randoms correction for 3D positron emission tomography Pointoin, Barry William

Abstract

Random coincidences (randoms) are frequently a major source of image degradation and quantitative inaccuracies in positron emission tomography. Randoms occurring in the true coincidence window are commonly corrected for by subtracting the randoms measured in a delayed coincidence window. This requires additional processing demands on the tomograph and increases the noise in the corrected data. A less noisy randoms estimate may be obtained by measuring individual detector singles rates, but few tomographs have this capability. This work describes a new randoms correction method that uses the singles rates from an analytic calculation, rather than measurements. This is a logical and novel extension of the model-based methods presently used for scatter correction. The singles calculation uses a set of sample points randomly generated within the preliminary reconstructed radioactivity image. The contribution of the activity at each point to the singles rate in every detector is calculated using a single photon detection model, producing an estimate o f the singles distribution. This is scaled to the measured global singles rate and used to calculate the randoms distribution which is subtracted from the measured image data. This method was tested for a MicroPET R4 tomograph. Measured and calculated randoms distributions were compared using count profiles and quantitative figures of merit for a set of phantom and animal studies. Reconstructed images, corrected with measured and calculated randoms, were also analysed. The calculation reproduced the measured randoms rates to within ≤ 1.4% for all realistic studies. The calculated randoms distributions showed excellent agreement with the measured, except that the calculated sinograms were smooth. Images corrected with both methods showed no significant differences due to biases. However, in the situations tested, no significant difference in the noise level o f the reconstructed images was detected due to the low randoms fractions of the acquired data. The model-based method of randoms correction uses only the measured image data and the global singles rate to produce smooth and accurate random distributions and therefore has much lower demands on the tomograph than other techniques. It is also expected to contribute to noise reduction in situations involving high randoms fraction.

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