Complementary frame reconstruction: a low-biased dynamic PET technique for low count density data in projection space

Abstract

A new data handling method is presented for improving the image noise distribution and reducing bias when reconstructing very short frames from low count dynamic PET acquisition. The new method termed ‘Complementary Frame Reconstruction’ (CFR) involves the indirect formation of a count-limited emission image in a short frame through subtraction of two frames with longer acquisition time, where the short time frame data is excluded from the second long frame data before the reconstruction. This approach can be regarded as an alternative to the AML algorithm recently proposed by Nuyts et al, as a method to reduce the bias for the maximum likelihood expectation maximization (MLEM) reconstruction of count limited data. CFR uses long scan emission data to stabilize the reconstruction and avoids modification of algorithms such as MLEM. The subtraction between two long frame images, naturally allows negative voxel values and significantly reduces bias introduced in the final image. Simulations based on phantom and clinical data were used to evaluate the accuracy of the reconstructed images to represent the true activity distribution. Applicability to determine the arterial input function in human and small animal studies is also explored. In situations with limited count rate, e.g. pediatric applications, gated abdominal, cardiac studies, etc., or when using limited doses of short-lived isotopes such as 15O-water, the proposed method will likely be preferred over independent frame reconstruction to address bias and noise issues.