King's College London

PET image reconstruction is highly susceptible to the
impact of Poisson noise, and if shorter acquisition times or reduced
injected doses are used, the noisy PET data become even more
limiting. The recent development of kernel expectation
maximisation (KEM) is a simple way to reduce noise in PET
images, and we show in this work that impressive dose reduction
can be achieved when the kernel method is used with MR-derived
kernels. The kernel method is shown to surpass maximum
likelihood expectation maximisation (MLEM) for the
reconstruction of low-count datasets (corresponding to those
obtained at reduced injected doses) producing visibly clearer
reconstructions for unsmoothed and smoothed images, at all count
levels. The kernel EM reconstruction of 10% of the data had
comparable whole brain voxel-level error measures to the MLEM
reconstruction of 100% of the data (for simulated data, at 100
iterations). For regional metrics, the kernel method at reduced
dose levels attained a reduced coefficient of variation and more
accurate mean values compared to MLEM. However, the
advances provided by the kernel method are at the expense of
possible over-smoothing of features unique to the PET data.
Further assessment on clinical data is required to determine the
level of dose reduction that can be routinely achieved using the
kernel method, whilst maintaining the diagnostic utility of the
scan.