List-Mode PET Image Reconstruction Using Dykstra-Like Splitting
Kibo Ote Fumio Hashimoto Yuya Onishi Yasuomi Ouchi
To converge the block iterative method in image reconstruction for positron
emission tomography (PET), careful control of relaxation parameters is
required, which is a challenging task. The automatic determination of
relaxation parameters for list-mode reconstructions also remains challenging.
Therefore, a different approach than controlling relaxation parameters would be
desired by list-mode PET reconstruction. In this study, we propose a list-mode
maximum likelihood Dykstra-like splitting PET reconstruction (LM-MLDS). LM-MLDS
converges the list-mode block iterative method by adding the distance from an
initial image as a penalty term into an objective function. LM-MLDS takes a
two-step approach because its performance depends on the quality of the initial
image. The first step uses a uniform image as the initial image, and then the
second step uses a reconstructed image after one main iteration as the initial
image. We evaluated LM-MLDS using simulation and clinical data. LM-MLDS
provided a higher peak signal-to-noise ratio and suppressed an oscillation of
tradeoff curves between noise and contrast than the other block iterative
methods. In a clinical study, LM-MLDS removed the false hotspots at the edge of
the axial field of view and improved the image quality of slices covering the
top of the head to the cerebellum. LM-MLDS showed different noise properties
than the other methods due to Gaussian denoising induced by the proximity
operator. The list-mode proximal splitting PET reconstruction is useful not
only for optimizing nondifferentiable functions such as total variation but
also for converging block iterative methods without controlling relaxation
parameters.