Iterative projected gradient descent for dynamic PET kernel reconstruction

TL;DR

This paper introduces an efficient iterative projected gradient descent method for dynamic PET kernel reconstruction, improving noise reduction and image quality without requiring high-quality priors or composite frames.

Contribution

The paper develops a novel iterative method (itePGDK) that calculates kernel matrices directly from noisy images, outperforming existing deep learning and gradient descent approaches in speed and quality.

Findings

itePGDK outperforms DeepKernel and PGDK in bias-variance tradeoff.

itePGDK produces images with less artifacts in fast kinetics organs.

Method improves image quality without needing composite frames.

Abstract

Dynamic positron emission tomography (PET) reconstruction often presents high noise due to the use of short duration frames to describe the kinetics of the radiotracer. Here we introduce a new method to calculate a kernel matrix to be used in the kernel reconstruction for noise reduction in dynamic PET. We first show that the kernel matrix originally calculated using a U-net neural network (DeepKernel) can be calculated more efficiently using projected gradient descent (PGDK), with several orders of magnitude faster calculation time for 3D images. Then, using the PGDK formulation, we developed an iterative method (itePGDK) to calculate the kernel matrix without the need of high quality composite priors, instead using the noisy dynamic PET image for calculation of the kernel matrix. In itePGDK, both the kernel matrix and the high quality reference image are iteratively calculated using PGDK. We performed 2D simulations and real 3D mouse whole body scans to compare itePGDK with DeepKernel and PGDK. Brain parametric maps of cerebral blood flow and non-displaceable binding potential were also calculated in 3D images. Performance in terms of bias-variance tradeoff, mean squared error, and parametric maps standard error, was similar between PGDK and DeepKernel, while itePGDK outperformed these methods in these metrics. Particularly in short duration frames, itePGDK presents less bias and less artifacts in fast kinetics organs uptake compared with DeepKernel. itePGDK eliminates the need to define composite frames in the kernel method, producing images and parametric maps with improved quality compared with deep learning methods.