Direct Reconstruction of Linear Parametric Images from Dynamic PET Using Nonlocal Deep Image Prior

TL;DR

This paper introduces an unsupervised deep learning framework for direct parametric image reconstruction from dynamic PET data, leveraging anatomical priors and non-local denoising to improve image quality without requiring high-quality training labels.

Contribution

The work presents a novel unsupervised deep learning approach that integrates kinetic models and anatomical priors for improved dynamic PET parametric imaging reconstruction.

Findings

Outperforms traditional direct reconstruction methods.

Effective denoising using non-local kernel layers.

Applicable to multiple tracer models like Patlak and Logan.

Abstract

Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework. Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited. Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available. For static PET imaging, high-quality training labels can be acquired by extending the scanning time. However, this is not feasible for dynamic PET imaging, where the scanning time is already long enough. In this work, we proposed an unsupervised deep learning framework for direct parametric reconstruction from dynamic PET, which was tested on the Patlak model and the relative equilibrium Logan model. The patient's anatomical prior image, which is readily available from PET/CT or PET/MR scans, was supplied as the network input to provide a manifold constraint, and also utilized to construct a kernel layer to perform non-local feature denoising. The linear kinetic model was embedded in the network structure as a 1x1 convolution layer. The training objective function was based on the PET statistical model. Evaluations based on dynamic datasets of 18F-FDG and 11C-PiB tracers show that the proposed framework can outperform the traditional and the kernel method-based direct reconstruction methods.