PET Image Reconstruction Using Deep Image Prior

Recently, deep neural networks have been widely and successfully applied in computer vision tasks and have attracted growing interest in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need for large amounts of prior training pairs, which is not always feasible in clinical practice. This is especially true for medical image reconstruction problems, where raw data are needed. Inspired by the deep image prior framework, in this paper, we proposed a personalized network training method where no prior training pairs are needed, but only the patient’s own prior information. The network is updated during the iterative reconstruction process using the patient-specific prior information and measured data. We formulated the maximum-likelihood estimation as a constrained optimization problem and solved it using the alternating direction method of multipliers algorithm. Magnetic resonance imaging guided positron emission tomography reconstruction was employed as an example to demonstrate the effectiveness of the proposed framework. Quantification results based on simulation and real data show that the proposed reconstruction framework can outperform Gaussian post-smoothing and anatomically guided reconstructions using the kernel method or the neural-network penalty.

I. Introduction

Over the past several years, deep neural networks have been widely and successfully applied to various imaging tasks such as segmentation [1], object detection [2] and image synethesis [3], by demonstrating better performance than state-of-the-art methods when large amounts of data sets are available. For medical imaging tasks such as lesion detection and region-of-interest (ROI) quantification, obtaining high quality diagnostic images is essential. Recently the neural network method has been applied to transform low-quality images into the images with improved signal-to-noise ratio (SNR).