Synergistic integration of deep learning and regularized image reconstruction for positron emission tomography

University Of California At Davis, Davis CA

Investigators

Linked publications, trials & patents

Abstract

Project Summary/Abstract Positron emission tomography (PET) is a high-sensitivity molecular imaging modality widely used in oncology, neurology, and cardiology, with the ability to observe molecular-level activities inside a living body through the injection of specific radioactive tracers. In addition to the commonly used F-18-FDG, new tracers are being constantly developed and investigated to pinpoint specific pathways in various diseases. New PET scanners are also being proposed by exploiting time of flight (TOF) information, enabling depth of interaction capability, and extending the solid angle coverage. To realize the full potential of the new PET tracers and scanners, there is an increasing need for the development of advanced image reconstruction methods. This grant application proposes a new framework for regularized image reconstruction that synergistically integrates deep learning and regularized image reconstruction. The new framework is enabled by the recent advances in machine learning, which provide a tool to digest vast amount information embedded in existing medical images. The proposed method embeds a pre-trained deep neural network in an iterative image reconstruction framework and uses the deep neural network to regularize PET image directly. By training the deep neural network with a large amount of high-quality low-noise PET images, the proposed method can capture complex prior information from existing inter-subject and intra-subject data and thus is expected to substantially outperform the current state-of-the-art regularized image reconstruction method. The two specific aims of this exploratory proposal are (1) to develop the theoretical framework to synergistically integrate deep learning in regularized image reconstruction for PET and (2) to implement the proposed method and validate its effectiveness using existing animal data. Once the proposed method is validated using existing animal data, we will seek funding to acquire necessary human data for the implementation of the proposed method on clinical PET scanners.