CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach

TL;DR

This paper introduces a large-scale CT-PET dataset and a novel domain-knowledge-guided diffusion model called CPDM for translating CT images into PET images, aiming to reduce costs and health risks associated with PET scans.

Contribution

The study presents the first diffusion-based model for CT to PET translation and provides the largest paired dataset to date for this task.

Findings

CPDM outperforms existing methods in image quality metrics.

Incorporation of domain knowledge improves translation accuracy.

The dataset enables robust training and evaluation of CT-PET models.

Abstract

Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In contrast, CT scanners are more widely available and significantly less expensive. In response to these challenges, our study addresses the issue of generating PET images from CT images, aiming to reduce both the medical examination cost and the associated health risks for patients. Our contributions are twofold: First, we introduce a conditional diffusion model named CPDM, which, to our knowledge, is one of the initial attempts to employ a diffusion model for translating from CT to PET images. Second, we provide the largest CT-PET dataset to date, comprising 2,028,628 paired CT-PET images, which facilitates the training and evaluation of CT-to-PET translation models. For the CPDM model, we incorporate domain knowledge to develop two conditional maps: the Attention map and the Attenuation map. The former helps the diffusion process focus on areas of interest, while the latter improves PET data correction and ensures accurate diagnostic information. Experimental evaluations across various benchmarks demonstrate that CPDM surpasses existing methods in generating high-quality PET images in terms of multiple metrics. The source code and data samples are available at https://github.com/thanhhff/CPDM.