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

Dac Thai Nguyen, Trung Thanh Nguyen, Huu Tien Nguyen, Thanh Trung Nguyen, Huy Hieu Pham, Thanh Hung Nguyen, Thao Nguyen Truong, Phi Le Nguyen; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1498-1507

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 2028628 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.