Multi-Sequence MRI to Multi-Tracer Pet Generation via Diffusion Model

Wenjin Zhong, Cong Cong, Ghasem Azemi, Mehnaz Tabassum, Antonio Di Ieva, Sidong Liu

Abstract: Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are essential tools for diagnosing brain diseases. However, PET image acquisition is typically associated with high costs and significant radiation exposure to patients. Recent works have leveraged generative models to synthesise PET images from MRI scans. However, previous methods undergo training multiple times to accommodate the varying tracers of PET images, leading to limited flexibility. Additionally, they rely on simple concatenation when combining multiple MRI sequences, overlooking the correlations between these sequences. To address these limitations, this paper proposes a novel Diffusion Model that accepts multiple MRI sequences as inputs and generates PET images with various tracers. The model integrates an image encoder that processes multiple MRI sequences with cross attention and a categorical embedding that encodes the specific tracer information to guide the PET image generation. Experimental results on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our method offers higher flexibility and superior performance in PET image generation compared to the current state-of-the-art. The source code is available at: https://github.com/ZJohnWenjin/MTGD.git

External IDs:dblp:conf/isbi/ZhongCATIL25