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Abstract #3225

Dual-Branch Score-Based Diffusion Model Incorporating Radiomics Into Synthesizing PET From MR Images

Bin Zhang1, Taofeng Xie2, Zhuoxu Cui1, Xuhao Dong1, Jinxuan Lyu1, Shiyi Zhang1, Haonan Li3, Haifeng Wang3, Dong Liang1,3, and Yihang Zhou1
1Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Shenzhen, China, 2Department of Computer Information, Inner Mongolia Medical University, Inner Mongolia, China, 33. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Shenzhen, China

Synopsis

Keywords: AI Diffusion Models, PET/MR, Radiomics

Motivation: PET imaging offers valuable diagnostic insights but is limited by high costs and radiation exposure. Synthesizing PET-like images from MRI offers a viable alternative, though current methods largely overlook quantitative metrics such as radiomics, which could enhance diagnostic accuracy.

Goal(s): Our goal was to enhance the fidelity and diagnostic relevance of synthesized PET images by incorporating radiomics features into the MRI-to-PET synthesis process.

Approach: We introduced a dual-branch diffusion model for MRI-to-PET synthesis, capturing both structural and functional information, with radiomics features integrated.

Results: The model excelled in generating high-quality PET images, showing enhanced accuracy and preservation of diagnostic details over previous methods.

Impact: The proposed method integrates radiomics into the synthesis process, enhancing the clinical utility of MRI-based PET alternatives by producing reliable, radiation-free PET-like images with improved diagnostic fidelity and accessibility.

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Keywords