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Abstract #3205

Enhanced Attenuation Correction in Hybrid PET/MR Imaging Using a Deep-Learning-Based Continuous μ-Map Generation Framework

Hanzhong Wang1,2, Yue Wang2, Zengping Lin3, Zheng Zhang3, Yang Yang 4, Qiu Huang1, and Biao Li2
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China, 3Central Research Institute, United Imaging, Shanghai, China, 4Beijing United Imaging Research Institute of Intelligent Imaging, Beijing, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction

Motivation: Accurate attenuation correction (AC) in PET/MR imaging faces challenges due to MR-based AC limitations, especially in segmenting bones and air-filled cavities, impacting PET quantification.

Goal(s): Develop a deep-learning-based MRAC framework with a pseudo-CT model and continuous μ-maps to improve PET quantification without specialized MR sequences.

Approach: The framework includes MR-to-CT generation, registration, and segmentation modules. Trained on 300 subjects, it was evaluated on 17 clinical cases, comparing four-tissue and five-tissue MRAC models to CTAC.

Results: The five-tissue continuous μ-map (MRAC4) showed high PET quantification accuracy, closely aligning with CTAC, and reduced dependency on specialized MR sequences.

Impact: The proposed framework enhances PET/MR accuracy, benefiting oncology and neurology diagnostics.

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