Enhanced Radiotracer Quantification via Multi-Modal Deep Learning Fusion for PET/CT Imaging

**Abstract:** Accurate quantification of radiotracer uptake is critical for diagnosis and monitoring in Positron Emission Tomography (PET) imaging. Conventional PET/CT image reconstruction and analysis methodologies are inherently limited by noise, partial volume effects, and anatomical ambiguity. This paper proposes a novel framework, HyperScore PET Quantification (HSPQ), which integrates PET, CT, and dynamic contrast-enhanced MRI (DCE-MRI) data using a multi-modal deep learning fusion network. The network incorporates a rigorous logical consistency engine, novelty assessment, and impact forecasting to provide significantly improved radiotracer quantification accuracy and clinical utility compared to standard reconstruction techniques. HSPQ is designed for near-term commercialization and addresses the urgent need for more precise and reliable PET diagnostics.