Positron Emission Tomography (PET) imaging plays a critical role in functional assessment and metabolic characterization of diseases, particularly in oncology, neurology, and cardiology. However, PET image quality is often compromised by noise, low spatial resolution, and limited photon counts, necessitating advanced reconstruction techniques. This study proposes an innovative hybrid reconstruction framework that leverages structural guidance from Magnetic Resonance Imaging (MRI) to enhance PET image restoration. The approach integrates anatomical priors, nonlocal regularization, and iterative optimization strategies to improve image fidelity while preserving clinically relevant features.
The proposed method combines Bayesian reconstruction principles with MR-informed constraints to guide the PET reconstruction process. A multi-parametric framework is introduced to incorporate both structural and functional information, enabling improved delineation of anatomical boundaries. Additionally, advanced regularization techniques, including edge-preserving priors and level-set-based constraints, are utilized to mitigate noise amplification while maintaining spatial accuracy. The model further integrates adaptive weighting mechanisms to balance MR influence and PET signal integrity.
Experimental validation is conducted using both simulated and clinical datasets, including brain and whole-body PET-MR scans. The results demonstrate significant improvements in spatial resolution, contrast recovery, and quantitative accuracy compared to conventional reconstruction techniques such as OSEM and penalized likelihood methods. The framework also exhibits robustness to motion artifacts and reconstruction inconsistencies.
This research contributes a novel methodological advancement in PET image reconstruction by effectively integrating structural MRI information. The findings highlight the potential of hybrid imaging approaches to enhance diagnostic reliability and quantitative precision. Limitations related to computational complexity and dependency on MR quality are discussed, along with future directions for real-time implementation and deep learning integration.

PET Reconstruction, MRI Guidance, Anatomical Priors

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